VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning
Authors: Wenhao Li
Qiangchang Wang
Xianjing Meng
Zhibin Wu
Yilong Yin
Venue: NeurIPS 2025 poster ()
Keywords: Few-Shot Learning
Multimodal Fusion
LLM
Primary area: applications

Abstract: Few-shot learning (FSL) aims to recognize novel concepts from only a few labeled support samples. Recent studies enhance support features by incorporating additional semantic information  (e.g., class descriptions) or designing complex semantic fusion modules. However, these methods still suffer from hallucinating semantics that contradict the visual evidence due to the lack of grounding in actual instances, resulting in noisy guidance and costly corrections. To address these issues, we propose a novel framework, bridging Vision and Text with LLMs for Few-Shot Learning (VT-FSL), which constructs precise cross-modal prompts conditioned on Large Language Models (LLMs) and support images, seamlessly integrating them through a geometry-aware alignment mechanism. It mainly consists of Cross-modal Iterative Prompting (CIP) and Cross-modal Geometric Alignment (CGA). Specifically, the CIP conditions an LLM on both class names and support images to generate precise class descriptions iteratively in a single  structured reasoning pass. These descriptions not only enrich the semantic understanding of novel classes but also enable the zero-shot synthesis of semantically consistent images. The descriptions and synthetic images act respectively as complementary textual and visual prompts, providing high-level class semantics and low-level intra-class diversity to compensate for limited support data. Furthermore, the CGA jointly aligns the fused textual, support, and synthetic visual representations by minimizing the kernelized volume of the 3-dimensional parallelotope they span. It captures global and nonlinear relationships among all representations, enabling structured and consistent multimodal integration. The proposed VT-FSL method establishes new state-of-the-art performance across ten diverse benchmarks, including standard, cross-domain, and fine-grained few-shot learning scenarios. Code is available at https://github.com/peacelwh/VT-FSL.

VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning
Wenhao Likb2, Qiangehang Wang"*, Xianjing Meng', Zhibin Wu', Yilong Yin!*
School of Software, Shandong University 'Shenzhen Loop Area Institute
"Scooo of Computing and Artificial Intelligence. Shandong University of Finance and Economics {wenhao.. .ii, zhibinwu] Brabinwu]@mail.. email. edu. cn, rongmengyuanggmail. com, {qiangchang, wang, Elyin>&sdu eecttt.dddu...m
Abstract
Few-shot learning (FSL) aims to recognize novel concepts from only a few la- beled support samples. Recent studies enhance support features by incorporating additional semantic information (e.g., class descriptions) or designing complex semantic fusion modules. However, these methods still suffer from hallucinat- ing semantics that contradict the visual evidence due to the lack of grounding in actual instances, resulting in noisy guidance and costly corrections. To address these issues, we propose a novel framework, bridging Vision and Text with LLMs for Few-Shot Learning (VT-FSL), which constructs precise cross-modal prompts conditioned on Large Language Models (LLMs) and support images, seamlessly in- tegrating them through geometry-aware alignment mechanism. mainly consists of Cross-modal Iterative Prompting (CIP) and Cross-modal Geometric Alignment (CGA). Specifically, the CIP conditions an LLM both class names and support images to generate precise class descriptions iteratively in single structured rea- soning pass. These descriptions not only enrich the semantic understanding of novel classes but also enable the zero-shot synthesis of semantically consistent images. The descriptions and synthetic images act respectively as complementary textual and visual prompts, providing high-level class semantics and low-level intra-class diversity to compensate for limited support data. Furthermore, the CGA jointly aligns the fused textual, support, and synthetic visual representations by minimizing the kernelized volume the 3-dimensional parallelotope they span. It captures global and nonlinear relationships among all representations, enabling structured and consistent multimodal integration. The proposed VT-FSL method establishes new state-of- the-art performance across ten diverse benchmarks, includ- ing standard, cross domain, and fine- ane-grained few-shot learning scenarios. Code is available http:///hbbbboooo/occccc////////
Introduction
Deep learning has achieved remarkable success in computer vision [13 and natural language
processing [4] thanks the availability of large-scale annotated datasets. However, collecting
such data is often expensive or infeasible in many real-world scenarios [5+10]. Few-shot learning (FSL) [T]] aims to address this challenge by enabling models generalize from only a few labeled samples.
In FSL., the support set provides N novel classes, each with K labeled samples. The model learned from the support set is required to classify test samples from the query set. Among various approaches, reetrc-based methods 21 widely adopted due their superior scalability and performance.
"Conresoondng authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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The house finch is a small bird marked by its bright red or rosy head and chest, with streaked brown und white body. and short, conical beak suited for seed eating, It ofien seen perched on thin branches...
Syntiettc image
Text
image
Synthetic
Text
Support
Support
Red or rosy head Streaked brown
and white body
Original
Aligned
Support image
Short, conical beak
Synthetic image
Volume (4)
0.76
0.35
Figure I: Illustration of the VT-FSL intuition. Left: The generated text and synthetic images provide high-level class semantics and low-level sample diversity. Right: By minimizing the volume of the 3-dimensional parallelotope spanned by all embeddings, they lie closer, indicating better alignment.
These methods embed both support and query samples into a shared feature space and classify each query by finding the nearest support sample. A common strategy is to construct class prototypes by averaging support features within each class to represent its semantic center []] However, the limited number of labeled examples makes it challenging to learn discriminative prototype representations, resulting in semantic deviation from the true class center.
To address this issue, alternative is to integrate additional semantic information from textual modality. Following this perspective, several studies [33228] reveal that integrating semantics from class names can improve prototype representation. However, class names offer minimal contextual information. Several recent [19 works attempt to leverage Large Language Models (LLMs) [4] external knowledge bases 32 to generate richer descriptions or attribute-basse semanties to replace names. Despite their success, they rely solely on class names, neglecting valuable visual patterns of the support images. As result, the generated text may lead to semantic hallucinations, iee, misalignment with the actual corresponding object, which requires costly manual or algorithmic corrections. Moreover, naive input prompting hinders LLMs from fully utilizing the reasoning and generation capabilities, limiting the quality of semantics in few-shot scenarios.
In this paper, propose novel framework, bridging Vision and Text with LLMs for Few-Shot Learning (VT-FSL). First, a Cross-modal Iterative Prompting (CIP) module is introduced to condition both class names and support images, obtaining precise and visually grounded descriptions. This is accomplished by a single structured inference pass, comprising four distinct stages for iteratively optimizing text quality: strategy, perception, refinement, and conclusion. Next, to enrich intra-class sample diversity, text-to-image model 35 36 is utilized to generate synthetic images with semantic consistency based these descriptions in zero-shot manner. As shown in Fig. (left), the resulting text and images serve as complementary textual and visual prompts to compensate for limited support data. To fully utilize these cross-modal prompts, we incorporate textual embeddings from CLIP 37 into support features along spatial and channel dimensions via a lightweight two-layer perceptron. Furthermore, we propose novel Cross-modal Geometric Alignment (CGA) that leverages the volume of a 3-dimensional parallelotope spanned by the fused support, textual, and synthetic visual embeddings for consistent alignment, as shown Fig. (right). CGA establishes a contrastive learning objective to enhance global and nonlinear relationships among all features by minimizing their volume in a kernelized parallelotope embedding space. Finally, VT-FSL. achieves comprehensive cross-modal integration, enabling the extraction of generalized class prototypes enriched with discriminative information.
LLMs 33 34
Overall, our contributions are summarized as follows:
A VT-FSL framework is proposed construct complementary cross-modal prompts with large language models, seamlessly integrating them through a geometty-awand alignment.
A CIP module is proposed to generate precise descriptions conditioned on both class names and support images, driving zero-shot synthesis of semantically consistent examples.
A CGA module is proposed to achieve comprehensive alignment across all representations and capture nonlinear semantic relations by kemelized volume-based contrastive learning.
The proposed method achieves state-of-the-art performance on ten standard, cross-domain. and fine-grained FSL benchmarks, significantly improving accuracy by 4.2% average.

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Iterative Prompting (CIP)
Cross-modal
The Arctic fox mammal distinguished by thick, seasonully changing white in winter... a
Bluck
Zu
Geometric Alignment (CGA)
Cross-modal
Strategy
Perception
Refimement
Cone Conclusion
Z
000
Z
CLIP
compact body, short legs. bushy tail...
Zz
L.M
Block
100
MI.P
Concat
Block
Trainable
Generative model
Support
"Aretic
Frozen
Figure 2: Overview of the proposed VT-FSL framework. First, given both class names and support images, CIP guides an LLM to generate precise descriptions via four structured stages. Synthetic images with semantic consistency are then generated based on these descriptions to expand the limited data. They extracted to obtain features Ze by a feature extractor consisting of multiple Transformer blocks with shared weights. Next, the textual features Z, encoded by CLIP are injected into the support features 25 via two-layer MLP, enhancing the support embeddings Z,. Finally, Z,. 7z, and Zu are jointly aligned through CGA, enabling global and nonlinear cross-modal interactions.
2 Related Work
Existing few-shot learning (FSL) methods can be broadly categorized into two types. Optimization-bant methods [8 adapt models to novel tasks with few optimization steps, but may lead to meta-overfittnng 44 due to the scarcity of task-specific supervision. In contrast, metric-based methods [1-22] learn a generalized embedding space where inter-class distances are maximized and intra-class distances are minimized. Several works also incorporate self-supprvised
Few-shot Learning.
ma
learning to refine feature representations [20] 48) To compensate for rare representative features within limited support data, many methods 22 49 incorporate additional semantic information.
For example, AM3 [25] fuses semantic features from class names with visual prototypes through
an adaptive fusion mechanism. CaFo 22 generates synthetic images based on class names to
expand the few-shot data. SIFT [49] generates high-quality features via a semantic transformation
process. Several recent methods [1) explore the use of Large Language Models (LLMs) to replace class names with richer textual information. Conditioned on class names, ECER 33 extracts attribute-level textual information, and SemFew 31 constructs coherent descriptions to enhance prototype learning. In contrast, our approach jointly leverages both class names and support images to generate visually grounded textual descriptions via structured LLM reasoning and further produces semantically consistent synthetic images, forming complementary cross-modal prompts. Moreover, we fully integrate these prompts with support features in geometry-awar alignment manner, capturing global and nonlinear cross-modal relationships.
Contrastive Learning. Most existing methods 55 adopt CLiP-based pairwise contrastive learning (37). However, aligning each representation only to a single anchor neglects the interac- tions among the remaining points, making difficult capture global structural relationships and limiting the full integration of cross-modal semantics. The Gram matrix, which characterizes the mutual geometry among sets of vectors, has shown promise in theoretical analyses of deep learning networks [56] and various downstream tasks [57] 58). To the best of our knowledge, we are the first to consider volume-based contrastive learning for global consistent alignment and further capture nonlinear relationships within kemelized parallelotope embedding space in few-shot learning.
3 Methodology
We begin by introducing the preliminaries of FSL, and then present two components of our method: (1) explaining how textual and visual prompts are generated through CIP, and (2) how all embeddings are sinultaneously aligned via CGA. Fig shows the overview of the proposed VT-FSI..
3.1 Preliminaries
Few-Shot Learning (FSL.) focuses on generalizing the knowledge learned from training set Cirin to test set Crest where both sets are disjoint (Crain n Crst 0). FSL is typically formalized as an
3

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Strategy Stage:
semantic assumptions and appreach sammary
=STRATEEY> "malamute" likely refors to the Alaskan Malamutc, strong, wolf-like dog breed. Approach involves interpreting the class name, verify it with visual clements, and refine the delinition accordingly. </STRA RATEEYY>
Class Name: mal mmlaamtte
Perception Stage: Stanee Image perception and thared visual patterns extraction
double coals, eneet triangular curled backs, and simond-shaped eyes... consistemily maintaiing those (calures across various settings </PERCEPTON?>
-PERCEPTION- The images show dogs with
LLM Refinement Stage: Conceptual refinement via step-by-step reasoning
-REFINEMENT I First, the images confimm this by show ing hallourk truits such as thick fur, creet trisngular or curled hacks, and wolf-like faees with almond eyes, Second, the presence of collars indicates domestication Finilly, despite varied settings, the core visual fentures remain consistent. <RFPIN F EMENT>
Conclusion Stage: Unified definition output by formalized conceptual representation
=CONCLI USION- The Aloskan Malamute lange, powerful dog breed known for thick, dauble-coated fur, often white with black, or light markings. has robust build.
trinnuular curled back, and wolf-like fox- hoth
like face with working companion unimal, with an appearunce suited for cold climates heavy- tasks,
almond
Distinguished by strength. stamina, and friendly demeaner, -CNNCUUSONN-
Figure 3: Illustration of Cross-modal Iterative Prompting (CIP). Given the class name and 5-shot support samples, CIP exploits the LLM through four structured reasoning stages: Strategy, Perception, Refinement, and Conclusion, to generate class-specific, precise class descriptions.
N-way K-shot classification task using an episodic strategy [2]. Each task consists of a support set N and query set Q = {(x,, V/ / N for performance evaluation. S = [Z1, )
In the FSL approach, prototype-baser inference is commonly utilized, computing prototype embedding e for each class by averaging support features from the feature extractor f& as follows:
K Σ fo(Tx); G = K
Th E S.
For query sample x", the similarity score between x" and all support classes is determined by calculating the distance function between x" and N class prototypes as follows:
cxp(cos(fg(x")),c)/
ply" i x²)
(2)
===1 exp(cos (x)),c)/T))
N
where T is a temperature parameter. cos( .) denotes the cosine distance function. The support class with the maximum score is regarded as the classification result.
3.2 Cross-modal Iterative Prompting
To address the ambiguity in class names and capture the shared visual patterns from support samples, we propose Cross-modal Iterative Prompting (CIP), which jointly leverages the class label and K-shot support images to generate precise and visually grounded class descriptions. CIP follows a structured reasoning process inspired by Chain-of-Thouugh (CoT), which improves the semantic interpretability and generation accuracy of Large Language Models (LL.Ms).
CIP decomposes the generation process into four structured reasoning stages, including strategy, perception, refinement, and conclusion, as shown in Fig. 3 Every stage guided by the prompting design corresponds outlining the problem, interpreting relevant information from the image, pro- ceeding with a step-by-step reasoning process, and ultimately reaching a well-supported conclusion, respectively. Each stage is marked using structured tags (e.g., SSTRAATGYS.../STRATTEER which allows the LLM to maintain reasoning boundaries throughout a single inference pass. This design removes the need for multi-turn interactions external filtering, reducing manual effort and latency. The entire structural reasoning process and more cases are detailed in the Appendix.
As shown in Fig. 4. the generated description is then fed into a text-to- text-to-image generative model to produce synthetic images with semantic consistency in zero-shot manner. To ensure semantic fidelity, we incorporate an LLM-based pairwise comparison strategy, which selects the K images per class by ranking them against the textual de- scription. An enriched support set is obtained, containing both real and synthetic samples without compromising the low-data regime. Formally, N-way (K + K) -Sh- support set can be constructed from N textual descriptions T'v as:
Description: The Alaskan Malarmtt lerge, powerful breed known its thick. double conted für, white with black. or light markings. It has robust build, erect trinngalar...
Synthetic images
Support
Generative
model
Judge Prompt: Giiven two imugos and the deserittion which imagee best noflects typical visual features of this catlgory?
LLM
Figure 4: Illustration of visual synthetic images in the 1-shot task.
SNX(K+K) (Synthesize(Tww) SNNK].
(3)

