DVLA-RL: Dual-Level Vision–Language Alignment with Reinforcement Learning Gating for Few-Shot Learning
Authors: Wenhao Li
Xianjing Meng
Qiangchang Wang
Zhongyi Han
Zhibin Wu
Yilong Yin
Venue: ICLR 2026 Poster (2026)
Keywords: Few-Shot Learning
Vision–Language Alignment
Large Language Models
Reinforcement Learning
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Few-shot learning (FSL) aims to generalize to novel categories with only a few samples. Recent approaches incorporate large language models (LLMs) to enrich visual representations with semantic embeddings derived from class names. However, they overlook progressive and adaptive alignment between vision and language from low-level to high-level semantics, resulting in limited semantic gains. To address these challenges, we propose Dual-level Vision–Language Alignment with Reinforcement Learning gating (DVLA-RL), which consists of Dual-level Semantic Construction (DSC) and RL-gated Attention (RLA). Specifically, DSC conditions LLMs on both class names and support samples to generate discriminative attributes, progressively selects the most relevant ones, and then synthesizes them into coherent class descriptions. This process provides complementary low-level attributes and high-level descriptions, enabling both fine-grained grounding and holistic class understanding. To dynamically integrate dual-level semantics along with the visual network layers, RLA formulates cross-modal fusion as a sequential decision process. A lightweight policy trained with episodic REINFORCE adaptively adjusts the contributions of self-attention and cross-attention to integrate textual and visual tokens. As a result, shallow layers refine local attributes and deep layers emphasize global semantics, enabling more precise cross-modal alignment. This achieves class-specific discrimination and generalized representations with merely a few support samples. DVLA-RL achieves new state-of-the-art performance across nine benchmarks in three diverse FSL scenarios.

Published as a conference paper at ICLR 2026
DVLA-RL: DUAL-LEVEL VISION-LANGUAGE ALIGN- MENT WITH REINFORCEMENT LEARNING GATING FOR FEW-SHOT LEARNING
Wenhao Lit2,, Xianjing Meng"", Qiangchang Wang'1, Zhongyi Han', Zhibin Wu', Yilong Yin"
I Sootware School, Shandong University 2Shenzhen Loop Area Institute School of Computing and Artificial Intelligence, Shandong University of Finance and Economics wwenhoo.. 1i, zhibinwu} @maimmll ii sdu. edu. cn rongmengyuan gmail com qiangchang wang, zhongyi han, ylyin)@sdu. edu..u.uuuu.... cn
ABSTRACT
Few-shot learning (FSL) aims to generalize to novel categories with only few samples. Recent approaches incorporate large language models (LLMs) to en- rich visual representations with semantic embeddings derived from class names. However, they overlook progressive and adaptive alignment between vision and language from low-level high-level semantics, resulting limited semantic gains. To address these challenges, we propose Dual-level Vision-Languanl Alignment with Reinforcement Learning gating (DVLA-RL), which consists of Dual-level Semantic Construction (DSC) and RL-gated Attention (RLA). Specifically, DSC conditions LLMs on both class names and support samples to generate discrimina- tive attributes, progressively selects the most relevant ones, and then synthesizes them into coherent class descriptions. This process provides complementary low- level attributes and high-level descriptions, enabling both fine-graine fine-grained grounding and holistic class understanding. To dynamically integrate dual-level semantics along with the visual network layers, RLA formulates cross-modal fusion as a sequential decision process. A lightweight policy trained with episodic REIN- FORCE adaptively adjusts the contributions of self-attention and cross-attention integrate textual and visual tokens. As result, shallow layers refine local attributes and deep layers emphasize global semantics, enabling more precise cross-modal alignment. This achieves class-specific discrimination and generalized representa- tions with merely a few support samples. DVLA-RL achieves new state-of-the-an performance across nine benchmarks in three diverse FSL scenarios.
INTRODUCTION
Deep learning has achieved remarkable success, driven by scage labeled datasets He et al. 2016
Dosovitskiy al.) 202T; Wang et al. [202]] 20256 Liang al.) 2025 Wang et al.) 2023a Meng et al. 2024 Li et al. 2025al Wang et al. 2025a Sun et al.) 2025 Ma et al. (025a) However, acquiring such annotations is often costly or even impractical in real-world settings. To reduce the reliance on abundant labeled data and simulate human rapid learning ability from limited experience, Few-Shot Learning (FSL) (Vinyals et al. 2016) is proposed to generalize the knowledge learned from a base set to novel and unseen tasks using few labeled samples. FSL has garnered significant attention due to its broad application prospects in real-world scenarios, such as rare disease diagnosis and industrial anomaly detection.