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3.3 Cross-modal Geometric Alignment
Cross-modal Fusion. First, we leverage textual prompts adaptively adjust support features extrac- tion based on rich semantics. The textual features Ze encoded from the CLIP are linearly mapped to the same embedding space, followed by integrating with support features 23 by a lightweight and effective two-layer network, as follows:
B = o (W2o [ [Ze Avg(z+)l))
where W; and W2 are parameters of the network, a is sigmoid activation function. Avg( denotes the average of all patch tokens. The vector / is injected into each token of support features, modulating support features along the channel dimension.
Zz and 2, are then concatenated along the spatial dimension to capture the relevance between tokens based rich semantics via Multi-head Self belf-Attention (MSA) of the Transformer block:
QKT
A Softmax
(5)
vd
Z, (AV)W;
(6)
where Q, K, and V are linearly mapped by each token. is the dimension of each head. The attention matrix A is used to aggregate information from the value V. The outputs from all attention heads are concatenated and projected linearly by the weight matrix W, obtaining the enhanced Z..
Kernelized Volume-based Contrastive Learning. To achieve comprehensive consistency alignment, we propose kernelized volume-based contrastive objective to improve global and nonlinear interac- tions among all features. Unlike traditional pairwise contrastive objectives, we measure the alignment among multiple vectors via the volume of the k-dimensional parallelotope they span in a shared embedding space [59]. A smaller volume indicates closer alignment. Specifically, K normalized embeddings Vi,..., V& R" construct the matrix A [Vi,..., v& and their extremities lie on the surface of the unit hypersphere. The Gram matrix G (Vv1,.-.. v&. R is first defined, reflecting the square of the corresponding volume Vol as follows:
v1),
v2)
(Vi v&)
(v2, v1)
(V2. v2)
(V2, vk
= A
(7)
vi)
(Vx, V2)
(vx, va)
Vol(v1..... vn) /d)(GG), Giy (vi, vy).
(8)
To further model nonlinear interactions, this metric is extended into digh-ddmensional Reproducing Kernel Hilbert Space (RKHS) using a Radial Basis Function (RBF) kernel mapping k(.,)). The volume is computed from the kernel Gram matrix K:
Volæ(v1,..... v*) = v (e),,,, K;J n(V., Vz).
Next, Textual, synthetic, and enhanced support features are transformed into normalized triplets (Ze; Z.. Zz). Selecting textual modality as the anchor focuses the alignment around it. Formal derivations of relevant theory and ablations on anchor and kernel function choices are detailed in the Appendix. Minimizing the kernelized volume via the contrastive loss defined as:
B
exp
(- Volx (2 7g, (),
LUZa = B
log i=1
(10)
(-Vol 23, 2:)/T)
exp
B
exp (- (oVo
75, ())/7)
CADD
log
(11)
B
Vol (Z; ZZ, /
Dj=1 exp
Finally, the overall loss also includes the Cross- Cross-Entropy loss between the probability p; of the query sample q to the i-th class Eq. (2) and the corresponding ground-trutt label, as follows:
Listal CrossEntropyyp,, vi) + (Crza + LAND).
(12)
5

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Table 1: Results (%) on manelmaeeeet [22 and riered (60]
The average accuracy with
95% confidence interval reported. Bold and blue font indicates the best and suboptimal results.
miniImageNet
fieeedmageeeet
Model
Venue
Backbone
Params
1-shot
5-shot
1-shot
S-shot
MatchNet
12
NeurlPS 16
ResNet-12
12.4M
65.64-20 20
78.7220.15
68.50=0 92
20.6
ProtoNet
NeurIPS
ResNet-12
12.4M
62.39+0.21
80.53=00144
2.
84.03+0.01
AM3 225
NeurIPS
ResNet-12
12.4M
65.30
15.006
69.08.0.44
82. 02.
DeepEMD 18
CVPR'20
ResNet-12
12.4M
65.91+0.82
82.41 =0.00
71.16=0.87
86. a..333
PCPK 29
CVPR'21
ResNet-12
12,4M
73.13:0..3
82, 92.06.10.c4 5.
81 04.00..0.0
87 0.
SUN 17
ECCV*22
Visformer-S
12.3M
67.80 45
(5..25.00300
72.99 50
86.74420.38
FewTURE -7
22urIPS-22
VTT-S/16
22.0M
68.0240
84.51
72.96.0010
86.43310.0
SVAE 27
CVPR'22
ResNet-12
12.4M
74.84+ 23
83. 28.28...000
76.98=
85.77=0.50
Meta-AdaM
25
ResNet-12
12.4M
59.89+0.44
77.92=0.0o
05.31
00.24
PiotoDiff 62
NeurIPS
ResNet-12
12.4M
06.03
83.48=00115
72.95=0.24
85.15+0.3
CPEA 63
ICCV'23
VIT-S/16
22.0M
71.97
87. 07.06=10.3
76. 20.70
90.12:
SP 23
CVPR'23
Visformer-T
10.0M
T2.31 12 40
83.42:20.0
78.03 86
88 20.33
NTW-Meta 64
ICLR'24
WRN28-10
36,5M
08..4410..2 26
84.81
74.59 30
89.76.10.29
FeatWalk [6
AAA1'24
ResNet-12 12
12.4M
70.21
87.3820.22
75.25
89.92:0.39
BECLR 65
ICLR'24
ResNet-18
11.7M
75. 75.7420.62
84.93=00.5
76.44=0.66
84.86
SIFT 49
IICV'24
WRN-28-10
36,5M
T7.31#0.67
86, 86.95 53
77. 77.86=0.17
89 89.0.2
SemFew
C4PR'24
Swin-T
29.0M
78.91+0.68
86.49=00.0
=0.37
89. a9.80
UAP [66
N4
ResNet-12
12.4M
81. 1.63310222 22
79.05
79.68.1..2
76.7810.21
ECER [30]
AAAI225
Visformer-T
10.0M
81. 81.14:00.13
$1.81=0.0
VT-FSL
OurS
Visformer-T
10.0M
83.66:0.31
08.38
85. 35.0244..4
91.71=0027
We detail the algorithm of the entire training process in the Appendix. During testing, the prototype with textual prompts e is obtained by averaging the enhanced support features. The prototype with vi- sual prompts c, is computed by averaging the expanding support set Eq. (3). The final classification prototype C' is then obtained by integrating Co and e₂ in convex combination manner 25
C = uct + u)Cr,
(13)
where E [0, 1] is manually controlled fusion factor, determined in the validation set.
4
Experiments
4.1 Experimental Details
Datasets.
Extensive experiments are conducted in three distinct few-shot learning scenarios. (1) Four
datasets in standard FSL: minlImageNet [12], fieredTieredNet [60], CIFAR-FS (67] and FC100 [19].
(2)Three datasets in fine-grained FSL: CUB [68] Cars [69, and Dogs [70] (3)Three datasets in
cross-domain FSL.: CUB, Places (71. and Plantae 72 The detailed dataset statistics regarding the number of categories and images are introduced in the Appendix.
Implementation Details. Following recent few-shot studies 124
23
30
we adopt Visformer-
Tiny 3 as the feature extractor and apply the text encoder from V CLIP 37 where the output dimension is 512. DWL-2.5-VL-33BB [44] and Janus-Pro (36] models are utilized to generate textual
and visual prompts by default. More architecture comparisons are detailed in the Appendix. Our
training framework adopts two-stage framework [17]. consisting of pre-training and meta-tuning stages. Input images are resized into 224x 224 [II] The Adam W optimizer
[73] is used with a learning rate of 5c-4 and cosine scheduler. Pre-training runs for 300 epochs in tieredImageNet and 800 epochs in other datasets with batch size of 512, followed by meta-tuning for 100 epochs via an episodic training strategy. The hyperparameter 7 set as 0.2 according to validation accuracy. All experiments are performed with an NVIDIA RTX 6000 Ada.
Evaluation Protocol. For evaluation, adopt the widely used episodic protocol 23-28 in FSL. Specifically, 2000 classification tasks are uniformly sampled from the novel classes that do not overlap with training categories. Each task follows the standard N N-way K-shot setting, where 15 query samples per class are included for evaluation. The final performance is reported as the mean classification accuracy across all sampled tasks, along with the 95% confidence interval.
4.2 Main Results
Standard Few-Shot Classification. Table and Table 2 show the comparative results on four benchmarks. It is worth noting that (1) Semantic-based methods (AM3, PCPK, SVAE, SP, SIFT,

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SemFew, and ECER) generally outperform oth- ers, highlighting the effectiveness of semantic in- formation in learning more generalizable feature representations, (2) The proposed VT-FSI. uses lightweight Visformer-T T backbone yet outper- forms all methods with larger backbones (e.g.,
Table Results (%) with the average accuracy is reported on CIFAR-FS 67] and FC100 [9]]
Method
Venue
CIFAR-FS
FC100
1-shot
5-shot
I-shot
5-shot
ProtoNet
M
NewrPPS17
72.20
83.50
41.54
57.08
MABAS
74
ECCV'20
7:3.51
85.65
42.31
58.16
VIT-S/16, Swin-T, and WRN28-100 10), demonstrat- ing that VT-FSL fully utilizes the feature ex- traction potential of the backbone. (3) VT-FSL outperforms previous methods by a large margin. Compared with the competing methods benchmarks, VT-FSL surpasses SemFew [31 by 3.7%-5.7%% and 1.0%-2.7% in the 1-shot and 5-shot settings. Moreover, compared to complex fusion mechanisms (25/27), VT-FSI. achieves such improvements using a simple two-layer network with only 0.7M parameters, significantly fewer than the 4.3M parameters used in SemFew. These results highlight the effectiveness of constructing complementary multimodal prompts with global alignment across all features for full semantic integration.
FoWTURE
37
NeurIPS:22
77.76
88.90
17.68
63.81
MAdaM
61
NeurIPS223
41.12
56.14
SP
23
CVPR
82.18
89,24
48.53
61.55
ALFA
TPAMI
76.32
86 73
44.54
58.44
LastShot
40
TPAMI 24
76.76
87.49
44.08
59.14
SemiFew
CVPR 24
84.34
89.1 11
54.27
65.02
four
ECER
AAAT225
96.01
57.34
VIFFSL
OUIS
88.67
01:45
57.90
67.68
Table 3: Results (%) on CUB 68
Dogs 77), and Cars [69]. The average accuracy with 95%
confidence interval is reported. Bold and blue font indicates the best and suboptimal results.
CUB-200-2011
Sranford-Dogee
Stanford-
Method
Venue
1-shot
5-shot
I-shot
5-shot
1-shot
5-shot
ProtoNet 62
NeurlPS' 17
63.4420.50
83. 17.0.5
=1.61
76.7822..5 58
40.01 +0.19
87.19-20.1
FRN 75
CVPR'21
83.55=0.11
92. =0.92
=9.37=0.20
67.13
58.90+0.22
79.65=0015
DAN 76
AAA1'22
72. 72.89+0.50
86.60=0.31
59.81
77.19+0.3
70.21+0.50
85.55=0031
MPGN 77
LICAI'22
84.0110.30
84.01 0..39
74. 74.811:0.0
86.52.0.26
TDM 78
CVPR'22
84.36:20.19
93.37=0.1
57.6420.22
75.03:0.11 10
68.3620.22
86. 14:0.12.1
BSFA 79
2CSVT'23
86.0010.41
92.53 20.23
69.58
82.5910.3 33
88, 18,9310.35
95.20
MLI 80
TIP'24
85. +0.94+0.42
93.50=0.29
76.32=0.47
88.25 27
C2-Net 81
AAAT'24
83.31 40.41
18:0822
75.50=0.40
87.65 28
88.96:0-37
95.16:2:00
SUITED
82
AAAI'25
86. 86.02+0.47
94.13=m0.2
76.55=0.47
88, 15.8660.22 27
89.97+0.36
96.53=0016
VT-FSL
ours
91.0810.28
94.63.10.19
86.58L0.30
90.69L0.25
92.9510.24
96 15
Fine-grained Few-Shot Classification. The classification results on three fine-grained datasets are presented in Table It can be observed that the proposed VT-FSL method obtains the best classification results, outperforming the second-best SUITED (82) method by significant margin of 3.0%-110.39 in the challenging 1-shot task across three benchmarks, and is far superior to other competing methods. This shows that our VT-FSL can also be effective on the fine-grained few-shot image classification tasks to capture subtle inter-class differences and preserve intra-class consistency by bridging the cross- moosl semantic gap and enhancing multimodal integration.
Table 4: The average accuracy (%) is reported on cross-domain minilmageNet 12 CCU (68)
Places [68] and Plantae (72.. Bold and blue font indicates the best and suboptimal results.
CUB
Places
Plantae
Method
Venue
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
GNN 83
ICLR'18
44. 14.40.10.68
62.87 10.65
52, 10.80
70.91 10.65
33. 10.36
48. 18.51
FWT 84
ICLR'20
45.50=0.75
64.97
53.4420.19
70.70=
32. 22.56220.5
49.6620.62
AFA
ECCV'22
46.86-20.70
68.2510.85
54.0410.75
76.21 60
36.76.10.6
54.2610.08
UCD
86
NeurIPS'22
40. 60.65
58.54+0.70
51.84+0.72
72.19=0.60
37 28.677
54.155+.66
ATA
[7
AII'23
45.00=0.50
06.22:0.50
53.57
75.48
34.4240.40
52.69
LDP-net
88
CVPR'23
49.82=0070
70.39
63..82
72. 72.90=0.63
39. n..
58. 49.4920.c0
StyleAdv
89
CVPR'23
48.49=00
68. 08.72
58. 08.58
77. 77.33=0.62
41. 03
61.52+0.68
FAP 90
LJCAI'24
50.56.10.73
64.17
57. 17.34
72.05
37 44.10.54
53.5 13.5810.66
FL 91
CVPR'24 24
49.99=2065 69
70.
53. 120.70
72.3 20.62
40. 10:2..55
55.8020.66
MEFP
92
NeurIPS'24
51.55 70
73.61 61.00.
52.0610.80
73.78
41.55.10.5
6..3010.07
SVasP
93
AAAI'25
49. 49.49=0072
68.95+0.66
59.07+0.81
77.78+0.62
41.22+0.62
60.63+0.64
VT-FSL
ours
66.8610.47
81.02
81.52 73.6810.41
45.90
61.5440.33
Cross-Domain Few-Shot Classification. Following the setup 85
87
the model is trained on the
training set of mindmaaeeee and evaluated on three novel datasets: minilmageNet - CUB (68),
Places 668 and Plantac (72), which is more challenging due to significant domain shifts. As shown in
7

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Table 5: Ablation study on three datasets under the 1-shot and 5-shot settings. Pieur means textual prompts, and P Pission means visual prompts. Lalign is kernelized volume baseee-badddd contrastive loss.
minilmageNet
CIFAR-FS
magee
Plant
Prision
Calign
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
68.47:0.43
82.63.10.00
76.43:10.5
06.60=0.31
75.88=0.37
87.39+ 29
78.82 20.32
86.01
84. 76:0.36
89.23-0.29
86. 15:0.8
89.65
78.96
66.35++0.29
$6.5440..34
89.74=0.22
86.91=0.a7
90.19+0..z
79.17+0.35
86.26+0..2
86.01+0.34
89.75=0.22
85.541=0.38
90.01
79.76+0.35
86. 255.7240.20
86.64
90.13=0..8
85.93=0.36
90.30+0 29
82.0820.31
87.06:20.22
37.7220.33
90. 20014400.2
87.13=0.30
90.78
$3.66:20.35
88.38 00.38800225
88.67
88. 38.02200.34
91.71 00.277
Table 4 the proposed VT-FSL consistently outperforms all baselines by a large margin. Specifically, in the 1-shot task, VT-FSL surpasses the second-best methods by 4.35% (vs. SVasP) to 15.3 % (vs. MEFP) across the three datasets. These results demonstrate that VT-FSI. effectively learns more transferable representations by establishing comprehensive interaction with the class-specific cross-modal semantics, generalizing well to novel categories even under distribution shifts.
4.3 Model Analysis
Ablation Study. We conduct an ablation study on three datasets evaluate the effectiveness of our VT-FSL, as shown in Table 3 It is to be noted that (1) Introducing textual prompts alone improves performance over the baseline across all datasets, showing that precise and visually grounded semantics facilitate the extraction of discriminative features. Adding the kernelized volume- based contrastive loss, i.e., aligmment loss, further improves results, especially on minilmageNet and magred 1-shot settings, suggesting better cross-modal consistency. (2) Visual prompts also bring performance gains. The model with only visual prompts outperforms the baseline, and combining them with the alignment loss achieves further improvements from 89. 99.75% to 90.14% in the 5-shot task on CIFAR-FS, indicating the alignment loss enhances intra-modal structure. (3) The best performance is achieved when both prompts and alignment loss used. The full model consistently outperforms all ablations, demonstrating the complementarity of textual and visual prompts and the importance of jointly aligning them with the proposed geometric objective.
S-way S-shot setting
5-way 1-shot setting
Average result
minilangeN
tirrellaagee
CIFAR-ES
mohclmageNet
tievedinggeNet
CIFAR-PS
I-bui
S-hie
Figure 5: Comparison with textual semantics from name, definition of SemFeww
31 and ours.
Comparison with Different Textual Semantics. As shown Fig. 5 the texts generated by proposed VT-FSI. method achieve the best performance across all settings on three datasets, followed by SemFew B1 30 31 definitions and class names. This trend suggests that class names provide minimal information and that SemFew definitions, although generated by the LLM, rely only on class names and naive prompting, limiting the effectiveness. VT-FSL yields richer and more precise textual semantics by combining class names with support images and structured iterative prompting.
we compare three contrastive learning strategies: InfoNCE (94), volume-based loss [58] and our proposed kernelized volume-based loss. The baseline utilized without contrastive objective. InfoNCE performs worst, especially in the 1-shot setting, due to its reliance pairwise similarity, which overlooks interactions among multiple modalities. While the volume-based loss improves upon this, remains limited to linear space and fails to model complex semantic structures. In contrast, method
Comparison with Contrastive Learning Methods. As shown Table 6a

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Table 6: Comparison with contrastive learning methods and different prototypes for inference
(a) Contrasting learning methods
(b) Inference prototypes
miniImageNet
CIFAR-FS
minilmage
CIFAR-FS
Method
Method
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
baseline
82.49
87.35
87.01
90.34
C
71.63
86.13
80.75
90.34
InfoNCE [4
79.96
86,61
86.47
88.35
T + C
80.05
86,92
87.43
90.43
Volume [38]
82.33
87.59
87.20
89.72
V C T+
82.83
87.55
87. 27
90.63
VT-FSL
83.66
88.38
88.67
91.45
83.66
88.38
88.67
91.45
introduces a Reproducing Kernel Hilbert Space (RKHS) to enable nonlinear alignment, substantially enhances the ability to integrate cross-modal semantics and achieves the best results across settings.
Comparison with Different Inference Prototypes. In Table [bb] using only support features (C) yields the lowest accuracy, indicating limited discriminative power under scarce supervision. Incorporating textual or visual prompts significantly improves performance, demonstrating the value of external semantic guidance. The best results obtained when both prompts are jointly integrated (T' + V + C²), confirming their complementarity and the effectiveness of cross- modal synergy.
Table 7: Comparison of training and inference times on minilmageNet under 5-way 1-shot tasks.
Method
Prompt (h)
Training (min)
Inference (ms)
Acc (%)
SP (23
1.7
78
72.31 +0.40
SemFew 33
1.5
2.3
105
78.9440.06
ECER 33
0.7
3.0
119
81.14+t..15
VT-FSL (ours)
0.7
1.1
76
83.66++0.5
The effect of computational overhead with LLMs. To further investigate the efficiency of VT-FSL, we analyze the impact of computational overhead introduced by large language models (LLMs). The experiment is conducted on the same server configuration, comparing to SP (23 without any large models to generate descriptions/imaaee and SemFew [31 and ECER 30 with these models. SP and ECER employ the same Visformer-T backbone as VT-FSL during training/inference. As shown in the Table 7 VT-FSL achieves both the lowest training time and inference time, while attaining the highest accuracy (83.66%). Compared to ECER, VT-FSL reduces training time from 3.0 min min and inference time from 119 ms to 76 ms, while improving accuracy by 2.5%. These results confirm that the plug-in design of VT-FSL transfers knowledge from large models efficiently in one-time offline stage to construct cross-modal prompts. Any downstream FSL model can directly use the resulting cross-modal prompts, adding virtually no extra overhead during training or inference. This avoids the extra manual or algorithmic corrections required by SemFew and ECER, leading to better trade-off between efficiency and performance.
The effect of fusion factor u. u controls the relative weight between textual and visual fea- tures to obtain the final inference prototypes in Eq. 13 For each dataset, u is automatically determined by grid search over [0, 1] the corresponding validation set, selecting the value that yields the highest validation accuracy. As shown in the Fig. [ The optimal values are 0.5 (minilmageNet), 0.7 (tieredImageNet), 0.6 (CIFAR-FS), and 0.6 (FC100). The results show that performance peaks intermediate values of u, confirming that textual and visual features complementary. Using only visual prompts (u = 0) or only textual prompts (u = 1) consis- tently leads to inferior results.
Corrollngehe
CIAAR-FS
91:71
90
00.35
85
Accuracy
75
70
67.6
65
Valuee of
Figure 6: The effect of the fusion factor u.
The effect of the Synthetic Image Number. We evaluate how the number of synthetic images per
class affects performance in the 1-shot setting, as shown in Fig. 7a Accuracy improves notably when

---

Golden retriever
barrel
triceratops
vase
Number syntheii imago
ImageNet
11
Accomcy
Janus-Pap Peous-Pro
(a) The effect of the generated number
(b) Visualization of generated images
Figure 7: The effect of the generated number and visualization of generated images.
adding 1-3 synthetic images, but plateaus or slightly declines beyond that. This trend consistent across all datasets. These results validate our design choice of generating K synthetic images for K-shot tasks, which effectively augments the support set without compromising the low-data regime. However, increasing the number further introduces noise, as lower-ranked generations tend to be less discriminative and may degrade representation quality. Fig 7b we visualize the synthetic images generated by Janus-Pro on ImageNet [95]. Benefiting from precise descriptions with key visual attributes, synthetic images can well highlight the low-level semantics of the target category.
Baseline
Baseline
test, midillmaeeed Not
test, tiered dleredlmageNet
tast, CIEAR-PS
wi proopes Qur
(a) T-SNE visualization novel classes
(b) Visualization of attention maps
Figure 8: Visualization of T-SNE and attention maps.
Visualization T-SNE and Attention Maps. Fig. 8a shows that VT-FSL produces well-separated and compact class clusters, in contrast to the scattered distributions of the baseline, indicating improved semantic alignment. As shown in Fig. 88 the baseline suffers from attention to irrelevant regions, while method focuses more accurately key object areas, guided by cross-modal prompts and further enhanced through geometry-awan semantic alignment.
5 Conclusion
In this paper, we propose VT-FSL, novel framework that bridges vision and text representations with large language models to advance few-shot learning by generating complementary cooss-modal prompts and integrating them via geometry-awen Cross-moda. Iterative Prompting (CIP) to generate precise descriptions with high-level semantics from both class names and support images by single structure inference pass, enabling the zero-shot synthesis of semantically consistent images with low-level diversity. We further propose Cross-modal Geometric Alignment (CGA) to comprehensively align the fused textual, support, and synthetic features by minimizing the volume in their kernelized parallelotope space, capturing global and nonlinear cross-modal dependencies. Extensive experiments on ten FSL. benchmarks demonstrate the effectiveness of VT-FSL, improving the classification accuracy by 4.2% average.
consistency alignment. Specifically, we introduce
10

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Acknowledgment: This work was supported in part by the Natural Science Foundation of China under Grant U23A20389. 62176139. and 62406177, in part by the Shandong Excellent Young Scientists Fund (Oversea) under Grant D024HWYQ-021) in part by the Natural Science Foundation of Shandong province under Grant ZR2023QF122 124, part by the Young Scholars Program of Shandong University, and in part by the Fundamental Research Funds of Shandong University.
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A Appendix/ supplemental material
A.1 Training Algorithm
Algorithm 1:
Training algorithm of the proposed VT-FSL.
Input: Training set Cirain: feature extractor fe. linear projector 9t,, two-layer perception 9p while not converged do
1. Sample N- N-way K-shot task T = {S, 2} from Cerain by the episodic strategy: for each class ys in do
2. Generate textual descriptions TN via LLM with class name and K support images,
using four structured reasoning stages: Strategy, Perception, Refinement, Conclusion;
3. Generate M synthetic images from TN using text-to-image model;
Select top-K images with LLM-bbsed pairwise comparison according to Eq.
Extract support features 2, and synthetic visual features Z,, using fei
6. Encode textual features Ze via CLIP, and project them visual space with gy:
7. Fuse Ze and 2, via Ir along channel and spatial dimensions, generating enhanced support
features Z5 according to Eq. 4 Eq. and Eq. [
8. Calculate the kernelized volume- based contrastive loss LDZA and CA2D.jonnty denoted as Calign with Z, Z.. and Zy according to Eq. [0 and Eq. Eq.
Compute class prototypes C for each query image 2j in Q do
1. [ by averaging Z, per class according to Eq.
10. Compute the prediction scores Pa, according to Eq. 2
11. Calculate the cross cross-entropy loss between Pz, and the ground-truth label yy;
12. Calculate the overall loss Lioil according to Eq. T2.
13. Update 0, y, and via gradient backpropagation
Output: Final prototypes with text and synthetic images for testing according to Eq. 13
We describe the entire training procedure of VT-FSL in detail in Algorithm|] 1 The training process follows an episodic training paradigm and incorporates two key modules: (1) Cross-modal Iterative Prompting (CIP), which generates class-specific textual descriptions based on both class names and support samples through a structured iterative reasoning process; and (2) Cross-modal Geometric Alignment (CGA), which integrates textual, synthetic visual, and support features via volume-based contrastive loss a kernelized embedding space. These modules jointly enable the construction of cross- modal prototypes that are both semantically rich and visually grounded. The complete process, including prompt generation, feature extraction, fusion, prototype construction, loss computation, and parameter updates, is formalized in Algorithm T
A.2 Text Generation Scheme
Some studies 24 extract additional semantic information from class names to compensate for the lack of representative semantics in limited support samples. However, class names typically provide minimal contextual information and are inherently ambiguous. For example, the term "mouse" may refer either animal computer device, offering little discriminative value without additional context.
To address this, SemFew
31 uses a large language model (LLM) to expand a class name into detailed definition. However, to ensure textual-visual consistency, it relies on external knowledge bases such WordNet [32), introducing additional matching constraints and reducing generation efficiency. ECER (30 also employs LLMs to generate a set of visual attributes from the class name, but due to the lack of guidance from support images, it requires carefully designed attribute-filtering mechanism to remove visually irrelevant semantics.
In contrast, our method incurs no extra matching overhead and fully leverages the LLM's reasoning and generation capabilities to produce precise and visually grounded class deseriptions. We propose Cross-modal Iterative Prompting (CIP) framework, which conditions the LLM on both the class name and K support images elicit structured and grounded definitions. Specifically, CIP decomposes the generation process into four explicitly defined reasoning stages:
23