FSL is typically formalized as an N N-way K-shot episodic task (Snell et al. 2017). In each task, support set contains N classes with K labeled samples per class, while query set with several testing samples is involved to evaluate model performance. Most FSL methods project both support and query samples into shared embedding space and classify queries by comparing distances to supports. Researchers have conducted extensive studies on vision-basee approaches (Snell et al.
"quual contribution
I "ooresponniig authors.

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2017: Zhang et al. 2020 Dong et al. 2022: Hiller er et al., 2022 Sun & Gao 2023
Qi al. 2023
Baik et al. 2024] Ye et al.
2024; Kim et al. 2024; Poul et al., 2024| Hu et al. 2024; Guo et al. 2025].
aiming extract class-reless evlannt features from images and reduce intra-class variation. Despite their success, available representations may not be sufficiently discriminative for accurate classification due to limited samples, especially in the 1-shot scenario with only labeled sample.
Several recent methods (Xing et al., 2019; Xu & Le 2022 Pan et al. 2024, Zhang et al.) 2024a
Li et al. 2025c; Liu et al.) 2025; Li et 2025b) attempted to incorporate additional semantic information from other modalities, e.g., natural language, to assist in learning new concepts. SP (Chen ct al., 2023) encodes class templates with "A photo of [CLASS)" to generate semantic prototypes for complementing visual prototypes. SemFew (Zhang et al. 2024a) further enhances semantic quality via large language models (I..Ms) by expanding class names into textual descriptions with high-level information. However, they often ignore the low-level discriminative patterns that are critical for extracting class-specific features. alternative (Liu et al.) 2025) attempted to generate detailed class entities to replace high-level semantics. However, these methods rely solely on low-level or high-level semantic embeddings, failing to progressively align visual features at different layers with corresponding levels of semantics. Moreover, their static fusion modules struggle to perform adaptive vision- laiguale alignment across layers, resulting in limited semantic gains.
To address these limitations, a Dual-level Vision- Languag Alignment with Reinforcement Learning gating (DVLA-RL. is proposed to achieve hierarchical and dynamic cross-modal alignment from low- level to high-level semantics. Specifically, DVLA-RL consists of Dual-Ievel Semantic Construction (DSC) and the RL-gated Attention (RLA). In DSC, an LLM is conditioned on both class names and support samples to generate fine-grained attribute candidates. A progressive Top-k selection strategy then iteratively filters these attributes by measuring their semantic relevance to retain only the most discriminative ones, suppressing semantic hallucinations from irrelevant attributes. The selected attributes are further synthesized into coherent class descriptions, providing complementary local and global semantic guidance. To fully integrate these dual-level cues into visual feature extraction, RIA formulates cross-modal fusion sequential decision process. A lightweight policy trained with episodic REINFORCE dynamically balances self-attention and cross-attention between visual and textual tokens across network layers. This adaptive mechanism progressively enables shallow layers to focus on fine-grained local details, while deeper layers emphasize holistic contextual semantics. Consequently, DVLA-RL effectively captures complex cross modal relationships, enhancing both class specifi discrimination and generalization with merely a few support samples.
Overall, our contributions summarized as follows:
A DVLA-RL framework is proposed to achieve hierarchical and dynamic visual-language alignment between low-level and high-level feature extraction.
A DSC module is proposed to consistently generate fine-grained attributes and coherent descriptions as complementary semantics, effectively alleviating semantic hallucinations.
An RLA module is proposed to dynamically balance self-attention and cross-attention between vision and language tokens through reinforcement learning across network layers.
Extensive experiments are conducted on nine popular benchmark datasets across three distinct FSL scenarios, indicating the superior performance over the sfate-of-the-art methods.