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Strategy Stage: Acts as a visual taxonomy expert, interpreting the class name to establish an initial semantic anchor and outlining the reasoning process.
Perception Stage: Analyzes the K support images to identify shared and discriminative visual traits that define the category.
Refinement Stage: Refines the initial interpretation through logical reasoning, aligning class semantics with observed visual evidence, while eliminating hallucinated or irrelevant attributes.
Conclusion Stage: Produces concise, scientifically accurate, and generalizable definition, following a unified format and concrete example.
We guide the LLM to produce answers each stage using carefully designed prompt struc- ture for iterative optimization the final generated textual descriptions. The input is for- matted using four special tags: <STRATEGY></STTATTTE /AAFIIEMENTT<</EFIIEEEENEE
SERCEPTTON><</PRCEPPT and <ONNCLSSIOO<<<OONCSSSSSSN corresponding to outlining the problem, interpreting visual information, conducting reasoning, and producing the final definition, respectively.
The complete prompt template is shown below:
Prompt Template for Cross-modal Iterative Prompting (CIP)
You are an expert in computer vision and concept definition. Given a class name and a set of representative images, your task is to generate a brief, scientifically accurate, and visually grounded definition. The definition should be primarily guided by the seman- tic meaning the class name, and refined using the visual evidence from images to resolve ambiguities and enhance relevance.
Please strictly follow the structured reasoning format below: <SUMMARY>
Briefly describe your overall approach: (I) Interpret the class and infer its possible meanings: (2) Analyze image content validate refine interpretation; (3) Formulate a revised description based on both sources. </SUMMARY>
<CAPTION>
Describe common visual elements observed across all images that help clarify the concept, such as objects, shapes, textures, colors, background environments. </CAPTION> <REASDNING>
Explain how the visual patterns confirm or adjust the class meaning, especially if is ambiguous, polysemous, or abstract. Use step-by-step reasoning to refine the concept. </REASONING> <CONCLUSION>
Rewrite the class definition concise, scientifically sound, and visually consistent paragraph. Avoid redundancy; prioritize clarity and relevance.
Example: The American robin is a widely recognized songbird, characterized by rust-red to orange breast, dark grayish-blue upperparts, white eye ring, and slender yellow bill. It is commonly found open woodlands and suburban gardens
North America. </CONCLUSION>
Class Name: (class __abe]]
Images: [K image inputs attached here]
A.3 Related Theories of Cross-modal Geometric Alignment
A.3.1
Gram Matrix-based Volume
Volume Computation Based on Gram Matrix. Given set of vectors Vi...., V& E RR", we aim to compute the volume of the k-dimensional parallelotope spanned by them. Let these vectors be arranged as columns in a matrix A .V....., Vol m The associated Gram matrix is defined as:
V1) (v2.
(V1, Ve)
(Vi. vk)
(V2, v2)
(V2, vk)
G(v - A
(14)
:
(Va,
(Vk, v2)
(ve, vn)
24

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This matrix captures all geometric information of the set [vi...., v&),, ...,,, including vector lengths and pairwise angles. The k-volume of the parallelotope is computed by:
Vol(v1,..... v&) det G(V Vk).
(15)
This formulation generalizes the intuition of Euclidean norm: when A 1, the volume reduces to the norm of single vector, i.e., Vol(v)
|v|l.
Theoretical Validity.
To justify the above definition, we consider three cases:
Case I: k n. Then.
det G(V1, V&) = det(AA A) = (det A)² = |A| 2 0.
(16)
Therefore,
Vol(vi,...., det A| = V ATA det G(VI,..., V&).
(17)
Case II: k < In this case, the volume lies within the k-dimensional subspace spanned by the vectors. Suppose (vi,..., Vi are linearly independent. We can extend this set to orthonormal basis (Wi....p Wn) of R" and define orthogonal transformation O that maps the basis to new coordinate frame
The transformed vectors become:
mi
m2
v O(v.),
mas
(18)
which visibly lie in k-dimensional subspace of IR Since orthogonal transformations preserve inner products, we have:
(V.. (Vi, Vg):
(19)
and thus,
Vol(vI, Va) Vol(vi..... va) = del G(Vi,..., Vn..
(20)
Case II:: k n. this case, the vectors must be linearly dependent, and hence the k-dimensional volume is zero:
det G(v vR) = 0, Vol(vi,..., 0.
(21)
Summary. The determinant of the Gram matrix provides robust and theoretically grounded measure of the volume of the parallelotope spanned by a set of vectors. This geometric quantity generalizes norm and angle in high-dimensional embedding spaces, and forms the basis of our proposed alignment loss for cross-modal representation matching.
A.3.2 Volume Generalized Alignment Metric
We demonstrate that volume computation based on the Gram matrix provides more comprehensive and expressive alignment mechanism compared to cosine similarity, particularly when multiple types of embeddings are involved.
We first consider the case of two vectors VI, V2 E I with unit norm, i.e., ||v = ||v = 1. The Gram matrix is:
[vv (vT,vi)
vi)
(VI v2)
G
(22)
(V2, vi)
(V2 v2)
25

---

with determinant:
det(G) = vi) v2)
(23)
Hence, the volume spanned by these two vectors is:
Vol = v de) = V v2)
(24)
Letting cos(0) (V1, V2), this becomes:
Vol V cos (0) sin(0).
(25)
This reveals that in the two-vector case, the volume is directly proportional to the sine of the angle between them, capturing the degree of geometric misalignment. Unlike cosine similarity, which increases vectors become aligned, volume achieves maximum when vectors orthogonal, offering complementary information and sensitivity to structural differences.
Volume Captures Full Pairwise Interactions. We extend the analysis to our setup involving three types of embeddings: textual features T, support embeddings S, and synthetic visual embeddings V. The corresponding Gram matrix is:
TT
TS
TV
G
ST
SS
SV
(26)
VT
VS
VV
where TS (T, TV TV= (T. V), and so on. All embeddings are normalized such that 7T SS = VV = 1. The determinant of this matrix expands as:
det(G) = TT. (SS VV - SV -V) TS (ST VV SV VT)
+V. (ST (ST VS SS VT),
(27)
and simplifies to:
(et(G) = 1- 1111--1111 (1- -S (TS- SV VT) +TV -TS-SV - TV)
- SV -52 +TS 5V +TV .TS 5V TV2
T5²
= 11-11 - SV - 2 -
SV- VT.
(28)
This result shows that volume-based computation naturally incorporates all pairwise interactions among the three embeddings-texiee textual (T), support (S), and synthetic visual (V). In particular, accounts for relationships like SV. which not explicitly considered when only using pairwise cosine similarities between the anchor (T) and the other embeddings.
Comparison with Cosine-based Alignment. In many existing methods, only the similarities be- tween the anchor and the remaining embeddings (e.g.. TS and TV) are computed, while relationships among the non-anchor embeddings (e.g., SV) are ignored. This may lead to suboptimal alignment, especially when synthetic features or support embeddings deviate semantically.
In contrast, volume-based alignment considers all pairwise inner products in unified geometric form. The determinant of the Gram matrix thus encodes higher-order consistency among embeddings, enabling more robust alignment objective that jointly optimizes their global compatibility.
26

---

A.3.3 Kernelized Volume for Nonlinear Alignment
While the volume computed from the standard Gram matrix already captures higher-order geometric relationships among textual, support, and synthetic visual embeddings, it remains linear measure. To further model complex nonlinear relationships, extend our formulation to high-dimensional Reproducing Kernel Hilbert Space (RKHS) via kernel embedding.
Let K : R" x I + IR be a positive definite kernel function. The most common example is the Radial Basis Function (RBF) kernel, defined as:
x 2|1
<(x, 2) exp
(29)
2x²
where a controls the smoothness of the kernel. Such kernels define an implicit mapping Φ RR" - H into high-dimensional Hilbert space H. where
<(xx, z) = (Ø(x), o(z))
(30)
Given set of l embeddings (vi...., VkJ, we construct kernel Gram matrix K lxk as:
K; vy),,,y)). Vi,j E {1....,k).
(31)
Due to the positive definiteness of the kernel Gram matrix K is symmetric and positive semi- definite. Thus, we can generalize the definition of volume in the RKHS as:
Volu(V1..... v&) / (e).
(32)
This formulation retains the original motivation of measuring the mutual independence spread among embeddings, but now in a nonlinear feature space where complex dependencies can be captured.
Why Kernelized Volume Matters. The kernelized volume metric Voly, has several key advantages over both cosine similarity and its linear volume counterpart:
Nonlinear feature interactions: Through the kernel map σ, we effectively transform the original embeddings into high-dimensional space where nonlinear interactions become linearly separable. This enables detection of subtle structural mismatches between the three embeddings (text T, support S, synthetic vision V) that may not be captured by inner product or cosine measures in the original space.
Higher expressive power: Unlike cosine similarity, which only encodes directional align- ment between pairs of vectors, the determinant (K) captures how all embeddings interact geometrically in the kernel space. This includes all mutual pairwise kernel similari- ties and their higher-order arrangements.
Connection to independence and compactness: When the embeddings ((v1)..... ..... d( (v&)) are highly correlated, the volume tends to zero, indicating collapse in representation diversity. Conversely, larger volume suggests that the embeddings geometrically well-distributed and carry complementary information. This is especially valuable for few-shot learning, where redundancy among features may severely harm generalization.
Kernel Properties and Geometric Interpretation. From the theory of positive definite kernels, know that for any set of points (21, .., ) the associated kernel matrix:
K
[ [
(33)
guarantced to be symmetric and positive semi-definite. This ensures that the square root of its determinant always yields valid (possibly zero) volume in RKHS.
In essence, Voly defines the volume of parallelotope formed by v(v1)...., in H:
Volæ(vi,-. ....... vx) [|Φ(v)) A... A Φ(vx))l,
(34)
where A denotes the exterior product. This geometric interpretation highlights its capacity to quantify not just similarity, but high-dimensional structural diversity.
27

---

Table The splits of categories and the number of categories/images in each few-shot dataset.
#( Class)
#(Image)
Dataset
D Danin
D Dalid
D test
Test
Total
minilmageNeet [12
64
16
20
12,000
60,000
eiered [60]
351
97
160
206,209
779,165
CIFAR-FS
64
16
20
12,000
60,000
FC100 19
60
20
20
12,000
60,000
CUB-200-2011
668
100
50
50
2,958
11,788
Stanford-Can 69
130
17
49
4,103
16,185
Stanford-Dogs 70
70
20
30
5,115
20,580
Places 7
183
91
91
18,200
73,000
Plantae 72
100
50
50
17,253
68,461
Summary. By embedding the embeddings into a kernel-induced space and computing their geo- metric volume, our approach moves beyond simple pairwise similarity, capturing richer nonlinear relationships among text, support, and synthetic visual features. This kernelized extension of the CGA loss offers greater alignment flexibility and improves robustness under complex cross-space discrepancies.
A.4 Experiments
A.4.1 Dataset Details
We evaluate our method on range of widely used few-shot learning benchmarks across three scenarios: standard few-shot classification, fine-grained few-shot classification, and cross-domain few-shot classification. Detailed statistics of all datasets are summarized in Table
(1) Standard Few-Shot Classification Benchmarks. Following ing four benchmark datasets:
23
31
47
adopt the follow-
minilmageNNt [2]:
A subset ImageNet (95] consisting of 100 object categories, each with 600 images, totaling 60,000 images. The dataset is split into 64 classes for training, 16 for validation, and 20 for testing.
mieredImageNet [60]:
larger subset of ImageNet 95 comprising 779,165 images from
608 categories, grouped into broader semantic superclasses. The training/valicon sriniing/valdaationnesst split contains 351, 97, and 160 categories, respectively.
CIFAR-FS 667: Derived from CIFAR-100 (96) by randomly splitting the 100 classes into 64 for training, 16 for validation, and 20 for testing. Each class contains 600 images, resulting in 60,000 images in total.
FC100 [19]: Also based CIFAR-100 196.. but categories are split according to their semantic superclasses. It uses classes from superclasses for training, 20 classes from 4 superclasses for validation, and 20 classes from 4 different superclasses for testing. The large semantic gap across splits makes FC100 more challenging.
(2) Fine-Grained Few-Shot Classification Benchmarks. datasets focused on fine-grained visual categories:
Following 80-82
evaluate on three
CUB-200-2011 (CUB) 68]:
Comprises 11,788 images from 200 bird species. The classes are divided into 100 for training, 50 for validation, and 50 for testing. Each image is cropped to the human-ane annotttnd bounding box of the bird.
Stanford-Cars [69]: Contains 16,185 images from classes of cars. The split includes 130 training classes, 17 validation classes, and 49 testing classes.
Stanford-Dogs [70] Consists of 20,580 images of 120 dog breeds. The dataset split into 70 training, 20 validation, and 30 testing classes.
28

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Table 9: The performance of different backbone architectures. *indicates our implementation.
minilmageNet
fieredImageNet
Method
Backbone
1-shot
5-shot
1-shot
5-shot
ProtoNet [1]
Resnet- 12
62.39
80.53=0.14
68. 03
84.03=0.16
ProtoNet
Visformer-T
62.48=0.35
79.78=0.20
68. 0..37
83.65=0.28
Meta-Baseline [22]
Resnet- 12
63.17=0.23
79.26=0017
68.62+0.27
83.29=0.18
Meta-Bass Meta-Baseline*
Visformer-T
62.59=0.31
79.88=0.27
00.01
82. $2.75=0.29
ours
Visformer-TT
83.66=0.33 31
$8.38=0.22 25
88.02+0.85 34
91.71 =0.27
(3) Few-Shot Classification Benchmarks. Following
84 88
91,
we train the
model on the mindmmaeeNet training set and evaluate it on domain-shifted datasets:
CUB 668 Same as above.
Places 71 contains 73,000 images with 365 scene categories. The classes are split into 183 base classes, 91 validation classes, and 91 test classes with 18,200 images.
Plantae [72]:
A subset of the iNaturalist [72) dataset focusing plant species with 68,461 images. We follow the 100/50/50 split for training, validation, and testing classes, with 17,253 images in the test set.
A.4.2 Ablation Study of Backbone Architectures
Table 9 showcases the performance comparison of different backbone architectures on the minilmageNeet and rieredImageNet datasets under 1-shot and 5-shot settings. The results reveal that directly replacing the standard ResNet-12 backbone with the more advanced Visformer-T T in existing baseline methods, such as ProtoNet and Meta-Baselinee fails to produce consistent perfor- mance gains. For example, in ProtoNet, the 1-shot accuracy only slightly improves from 62.39% to 62.48%, while the 5-shot accuracy even drops from 80.53% to 79.78%. similar trend is observed for Meta-Baseline, where performance fluctuates around the original ResNet- 12 values without notable improvement. This suggests that simply substituting the backbone does not effectively enhance the feature extraction or task-specific generalization ability of these methods.
In contrast, our method, VT-FSL, achieves a substantial performance boost when built upon the Visformer-T backbone. Specifically, VT-FSL attains 83.66% and 88.38% in 1-shot and 5-shot settings on minilmageNet, and 88.02% and 91.71% on tieredImageNet, outperforming all backbone- based baselines by a large margin. This significant improvement is attributed to the effective design of VT-FSL, which fully unleashes the potential of the backbone through cross-modal prompts and geometric-awan alignment. By leveraging rich cross-modal semantic information and global consistency among all features, VT-FSL. dynamically enhances the extracted features and facilitates more discriminative class-specific representations, leading to superior generalization in few-shot scenarios.
A.4.3 The Effect of Different Kernel Functions
Table 10: The effect of different kernel functions from kernelized volume-based contrastive loss
minilmageNet
CIFAR-FS
tieredImageNett 1-shot
Type
1-shot
5-shot
1-shot
5-shot
5-shot
Linear
82.01+0.30
87.10+0.27
87.33+0.33
90.25
87.35++0.36
90.07=0.28
Poly
82.76+0.31
87.80
87.82+0.33
89.87+0.30
87.51+0.36
90. 90.70=0.29
RBF
83.66+0.31
88.38=0.25
88.67+0.32
91.45=0028
08.02=0.34
91.71=0.27
The Effect Kernel Functions in CGA
We evaluate the impact of different kernel functions used in our kernelized volume-based contrastive loss. The three kemelss considered are:
29

---

y: (x y c)
Linear kernel
x(x,y)
Polynomial kernel (Poly)
(35)
x y
exp
20
Radial Basis Function (RBF)
Table reportss the results on minilmageNet, CIFAR-FS, and rieredImageNet under both 1-shot and 5-shot settings. We observe that the RBF kernel consistently yields the best performance across all datasets. For instance, it achieves 83.66% and 88.38% minilmageNet, 88.67% and 91.45% on CIFAR-FS, outperforming the other two kernels.
The superior performance of the RBF kernel is attributed to its ability to project data into an infinite- dimensional Hilbert space, capturing fine-grained nonlinear relationships among textual, support, and synthetic visual embeddings. In contrast, the polynomial kernel introduces limited nonlinearity and is sensitive to hyperparameters such as degree and offset. The linear kernel performs the worst, as lacks the expressiveness required model complex cross-modal structures and effectively reduces to dot product in the original feature space. These results validate the effectiveness of kernliized geometrie alignment and confirm the choice of the RBF kernel the default setting in approach.
A.4.4
The Effect of Anchor Selection in Contrastive Learning
Table 11: The effect of different Anchors from kernelized volume-based contrastive loss
minilmageNet 1-shot
CIFAR-FS
fieredImageNett
Type
5-shot
1-shot
5-shot
1-shot
5-shot
Vision
82.70+0.30
87.60+0.28
87.91
90.58=0.30 91.45=0.28
87.30+0.36
90.92=0.29
Text
83.66+0.31
88.38#0.
88.67#0.82
88.02=0.34
91.71=0027
The Effect of Anchor Selection in Contrastive Learning. We conduct an ablation study to investigate the effect of anchor selection in the kernelized volume-hased contrastive loss. Specifically, compare two anchor choices: (1) Text, where the textual feature Z, generated by the L.M serves as the anchor; and (2) Vision, where the synthetic visual feature Ze, generated by the text-to-image model from the same class description, used as the anchor. The results are reported in Table II As shown, using the textual feature as the anchor consistently leads to better performance across all datasets and settings. For instance, on minilmageNet, the accuracy improves from 82.70% to 83. 93.66% in the 1-shot setting and from 87. 87.60% to 88.38% in the 5-shot setting when switching from the Vision to the Text anchor. Similar trends are observed on CIFAR-FS (88.67% vs. 87.91%) and tieredImageNet (88.0) 088002% vs. 87.30%) under the 1-shot setting.
This result validates our default choice of textual anchor in contrastive learning. Although both generated text and synthetic images are used to enrich the support representation Z,, the textual feature Z, provides a class-level semantic anchor that is more stable and discriminative. In contrast, the synthetic visual feature Z., while visually grounded, is more prone to noise and distributional artifacts due limitations generative models. Furthermore, aligning support and visual features around a textual anchor enables the model to centralize its representation around high-level semantics, which is particularly beneficial under few-shot scenarios where visual variability high and labeled samples are scarce. Therefore, anchoring on the generated text helps better guide the cross- cross-modal geometric alignment and leads to more robust class representations.
A.4.5 The Effect of Different LLMs and Text-to- Image Models
We investigate the effect of large language models (LLMs) and Text-to-Image (T2I) Models on VT-FSL: LLM used for class description generation, and the text-to-image model used for synthetic image generation.
Large language models. As illustrated in Fig 9a we compare GPT-4o 33 and Qwen2.5- VL- 32B (34] under both 1-shot and 5-shot settings on minilmageNNet and CIFAR-FS. GPT-4o slightly outperforms Qwen2.5-VIL-22B in 1-shot settings and maintains comparable or superior performance in 5-shot settings. The consistent results validate the robustness of VT-FSL across different LLMs,
30

---

mintimageNet
CIFAR-FS
prDta ImaggeNet
CIFAR-FS
65'
95
BALLEJ
1-bbor
X-dve
(a) Large language models
(b) Text-to-image -oo-mmaeeeeea models
Figure 9: The Effect of different LLMs and text-to-image models
but also suggest that GPT-4o's structured reasoning and richer world knowledge provide more semantically precise class descriptions, which particularly beneficial when visual samples
scarce.
Text-to-image models. In Fig 9b we examine the impact of popular T2I models: DALL-E 3 (35) and Janus-Pro [36]. Results demonstrate that DALL-E 3 provides stronger vi- sual priors than Janus-Pro, yielding higher accuracy across both datasets and settings. This is likely because DALL-E generates more visually consistent and semantically faithful images from class- level descriptions, improving the quality of visual support features used during training. Despite Janus-Pro being multimodally capable, its generation quality is more variable and less optimized for fine-grained alignment with textual descriptions.
Overall, these results highlight that while VT-FSL is generally robust to backbone choices, the selection of higher- capaciiy and better-aligned LLMs and T2I models can further boost performance by improving the quality generated semantic priors.
A.5 Limitations
While VT-FSL demonstrates strong performance across a variety of few-shot classification bench- marks, several limitations remain. First, although we evaluate the method on cross-domain datasets such Places and Plantae, these settings still exhibit certain similarities to the source domain. The robustness of VT-FSL under more challenging distribution shifts (e.g., medical images) has not been fully assessed. Second, VT-FSL depends the quality of external generative models to provide textual descriptions and synthetic visual samples. While the proposed Cross-modaa Iterative Prompting (CIP) fully leverages the reasoning and generation capabilities of large models pro- duce semantically rich descriptions, the overall quality of the generated content remains inherently bounded by the capacity of the underlying models. For example, weaker LLMs may produce generic or noisy descriptions, and low-quality image synthesis could introduce misleading visual signals. Finally, although introduce a kernelized volume-based contrastive loss enhance multimodal alignment, its theoretical behavior under high-ddimensionl, noisy, or semantically entangled feature distributions remains underexplored. Further study is needed to rigorously understand its convergence properties and sensitivity to kernel choice. We hope these observations inspire future improvements robustness, interpretability, and generalization for few-shot multimodal learning.
31


Reviews:
[{"review_id": "wnojZkPGD1", "reviewer": "Reviewer_QnTs", "summary": "The paper provides a novel way to efficiently extract semantic features from an image in a few-shot regime. Authors perform this extraction by proposing an approach containing two novel procedures: first, they augmentate the available examples by providing an iterative procedure to extract the semantic features of an image as text and use them to generate a new semantic-grounded image; second, they provide a geometry-based loss function to extract those features in a way they are aligned with the semantics of the class.", "questions": "* I do not understand table 5. Does each row represent a different baseline with the specified properties (i.e., text/vision/aling)? In that case, an additional column with the name would be handy. If not, does each row correspond to the same pre-training stage and different fine-tuning procedures? In this case, that may help to clarify the observation made in the \"weaknesses\" section. \n\n* Baselines used for each table differ in some cases. Is there any particular reason for this? For instance, SemFew and ECER are the second best methods in table 2 but they don't appear in table 3. Similarely, SUITED is the second best method of table 3 but it is not shown in table 4. Is there any reason why those methods could not be used in other scenarios?", "limitations": "Yes, the authors adequately addressed the limitations and potential negative societal impact of their work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "= Strengths:\n* The approach provided by the authors is (as to my knowledge) novel and well implemented, although it can be seen as a more complex version of the work proposed in https://arxiv.org/pdf/2311.18649 . \n* The overall framework (backbone + pre-training + fine-tuning) is unarguably better than the baselines shown. \n\n= Weaknesses:\n* I found it a little difficult to understand the overall system and I think that its description (mainly section 3.3 \"Cross modal fusion\") can be improved. For instance, it is not clear from figure 2 if the \"Block\" is shared in all cases, or where the $\\beta$ vector appears on the figure. Other than that, the paper is generally well written.\n* My main concern is the conclusions taken by comparing with the used baselines. I understand that they are taken directly from the corresponding table of the previous works, but there seems to be a lot of variance in the pre-training data, the backbone model and the fine-tuning procedure. That makes it difficult to determine if the framework is better because, for instance, the tieredImageNet was used in the pre-training stage or because of the loss function designed. As such, claims like the ones in line 213-214 (\"These results highlight the effectiveness and the advantage of constructing complementary multimodal prompts with global alignment across all features for full semantic integration.\") are not necessarily true, since there is (as to my understanding) no direct comparison between different fine-tuning procedures for the same pre-training stage.", "was_revised": true, "final_justification": "I thank the authors for their thoughtful and detailed rebuttal. The clarifications on Section 3.3 and Figure 2 will indeed improve readability, and the additional details on parameter sharing and the role of β are welcome. I also appreciate the careful explanation regarding backbone fairness and the inclusion of ablation studies, which strengthen the claims of the paper. Similarly, the planned improvements to Table 5 and the justification for the selection of baselines address several of my concerns about clarity and fairness.\nThat said, while the rebuttal provides convincing clarifications, my original concerns regarding the comparability of baselines and the interpretability of some results are only partially alleviated. I remain positive about the contributions and overall quality of the work, I have updated my score to reflect this improved understanding and appreciation of the paper’s contributions.", "rebuttal": "We sincerely appreciate your valuable reviews and positive recognition of our work (e.g., \"novel and well-implemented approach\", \"generally well-written\"). All concerns will be carefully addressed point by point in the revised paper. Below is our feedback: \n\n> **Q: I found it a little difficult to understand the overall system and I think that its description (mainly section 3.3 \"Cross modal fusion\") can be improved. For instance, it is not clear from figure 2 if the \"Block\" is shared in all cases, or where the β vector appears on the figure. Other than that, the paper is generally well written.**\n\nWe would like to apologize if the description (mainly Section 3.3 \"Cross modal fusion\") has not been explicit enough and might thus have led to increased difficulty in understanding our work. \nIn the revision, this section will be carefully revised to clearly indicate the parameter sharing of the \"Block\" and the β vector in Fig. 2 to improve readability. \nSpecifically, first, as indicated in the caption of Fig. 2, the multiple Transformer blocks annotated with shared weights are served as the visual feature extractor. Moreover, we will clearly show in Fig. 2 where the β vector is computed and applied, as defined in Eq. (4):\n\n$\\beta = \\sigma \\left( W_2 \\, \\sigma \\left( W_1 \\left[ Z_t ; \\text{Avg}(z_s) \\right] \\right) \\right),$\n\nwhere β is computed by integrating the textual features $Z_t$ and the average support features  $Avg(z_s)$ via the two-layer MLP. The resulting β dynamically modulates support features $z_s$ along the channel dimension via token‑wise scaling. \n\n\n> **Q: My main concern is the conclusions taken by comparing with the used baselines. I understand that they are taken directly from the corresponding table of the previous works, but there seems to be a lot of variance in the pre-training data, the backbone model and the fine-tuning procedure. That makes it difficult to determine if the framework is better because, for instance, the tieredImageNet was used in the pre-training stage or because of the loss function designed. As such, claims like the ones in line 213-214 (\"These results highlight the effectiveness and the advantage of constructing complementary multimodal prompts with global alignment across all features for full semantic integration.\") are not necessarily true, since there is (as to my understanding) no direct comparison between different fine-tuning procedures for the same pre-training stage.**\n\nThanks for raising this concern about the fair comparisons in terms of pre‑training data, backbone, and fine‑tuning procedure. \nFirst, the proposed VT-FSL method adopts the same pre-training data and training procedure as in previous works [7–27]. \nSpecifically, VT-FSL follows the standard two-stage training framework, consisting of pre-training and fine-tuning on the training set of datasets (e.g., tieredImageNet) and is evaluated on the testing set, where both sets are disjoint. \nSecondly, VT‑FSL employs the widely used backbone Visformer‑T (≈ 10.0 M parameters), consistent with several previous works including SUN [13], SP [19], KTPP [20], and ECER [26]. The lightweight Visformer-T in terms of parameters is smaller than ResNet‑12 (≈ 12.4 M) and even several times smaller than other backbones (i.e., ViT-S/16 ≈ 22.0  M, Swin-T ≈ 29.0 M, and WRN-28-10 ≈ 36.5 M) to avoid unfair advantage from model capacity. \n\nFinally, an ablation study with different backbone architectures is conducted in Table 8 of the Appendix.  The results are reported on miniImageNet and tieredImageNet under 1-shot and 5-shot settings, summarized below: \n\n| Method | Backbone | miniImageNet 1-shot | miniImageNet 5-shot | tieredImageNet 1-shot | tieredImageNet 1-shot | \n|:-------|:---------|:----------------------|:----------------------|:------------------------|:------------------------|\n| ProtoNet| Resnet-12 | $62.39 ± 0.21$ | $80.53 ± 0.14$ | $68.23 ± 0.23$ | $84.03 ± 0.16$ |\n| ProtoNet * | Visformer-T | $62.48 ± 0.35$ | $79.78 ± 0.26$ | $68.85 ± 0.37$ | $83.65 ± 0.28$ |\n| Meta-Baseline| Resnet-12 | $63.17 ± 0.23$ | $79.26 ± 0.17$ | $68.62 ± 0.27$ | $83.29 ± 0.18$ |\n| Meta-Baseline * | Visformer-T | $62.59 ± 0.34$ | $79.88 ± 0.27$ | $68.01 ± 0.35$ | $82.75 ± 0.29$ |\n| ours | Visformer-T | $\\\\bf{83.66} ± 0.31$ | $\\\\bf{88.38} ± 0.25$ | $\\\\bf{88.02} ± 0.34$ | $\\\\bf{91.71} ± 0.27$ |\n\n$*$ denotes results produced by Visformer‑T. It can be clearly observed that merely replacing ResNet-12 with Visformer-T fails to improve results. Instead, VT-FSL significantly improves accuracy by an average of 20.0% and 7.8% under 1-shot and 5-shot settings, respectively.  This demonstrates that the proposed modules can fully leverage the unique advantages of ViT-based Visformer-T in long-range dependencies and cross-modal interactions by cross-modal prompts and geometric-aware alignment. Therefore, the claim in lines 213–214 is grounded on the fair experimental settings and is confirmed by superior performance of VT-FSL. More analysis is detailed in the Appendix. \n\n\n> **Q: I do not understand table 5. Does each row represent a different baseline with the specified properties (i.e., text/vision/aling)? In that case, an additional column with the name would be handy. If not, does each row correspond to the same pre-training stage and different fine-tuning procedures? In this case, that may help to clarify the observation made in the \"weaknesses\" section.**\n\nThank you for raising the concern about the meaning of each row in Table 5 and suggesting an additional column with name to improve clarity.\nEach row represents an ablated variant of VT-FSL, where the textual prompts $P_\\text{text}$, visual prompts $P_\\text{vision}$, and kernelized volume-based contrastive loss $\\mathcal{L}_\\text{align}$ are selectively enabled or disabled. \nFor example, row 1 of Table 5 denotes the baseline without any of the proposed modules of VT-FSL. \nIt is worth noting that results of all variants are built on the same backbone and training strategy, ensuring that the comparison isolates the contribution of each module. \n\nIn the final revision, an additional \"Setting\" column will be included to explicitly name each variant, as shown below:\n\n| Setting            | $P_{\\\\text{text}}$ | $P_{\\\\text{vision}}$ | $\\\\mathcal{L}_{\\\\text{align}}$ | ... |\n| ------------------ | ------- | --------- | -------- | --- |\n| Baseline           | ✗       | ✗         | ✗        | ... |\n| +Text              | ✓       | ✗         | ✗        | ... |\n| +Text+Align        | ✓       | ✗         | ✓        | ... |\n| +Vision            | ✗       | ✓         | ✗        | ... |\n| +Vision+Align        |✗        | ✓         | ✓        | ... |\n| +Text+Vision       | ✓       | ✓         | ✗        | ... |\n| +Text+Vision+Align | ✓       | ✓         | ✓        | ... |\n\n\n> **Q: Baselines used for each table differ in some cases. Is there any particular reason for this? For instance, SemFew and ECER are the second best methods in table 2 but they don't appear in table 3. Similarely, SUITED is the second best method of table 3 but it is not shown in table 4. Is there any reason why those methods could not be used in other scenarios?**\n\nThank you for pointing the concern about why many strong methods are not present in all SOTA experiments. The results of the existing methods in all SOTA tables are taken directly from their original papers to ensure a fair comparison.  However, strong methods such as SemFew, ECER, and SUITED are not evaluated across all scenarios in their papers.For example, SemFew and ECER have not been evaluated on fine‑grained benchmarks, whereas SUITED does not report results on cross‑domain scenarios. In contrast, to the best of our knowledge, the proposed VT-FSL is the first to be extensively evaluated across three distinct FSL scenarios (standard, fine-grained, and cross-domain) on ten datasets, achieving a significant average improvement of 4.4% compared to existing SOTA methods. \n\nFurthermore, these methods are faithfully re-implemented in the missing scenarios by extensive hyperparameter tuning including batch size, learning rate, weight decay, and training epoch. The results are shown below: \n\nCross‑domain miniImageNet → Places:\n| Method| 1-shot | 5-shot | \n|:-------|:----------------------|:----------------------|\n| C2-Net [78] | $56.78 ± 0.51$ | $75.38 ± 0.40$ | \n| SUITED [79] | $58.95 ± 0.45$ | $76.81 ± 0.38$ |\n| ours | $\\\\bf{73.68} ± 0.41$ | $\\\\bf{81.52} ± 0.33$ | \n\nFine‑grained CUB:\n| Method| 1-shot | 5-shot | \n|:-------|:----------------------|:----------------------|\n| SemFew [27] | $84.49 ± 0.41$ | $93.82 ± 0.30$ | \n| ECER [26] | $85.95 ± 0.40$ | $93.13 ± 0.29$ |\n| ours | $\\\\bf{91.08} ± 0.28$ | $\\\\bf{94.63} ± 0.19$ |\n\nEven with carefully optimized implementations, VT-FSL consistently outperforms these methods (e.g., +5.1% in CUB and +14.7% in Places under the 1-shot setting), further confirming the effectiveness and generalization of VT-FSL framework in all three scenarios due to full utilization of complementary visual and textual prompts with on rich semantics."}, {"review_id": "TGX43U4v6Q", "reviewer": "Reviewer_cNcP", "summary": "This paper uses a large language model and a text-to-image generation model to produce precise category descriptions and visual cues, serving as auxiliary information to enhance the support set images. Specifically, two key modules: Cross-modal Iterative Prompting, which utilizes category names and images to generate detailed textual descriptions and synthesized images; and Cross-modal Geometric Alignment, which employs a kernel-based volume minimization approach to align the representations across modalities are introduced. Experimental results validate the effectiveness of the proposed approach.", "questions": "Please refer to the weaknesses.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n1. While previous works have primarily focused on incorporating auxiliary semantic information, this paper introduces the novel perspective of additionally leveraging visual information.\n2. To better align support images, descriptive texts, and synthesized images, the paper proposes an interesting volume-based alignment method, which offers mathematical interpretability.\n3. The experimental evaluation is relatively comprehensive and complete, demonstrating strong performance across benchmarks.\n\n**Weaknesses**:\n1. Regarding the selection of top-k synthesized images, how is the reliability of the selection ensured? The paper employs a LLM as a discriminator to perform pairwise comparisons. However, I wonder whether this process involves the support set at all. If not, can the visual consistency between the generated images and the support set images truly be guaranteed?\n2. I understand this method as an attempt to transfer knowledge from large models to smaller ones, thereby enriching existing representations. The integration of a large model undoubtedly introduces additional computational overhead during training. I am curious about how the paper addresses or justifies this concern.\n3. The term fusion factor k can be easily confused with k-shot. Moreover, the paper lacks an ablation study on this hyperparameter. I would like to know how the model performance changes as k varies from 0 to 1. Additionally, what is the optimal value of k for each dataset? These details could be included in the supplementary material.\n4. Since your model internally utilizes the CLIP architecture, why not directly perform parameter-efficient fine-tuning based on CLIP?", "was_revised": true, "final_justification": "Thank you for the authors' detailed rebuttal, which resolved most of my technical concerns. Therefore, I maintain my original positive score.", "rebuttal": "We sincerely thank you for your thoughtful review and positive recognition of our work, such as “novel perspective”, “interesting volume-based alignment method”, and “strong performance”. Below, we provide a point-by-point response to each of your comments: \n\n\n> **Q:  Regarding the selection of top-k synthesized images, how is the reliability of the selection ensured? The paper employs a LLM as a discriminator to perform pairwise comparisons. However, I wonder whether this process involves the support set at all. If not, can the visual consistency between the generated images and the support set images truly be guaranteed?**\n\nThank you for raising the concern about ensuring the reliability of the synthesized images and their visual consistency with the support images. For each category, synthetic images are generated in a zero‑shot manner by feeding the textual description into a text‑to‑image generative model. The description, derived from both class names and support images, provides precise and visually grounded semantics, thereby facilitating the generation of images that share visual patterns with the support images. In addition, we introduce an LLM‑based pairwise comparison method that compares synthesized images in pairs to assess how well they match the textual description in terms of visual features. The images are then ranked accordingly, and the top‑K ones are selected to further enhance the reliability of generation.\n\nTo validate the effectiveness of top-K selection, as shown in Fig. 6a of the manuscript, it can be observed that adding the top-ranked 1-3 synthesized images consistently improves average accuracy to 3.3%, while adding lower‑ranked ones reduces by 0.9%.\nTo visualize visual consistency, Fig. 6b shows that the synthesized image is very consistent with the corresponding support image on the target object and carries less background noise. \nTo further validate, an additional analysis is conducted on miniImageNet, where $S_i$ and $G_i$ respectively denote support and generated images of the i-th category. For each category, the cosine similarities between the generated image and the support image for each category are computed, as shown below:\n\n|Similarity| $S_1$ | $S_2$ | $S_3$ | $S_4$ | \n|:-------|:----------------------|:----------------------|:----------------------|:----------------------|\n| $G_1$ | **0.87** | $0.12$ |  $0.01$ |  $0.00$ | \n| $G_2$ | $0.01$ | **0.74** | $0.25$ |  $0.00$ | \n| $G_3$ | $0.02$ | $0.02$ | **0.65** |  $0.31$ | \n| $G_4$ | $0.01$ | $0.00$ | $0.29$ |  **0.70** | \n\nAfter applying a temperature‑scaled softmax to each row, the resulting 4 × 4 similarity matrix shows that the generated image of each category achieves its highest similarity with the corresponding support image, confirming strong visual consistency and reliability of generated images. For example, $G_1$ from the first category aligns best with $S_1$ at  0.87, while other similarities are minimal. This additional analysis will be added to the final revision.\n\n\n> **Q:  I understand this method as an attempt to transfer knowledge from large models to smaller ones, thereby enriching existing representations. The integration of a large model undoubtedly introduces additional computational overhead during training. I am curious about how the paper addresses or justifies this concern.**\n\nThank you for raising the concern about the additional computational overhead from LLMs during training. In the proposed VT-FSL, the large model is used only once in an offline prompt‑construction stage to construct cross‑modal prompts. After this stage, all downstream training/inference relies solely on the common lightweight Visformer‑T backbone (≈ 10 M parameters), without any dependence on large models. The cross‑modal prompts are integrated with support features as feature representations, adding virtually no extra overhead during training or inference. \n\nTo further validate the efficiency, additional experiments of computational overhead are conducted on miniImageNet using the same server configuration (single NVIDIA RTX 6000 Ada 48G with two Intel 4410Y CPUs), compared to SP [19] without large models to generate textual descriptions and SemFew [27] and ECER [26] with large models. SP and ECER employ the same Visformer-T backbone as VT-FSL during training.\n\nAs shown in the table below, VT‑FSL achieves the lowest training (1.1 min) and inference time (76 ms) while obtaining the highest accuracy (83.66%). In particular, compared to ECER [26], VT‑FSL reduces training time by 63% (1.1 vs. 3.0 min) and inference time by 36% (76 vs. 119 ms), while improving accuracy by +2.5%. This improvement is attributed to the plug-in nature of VT-FSL, where cross-modal prompts are constructed once offline and can be directly used by any downstream training FSL model without training or additional computation, avoiding the additional manual or algorithmic corrections of SemFew [27] and ECER [26].\n\n| Method            | Prompt (h) | Training (min) | Inference (ms) | Acc (%) | \n| ----------------- | ----------------------- | ---------------------- | ---------------------- | ------------------ | \n| SP \\[19]          | --                      | 1.7                    | 78                     | 72.31                | \n| SemFew \\[27]      | 1.5                     | 2.3                   | 105                     | 78.94               | \n| ECER \\[26]        | 0.7                     | 3.0                    | 119                     | 81.14                | \n| **VT-FSL (ours)** | **0.7**                 | **1.1**                | **76**                 | **83.66**            | \n\n\n\n> **Q: The term fusion factor k can be easily confused with k-shot. Moreover, the paper lacks an ablation study on this hyperparameter. I would like to know how the model's performance changes as k varies from 0 to 1. Additionally, what is the optimal value of k for each dataset? These details could be included in the supplementary material.**\n\nThank you for pointing out the potential confusion caused by the notation of the fusion factor k and suggesting an ablation study for it. \nFirst, to avoid confusion with k-shot, the fusion factor will be renamed from $k$ to $u$ in the final version. \nSecondly, the fusion factor  $u$ controls the relative weight between textual and visual features to obtain the final inference prototypes in Eq. (13) of the manuscript. For each dataset, $u$ is automatically determined by a grid search over [0, 1] on the corresponding validation set, selecting the value that yields the highest validation accuracy. The optimal values are 0.5 (miniImageNet), 0.7 (tieredImageNet), 0.6 (CIFAR‑FS), and 0.6 (FC100).\n\nAs shown in the table below, a full ablation study is conducted by varying $u$ from 0 to 1 with a step size of  0.1 in the 5‑way 5‑shot setting. The results show that performance peaks at intermediate values of $u$ , confirming that textual and visual features are complementary. Using only visual prompts ($u$= 0) or only textual prompts ($u$ = 1) consistently leads to inferior results. These ablation results, together with the optimal $u$ values, will be included in the supplementary material of the final version.\n\n| value of u| miniImageNet | tieredImageNet | CIFAR-FS | FC100 | \n|:-------|:---------------|:--------|:---------|:---------|\n| 0 | 86.92 | 89.68 |  90.43 |  65.07 | \n| 0.1 | 87.36| 90.22  |  90.68 |  65.82 | \n| 0.2 | 87.69 | 90.67 |  90.93 |  66.41 | \n| 0.3 | 88.03 | 91.06 |  91.14 |  66.96 | \n| 0.4 | 88.26 | 91.37 |  91.31 |  67.39 | \n| 0.5 | **88.38** | 91.57 |  91.41 | 67.57 | \n| 0.6 | 88.38 | 91.68 |  **91.45** |  **67.68** | \n| 0.7 | 88.32 | **91.71** |  91.40 |  67.49 | \n| 0.8 | 88.16 | 91.58 |  91.25 |  67.06 | \n| 0.9 | 87.91| 91.34 |  91.00 |  66.40 | \n| 1.0 | 87.55 | 90.96 |  90.63 |  65.54 | \n\n\n> **Q: Since your model internally utilizes the CLIP architecture, why not directly perform parameter-efficient fine-tuning based on CLIP?**\n\nThank you for pointing out this insightful question. \nFirst, following the same settings [20, 24, 26, 27], the proposed VT-FSL only uses the frozen CLIP text encoder to extract textual embeddings, ensuring a fair comparison with prior FSL methods that do not fine‑tune CLIP.\nSecondly, conventional FSL approaches [19-27] assume the training pipeline consists of pre‑training and meta‑tuning. Directly fine‑tuning CLIP breaks this setting, as CLIP is pre‑trained on hundreds of millions of image‑text pairs. It becomes difficult to disentangle whether gains come from algorithmic design or access to massive external pre-training data, leading to potential unfair comparisons. \nMoreover, CLIP models such as ViT‑B/16 (≈ 86 M parameters) are ~8× larger than typical FSL backbones (e.g., Visformer-T ≈ 10 M), significantly increasing computational cost.\n\nAs shown in the table below, we replace Visformer‑T with CLIP ViT‑B/16 and perform both LoRA fine‑tuning and full fine‑tuning. While LoRA fine‑tuning yields only a marginal gain (+0.4% accuracy), it requires ~10× longer training time (10.4 min vs. 1.1 min) and ~5× more GPU memory (22.1 GB vs. 4.3 GB). Full fine‑tuning incurs even higher costs (14.8 min, 28.6 GB). These results confirm that parameter‑efficient fine‑tuning of CLIP is computationally expensive and less suitable under the conventional few-shot benchmarks, making it difficult for deployment.\n\n| Backbone     | Params (M) | Fine-tuning Strategy | Acc (%) | Training (min) | GPU Memory (GB) |\n|-----------------------------|------------|----------------------|--------------|-----------------------|-----------------|\n| Visformer-T| 10.8       | Meta-training | 83.66    | **1.1**           | **4.3**         |\n| CLIP ViT-B/16| 86.7   | Adapter (LoRA)       | 84.02        | 10.4              | 22.1            |\n| CLIP ViT-B/16   | 86.7       | Full Fine-tuning     | 84.45        | 14.8              | 28.6            |"}, {"review_id": "0xx1aDFUeH", "reviewer": "Reviewer_PMWQ", "summary": "This paper proposes a novel method named VT-FSL, which aims to bridge vision and text modalities using a large language model (LLM) to enhance few-shot learning (FSL). The authors argue that prototype-based FSL methods often suffer from non-discriminative class representations, resulting in semantic deviation from the true class centers. To address this, the proposed VT-FSL framework incorporates two key components: Cross-modal Iterative Prompting (CIP) and Cross-modal Geometric Alignment (CGA).", "questions": "All of my questions and concerns are outlined in the “Weaknesses” section above. If the authors are able to address these issues satisfactorily, I would be open to revising my score.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. The paper is well-structured and clearly motivated, making it easy to follow the authors' logic and contributions.\n2. The idea of leveraging both visual and textual modalities for FSL is novel and timely, particularly with the growing capability of LLMs.\n3. Experimental results demonstrate significant improvements over state-of-the-art methods, validating the effectiveness of the proposed approach.\n\nWeakness:\n1. The title of the paper is too generic and does not effectively convey the unique contributions or technical highlights of the work. A more specific and informative title would help better position the paper.\n2. Some notations in the paper are unclear or inconsistent. For example,  Eq.(4) lacks the definition of the $z_v$. Additionally, the usage of $Z_t$ and $z_s$ and $Z_s$ is confusing—it's unclear why uppercase and lowercase letters are used differently. A clarification or unification of these notations is needed.\n3. The paper would benefit from an ablation study on the loss function, e.g., evaluating the performance using only the cross-entropy loss, only $L_{D2A}$ and only $L_{A2D}$.\n4. A major concern lies in the fairness of the experimental comparisons. The image generation process appears to introduce additional samples, which may compromise the integrity of the few-shot setting. Moreover, it is unclear whether the generative model has been exposed to the novel classes during training, which could further undermine the validity of the results. This point should be explicitly discussed and clarified.\n5. The generation process seems to effectively double the number of shots in each class, which may lead to an unfair advantage. I strongly recommend the authors to conduct ablation studies that vary the number of generated images and report the impact on performance. This would help assess whether the performance gain is due to the proposed framework or simply the increased data volume.", "was_revised": true, "final_justification": "After reading the authors’ rebuttal, my concerns have been resolved. Therefore, I have raised my rating.", "rebuttal": "We thank your valuable comments and appreciation of our work such as “well-structured”, “clearly motivated”, “easy to follow”, and “novel and timely idea”. All concerns will be carefully addressed point by point in the revised paper. Below is our feedback: \n\n\n> **Q: The title of the paper is too generic and does not effectively convey the unique contributions or technical highlights of the work. A more specific and informative title would help better position the paper.**\n\nThank you for pointing out the concern about the overly generic title.\nWe agree that a more specific and informative title could further highlight our contributions. The current choice of title is deliberately chosen to balance informativeness and conciseness. The title \"Bridging Vision and Text with LLMs\" highlights the core idea of leveraging LLMs to construct precise cross‑modal prompts conditioned on both class names and support images and integrating them seamlessly. \n(1) The framework is designed as a plug‑in module: as shown in Appendix Fig. 8, the VT-FSL pipeline can be combined with other large models (e.g., GPT‑4o and DALL·E 3) to generate high‑quality cross‑modal prompts (e.g., 84.85% 1-shot accuracy on miniImageNet using DALL-E 3). Once constructed, these prompts can be directly utilized by any downstream training model in few-shot learning, providing strong flexibility and general applicability. \n(2) To the best of our knowledge, VT‑FSL is the first method to be extensively evaluated across three distinct FSL scenarios (standard, fine‑grained, and cross‑domain) on ten datasets, achieving an average improvement of 4.4%. \n\n\n> **Q: Some notations in the paper are unclear or inconsistent. For example, Eq.(4) lacks the definition of the $z_v$. Additionally, the usage of $Z_t$ and $z_s$ and $Z_s$ is confusing—it's unclear why uppercase and lowercase letters are used differently. A clarification or unification of these notations is needed.**\n\nThank you for pointing out this issue. These notations will be carefully unified in the revision. For example, $z_v$ is corrected to $z_s$ in Eq. (4), as shown below:\n$\\beta = \\sigma \\left( W_2 \\, \\sigma \\left( W_1 \\left[ Z_t ; \\text{Avg}(z_s) \\right] \\right) \\right),$\nwhere $Z_t$ denotes textual features encoded by the CLIP text encoder, the lowercase $z_s$ denotes the initial support features extracted by the visual backbone, and the uppercase $Z_s$ refers to the final enhanced support features after integrating textual features along spatial and channel dimensions. \nTo further improve clarity, in the revised version, we will rename $z_s$ to $\\bar{Z_s}$ and $Z_s$ to $\\widetilde{Z_s}$ to clearly distinguish initial support features from the enhanced ones.\n\n\n> **Q: The paper would benefit from an ablation study on the loss function, e.g., evaluating the performance using only the cross-entropy loss, only $L_{D2A}$ and only $L_{A2D}$.**\n\nThank you for the valuable suggestion. \nOnly the cross-entropy (CE) loss is reported in table 5 (rows 1, 2, 4, and 6) of the manuscript, corresponding to baseline (CE), text + CE, vision + CE, and text + vision + CE. \n\nAdditional ablation experiments are conducted using only $L_{D2A}$ and only $L_{A2D}$ on the miniImageNet and tieredImageNet datasets, as shown below: \n\n| $\\\\mathcal{L}_{\\\\text{D2A}}$ | $\\\\mathcal{L}_{\\\\text{A2D}}$  | miniImageNet 1-shot | miniImageNet 5-shot | tieredImageNet 1-shot | tieredImageNet 1-shot | \n|:-------|:---------|:----------------------|:----------------------|:------------------------|:------------------------|\n| ✗ | ✗  | $82.08 ± 0.31$ | $87.06 ± 0.27$ | $87.13 ± 0.36$ | $90.78 ± 0.28$ |\n| ✓ | ✗  | $83.16 ± 0.32$ | $88.01 ± 0.26$ | $89.32 ± 0.37$ | $91.31 ± 0.28$ |\n| ✗ | ✓ | $82.97 ± 0.23$ | $87.70 ± 0.17$ | $89.11 ± 0.27$ | $91.09 ± 0.18$ |\n| ✓ | ✓ | $83.66 ± 0.31$ | $88.38 ± 0.25$ | $88.02 ± 0.34$ | $91.71 ± 0.27$ |\n\nTextual, synthetic, and enhanced support features are transformed into normalized triplets $(Z_t, Z_v, Z_s)$. \nFirst, only CE the lowest accuracy, confirming that classification supervision cannot fully leverage multimodal information. \nSecondly, only $L_{D2A}$ significantly improves performance (e.g., 82.08% → 83.16% on miniImageNet 1-shot; 87.13% → 89.32% on tieredImageNet 1-shot), showing that text-anchored alignment effectively enhances global semantic consistency. \nMoreover, only $L_{A2D}$ also achieves clear gains but underperforms than $L_{D2A}$, suggesting that textual anchors provide stronger global semantic guidance. \nFinally, both $L_{D2A}$ and $L_{A2D}$ achieves the best results, confirming that bidirectional alignment offers complementary benefits and maximizes cross-modal feature integration. These new results and analyses will be included in the revised version.\n\n\n> **Q: A major concern lies in the fairness of the experimental comparisons. The image generation process appears to introduce additional samples, which may compromise the integrity of the few-shot setting. Moreover, it is unclear whether the generative model has been exposed to the novel classes during training, which could further undermine the validity of the results. This point should be explicitly discussed and clarified.**\n\nThank you for pointing out the concerns about the potential fairness issue of additional samples and exposure of the generative model to novel classes. \nTo alleviate the challenge of rare sample, several FSL methods [24, 58] have attempted to generate additional visual samples. Unlike relying on the class names in CaFo [24] or the support sample in ProtoDiff  [58] to generate, VT-FSL can generate samples with semantic consistency based on precise and visually grounded descriptions in a zero-shot manner. In addition, to ensure fairness, the VT-FSL generation process strictly follows the standard N-way K-shot FSL protocol, using only the given K-shot support samples and never touching any other real support samples.\n\nAs shown in Fig. 5 of the manuscript, the effects of generated images using different textual templates are compared on miniImageNet, tieredImageNet, and CIFAR-FS. The average results are summarized below:\n\n| Text| 1-shot | 5-shot | \n|:-------|:----------------------|:----------------------|\n| ours (w/o $P_{vision}$)| $84.13$ | $88.76$ | \n| CaFo [24] | $81.91$ | $89.07$ | \n| SemFew [27] | $83.18$ | $89.20$ |\n| ours | $\\\\bf{86.78}$ | $\\\\bf{90.51}$ |\n\nInterestingly, using the template from CaFo [24] like “a photo of [CLASS]”  and class descriptions from SemFew [27] even leads to worse 1-shot performance than not using synthetic images (row 1) at all, confirming that the performance gains are not due to any prior exposure of the generative model to novel classes. Instead, the improvements come from precise cross‑modal prompts produced by CIP and geometry‑aware alignment (CGA), which fully exploit complementary textual and visual semantics.\n\n\n> **Q: The generation process seems to effectively double the number of shots in each class, which may lead to an unfair advantage. I strongly recommend the authors to conduct ablation studies that vary the number of generated images and report the impact on performance. This would help assess whether the performance gain is due to the proposed framework or simply the increased data volume.**\n\nCompared to the proposed VT-FSL, previous FSL works [24, 58] generate additional samples with more images or visual prototypes to enrich the limited support data. Moreover, as indicated in the above reply, the image generation process relies only on the given support images and never touches any additional real images. The entire process strictly follows the standard N-way K-shot setup in FSL [19-27], thereby ensuring fairness.\n\nAs shown in Fig. 6a of the manuscript, the ablation study of different generated images per class was conducted on miniImageNet, tiereredImageNet, and CIFAR-FS. The results are summarized below. It can be observed that adding top 1–3 generated images leads to a noticeable performance gain, while adding more images causes the accuracy to plateau or even slightly decline. This trend is consistent across datasets. Excessive generated samples introduce noise, as lower-ranked generations are less discriminative and can degrade representation quality. This validates the effective design of selecting top-K generated images for K-shot tasks, which augments the support set without compromising the few-shot regime. Consequently, these results demonstrate that the performance gain comes from the framework design rather than simply increasing data volume.\n\n| Generated number            |  miniImageNet | tiereredImageNet  | CIFAR-FS |\n| ------------------ | ------- | --------- | -------- | \n| 0          | 78.96       | 86.91        | 86.54        | \n| 1              |  83.66      | 88.02        | 88.67  | \n| 2        | 84.01       | 88.07        | 88.93        | \n| 3            | 84.20      | 88.16        | 88.91     |\n| 4        |83.98 | 88.04         | 89.21     | \n| 5       | 84.0       | 88.02         | 89.01      | \n| 10 | 83.06       | 87.52        | 88.51     |"}, {"review_id": "z6Jet958M5", "reviewer": "Reviewer_6u5o", "summary": "This paper claims to be addressing the semantic hallucination problem when class names or LLM generated descriptions of classes are used in few-shot learning. The paper argues that features corresponding to LLM description of classes or the class attributes (generated based on the class names) must be aligned with the support images and generated synthetic images. The paper uses a prompting strategy to select the best K (e.g., 4) synthetic images from multiple images generated by JanusPro based on the textual description given by Qwn2.5-VL-32B using a pair-wise comparison. The vision encoder is Visformer-Tiny and the text encoder is ViT-B/16 CLIP. It aligns textual features, features of generated images, and the features of the support set images. \n\nThe results---standard few shot classification on miniImageNet and tiredImageNet, CIFAR-FS FC100, fine-grained few-shot classification on CUB-200-2022, Stanford-Dogs, Stanford-Cars, cross-domain few-shot classification on miniImageNet to CUB, miniImageNet to Places, miniImageNet to Plantae---are superior.", "questions": "1.\tRelated to items 1 and 2 above, justify the use of several large models. Report on the timing. \n2.\tAddress the concern about the insufficient novelty. \n3.\tImprove clarity.", "limitations": "Reliance on the LLM and the image generator.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1.\tSuperior results. \n2.\tPrompting using the features of both support-set images and textual descriptions. \n3.\tAligning the embeddings of textual descriptions, support image embeddings, and generated image embeddings. \n\nWeaknesses:\n\n1.\tUsing a large language model and a large image generator in addition to the Visformer-Tiny visual encoder gives an unfair advantage to this model. It would be important to compare training and inference times with a few existing papers. \n2.\tPicking the best K image generations probably gives very clean images representing the class in question. Fig. 6b shows such clean generations. The ablation in Table 5 shows that the contribution of the P_vision alone is the largest. This too is, in my opinion, an unfair advantage.  \n3.\tWhat do the numbers in row 1 of Table 5 represent? Is it the few-shot classification accuracy of Visformer-Tiny?\n4.\tUsing a carefully generated descriptive caption embedding with prototype embeddings exists in works cited in this paper, e.g., [27], and [26]. In this context, the novelties are 1. Cross-modal prompting, and 2. Geometric alignment. I am concerned whether this novelty is sufficient. Does the ablation show prompting with textual data only?", "was_revised": true, "final_justification": "I have read through the comments by other reviewers, authors' rebuttals, and further responses. These discussions, in my opinion, substantially address the concerns. Therefore, I wish to retain my \"Borderline accept\" rating.", "rebuttal": "We thank you for the constructive review and appreciation of our work such as “Superior results”. Below is our feedback. All concerns will be carefully addressed in the revised paper.\n\n\n> **Q: Using a large language model and a large image generator in addition to the Visformer-Tiny visual encoder gives an unfair advantage to this model. It would be important to compare training and inference times with a few existing papers.**\n\nThank you for pointing out the concern about the potential unfair advantage of LLMs and generative models, suggesting a comparison of training and inference times. \nSeveral recent methods [20, 24, 26, 27] also leverage LLMs and generative models to generate textual descriptions and additional images to compensate for limited data. In contrast, the proposed VT-FSL utilizes the LLM to generate precise class descriptions iteratively in a single systematic and structured reasoning pass, enabling the zero-shot synthesis of semantically consistent images by the generative model. \nMoreover, the LLM and generative model are used only once in an offline prompt‑construction stage to construct cross‑modal prompts. After this stage, all downstream training/inference relies solely on the common lightweight Visformer‑T backbone (≈ 10 M parameters), without any dependence on large models. The cross‑modal prompts are integrated with support features as feature representations, adding virtually no extra overhead during training or inference.\n\nWe compare the efficiency of training and inference times with a few existing FSL methods. This experiment is conducted on miniImageNet using the same server configuration (single NVIDIA RTX 6000 Ada 48G with two Intel 4410Y CPUs), compared to SP [19] without any large models to generate descriptions/images and SemFew [27] and ECER [26] with these models. SP and ECER employ the same Visformer-T backbone as VT-FSL during training/inference. \n\n| Method            | Prompt (h) | Training (min) | Inference (ms) | Acc (%) | \n| --- | --- | --- | - | -- | \n| SP \\[19]          | --                      | 1.7                    | 78                     | 72.31                | \n| SemFew \\[27]      | 1.5                     | 2.3                   | 105                     | 78.94               | \n| ECER \\[26]        | 0.7                     | 3.0                    | 119                     | 81.14                | \n| **VT-FSL (ours)** | **0.7**                 | **1.1**                | **76**                 | **83.66**            | \n\nAs shown in the table above, VT‑FSL achieves both the lowest training time (1.1 min) and inference time (76 ms), while attaining the highest accuracy (83.66%). Compared to ECER, VT‑FSL reduces training time by 63% (1.1 vs. 3.0 min) and inference time by 36% (76 vs. 119 ms), while improving accuracy by +2.5%. These results confirm that the plug-in design of VT-FSL transfers knowledge from large models efficiently in a one-time offline stage, and any downstream FSL model can directly use the resulting cross-modal prompts. This avoids the extra manual or algorithmic corrections required by SemFew and ECER, leading to a better trade‑off between efficiency and performance. \n\n\n> **Q: Picking the best K image generations probably gives very clean images representing the class in question. Fig. 6b shows such clean generations. The ablation in Table 5 shows that the contribution of the P_vision alone is the largest. This too is, in my opinion, an unfair advantage.**\n\nThank you for pointing out the concerns about unfair advantage caused by visual prompts $P_{vision}$. \nVisual prompts $P_{vision}$ are generated based on the precise and visually grounded descriptions and filtered to top-K clean images with high high-quality by LLM-based pairwise comparison strategy. \nAs shown in Table 5 of the manuscript, only $P_{vision}$ and only textual prompts $P_{text}$ consistently improves performance over the baseline by a large margin. However, when combing both $P_{vision}$ and $P_{text}$ significantly outperforms all ablations (e.g. from 79.17% of $P_{vision}$ to 82.08%), demonstrating the complementarity of textual and visual prompts. \n\nMoreover, combining table 5 of the manuscript, an additional ablation of w/o $P_{vision}$, top-K, and random-K selection is conducted. The results on CIFAR-FS and tieredImageNet are summarized as follows:\n\n| Method                               | CIFAR‑FS 1‑shot | CIFAR‑FS 5‑shot | tieredImageNet 1‑shot | tieredImageNet 5‑shot |\n| --- | -- | -- | -- | -- |\n| SemFew [27]| 84.34 ± 0.67 | 89.11 ± 0.54  |  82.37 ± 0.77 | 89.89 ± 0.52 |\n| ECER [26] | 86.01 ± 0.35 | --| 81.81 ± 0.51 | -- |\n| ours w/o $P_{vision}$ | 86.54 ± 0.34    | 89.74 ± 0.29    | 86.91 ± 0.37          | 90.19 ± 0.29          |\n| ours w/ random-K  $P_{vision}$   | 87.83 ± 0.34    | 91.01 ± 0.28    | 87.39 ± 0.34          | 91.09 ± 0.27          |\n| ours  w/ top-K  $P_{vision}$     | 88.67 ± 0.32    | 91.45 ± 0.28    | 88.02 ± 0.34          | 91.71 ± 0.27          |\n\nEven without $P_{\\\\text{vision}}$ (row 3), the proposed method already outperforms existing state-of-the-art approaches [26, 27]. Using $P_{\\\\text{vision}}$ with random-K selection still brings notable gains over these methods. These results and clarifications will be included into the revision.\n\n\n> **Q: What do the numbers in row 1 of Table 5 represent? Is it the few-shot classification accuracy of Visformer-Tiny?**\n\n| Setting            | $P_{\\\\text{text}}$ | $P_{\\\\text{vision}}$ | $\\\\mathcal{L}_{\\\\text{align}}$ | miniImageNet 1-shot | miniImageNet 5-shot | CIFAR-FS 1-shot | CIFAR-FS 5-shot |... |\n| -- | -- | -- | -- | - | -- | - | - | --- |\n| Baseline           | ✗       | ✗         | ✗        | 68.47 ± 0.43 | 82.63 ± 0.30 | 76.43 ± 0.45 | 86.60 ± 0.31 | ... |\n\nThank you for pointing this out. \nThe numbers in row 1 of Table 5 represent the few-shot classification accuracy of the baseline model, which uses the same Visformer-Tiny backbone but does not include any of our proposed modules: $P_{\\\\text{text}}$, $P_{\\\\text{vision}}$, and $\\\\mathcal{L}_{\\\\text{align}}$. This ensures a fair comparison, as all ablations and the full VT‑FSL model share identical backbone and training strategy. We will clarify this point in the revision.\n\n\n> **Q: Using a carefully generated descriptive caption embedding with prototype embeddings exists in works cited in this paper, e.g., [27], and [26]. In this context, the novelties are 1. Cross-modal prompting, and 2. Geometric alignment. I am concerned whether this novelty is sufficient. Does the ablation show prompting with textual data only?**\n\nThank you for raising the concerns about the novelty of descriptive caption generation and the ablation of textual data only.\nFirst, as indicated in the introduction of the manuscript, several recent methods, SemFew [27] and ECER [26], generate descriptive captions based solely on class names, without leveraging the valuable visual patterns in the support images. This limitation can lead to semantic hallucinations and suboptimal descriptions, which requires costly manual or algorithmic corrections.\nMoreover, naive input prompting prevents LLMs from fully exploiting their reasoning capability. \nIn contrast, the proposed VT-FSL method jointly leverages class names and support images via Cross-modal Iterative Prompting (CIP) to generate more visually grounded and precise descriptions through a single structured and systematic reasoning pass, followed by driving the synthesis of semantically consistent images.\n\nRegarding textual data only, the ablation studies in table 5 of manuscript clearly show that leveraging textual data alone already brings consistent performance gains over the baseline. A comparison of the results of textual data alone (ours w/o $P_{vision}$) with SemFew and ECER is shown below:\n\n| Method |CIFAR-FS 1-shot | CIFAR-FS 5-shot | tieredImageNet 1-shot | tieredImageNet 1-shot | \n|:----|:---|:---|:---|:--|\n| SemFew| $84.34 ± 0.67$ | $89.11 ± 0.54 $ | $ 82.37 ± 0.77$ | $89.89 ± 0.52$ |\n| ECER  | $86.01 ± 0.35$ | $ - $| $81.81 ± 0.51$ | $ - $ |\n| ours w/o $P_{vision}$ | $86.54 ± 0.34$ | $89.74 ± 0.29$ | $86.91 ± 0.37$ | $90.19 ± 0.29$ |\n| ours  | $\\\\bf{88.67} ± 0.34$ | $\\\\bf{91.45} ± 0.28$ | $\\\\bf{88.02} ± 0.34$ | $\\\\bf{91.71} ± 0.27$ |\n\nThese superior results demonstrate that the Cross-modal Iterative Prompting (CIP) can provide richer and more precise textual semantics than existing methods [26, 27], thus still providing stronger discriminative capability even without synthetic images. Finally, when combined with complementary synthetic images and textual data, the proposed VT-FSL achieves best performance by providing high‑level class semantics and low‑level intra‑class diversity to compensate for limited support data.\n\n\n> **Q: Improve clarity. Definitions of some parameters are missing (e.g., S_i) or given further away (e.g. z_s). Section 3.3 is difficult to follow.**\n\nWe would like to apologize if some parameters and the section 3.3 were not sufficiently clear. \nIn the revised version, we will carefully clarify the parameters and improve the organization of the Section 3.3 to make it easier to understand. \nFor example, $S_i$ is explicitly defined as the $i$‑th support category and support features $z_s$ are defined where they are first introduced to improve readability and avoid confusion."}]

Author rebuttal:

Meta-review: This work proposes a novel methodology to construct text prompts which are conditioned on Large Language Models and the support images in a few-shot learning scenario. To achieve this, authors integrate a geometry-aware mechanism that better aligns two modalities. During the review process, this work received mixed scores, with reviewers acknowledging its novelty, mathematical interpretability, and comprehensive and complete empirical validation. Nevertheless, they raised important concerns, mostly related to the fairness of the setting (i.e., encoder) used and the experimental analysis, a few inconsistent notations, and clarifications and suggestions to improve the readability of the manuscript. These weaknesses were mostly addressed during the post-rebuttal discussion process, where all the reviewers maintain a positive overview of this work, recommending borderline accept. Despite the rebuttal not fully addressing certain issues, the contributions and overall quality of the work are sufficient to justify its acceptance
Decision: Accept (poster)