2 RELATED WORK
Visual-based Methods. Few-shot learning (FSL) has attracted extensive attention due to its wide application potential in real-world scenarios. Visual-bascd methods focus on purely visual approaches that learn class-relevant representations from images. They are divided into two types. On the one hand, optimization-basee ctt al. 2024), aim to quickly adapt to novel tasks with only a few gradient steps. However, full- model adaptation under limited samples is prone to instability and degraded generalization. On the other hand, metric-base query samples can be classified by similarity to class prototypes. Representative works include MatchingNet (Vinyals et al. 2016) using cosine similarity and ProtoNet (Snell et al. 2017) using Fuclidean distance, with numerous variants exploring different backbones, distance metrics, and
methods, such as MAML (Finn et al., 2017) and NIW-Meta (Kim)
methods focus on learning discriminative feature embeddings where
2

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Attribute Description Summarization
The Koreoddr is dog with sire sire and uniguely conted bille cosl, forms
LIM
Transformer Loyer
Reward
Gencrate conaind acamnilia clua Jeaription from the atribatc list stull Duest panasiah.
High- Aligrment
Policy updete
Rd Gane
Gated Attention Block
Action selection
Progressive Top-k Selection
Transformer Loyer
PoPlyyy
network
Siilority
selccted armbuss
"A photo komonderr which hax (sttnheae)"
Low-Level Alignment
"A ploto o komoodee""
RL Gated Attention Block
RL Gate
H
Altizttes lix Fvordeed while cosf"...
Top Attributes
smilanty
Transformer Loyer
Sequence of Visual Tokens Attention
Cross-
Attention
Self="
Visual Attribute Extraction
"mreutive sirc". "sanded is bis cual "Jesse like strndd
Patch Embedding Transformer Loyers
Normalization
What the dhinggasiing sitrburc of the kommnder?
Tokens
Hau
Encoder
Figure 1: Overview of the proposed DVLA-RL framework. Dual-level Semantic Construction (DSC) extracts visual attributes with LLMs, progressively selects the most discriminative ones, and synthesizes them into class descriptions. Adaptive RL-Gated Attention (RLA) integrates these dual-level semantics with visual tokens, dynamically balancing self- and cross-altention between visual and textual tokens across layers for hierarchical and adaptive vision- lanonnlanguuae alignment.
prototype designs (Zhang et al. 2020 Dong ct al.
2022 Hiller et al.
2022 Sun & Gao 2023 Du
al. 2023 Hao et al. 2023 Baik al. 2024 Kim et al. 2024 Poul et al. 2024 Hu al.) (024).
Semantic-based Methods. Relying solely on limited visual samples makes it difficult to acquire
discriminative features. To mitigate this, semantic-haid methods (Xing et al. 2019 Xu Le 2022 Chen et al. 2023 Li et al. [2024] Pan et al. 2022)) Zhang et al. [2024a] |iu et al. (2025) incorporate additional semantic information from language modality to complement limited visual evidence. Early
AM3 (Xing et al. 2019) incorporates class-level semantics into visual prototypes. KTPP (Li et al.
2024
introduces pyramid-shaped cross-modal prompts to guide support features. SIFT (Pan et al.
2024h further employs sategory-speciff semantic embeddings to generate enriched features through
an encoding-decoding process. Recent SemFew (Zhang et al. (024a) and ECER (Liu et al.) 2025) explore the of large language models (LLMM)) construct global abstractions and local structures, but both rely on single-level semantics and static MI.P-based fusion modules that lack adaptivity across network depths during visual extraction. In contrast, DVLAARR achieves hierarchical and dynamic cross-mode modsl alignment by progressively coordinating low-level attributes and high-level descriptions with visual features through an adaptive RL-gated attention mechanism. To the best of our knowledge, this is the first attempt to introduce reinforcement learning for vision-language alignment in shw-shot learning.
3 METHODOLOGGY
Fig. shows whole overview of DVLA-RL framework. In this section, two novel components of level Semantic Construction (DSC) and Adaptive RL--Gated Attention (RLA) are detailed.
3.1 DUAL-LEVEL SEMANTIC CONSTRUCTION
Visual Attribute Extraction. Given a support category Crup and its support samples, the first step is to discover discriminative attributes that highlight inter-class differences. For example, corded white coat" massive size" can differentiate the Komondor from other breeds. To obtain such cues, we query large language models (LLMs) with the prompt / "'hhll are the key distinguishing altributes of the CLASS the given image? List concise attributes" ), which yields a set of candidate attributes:
A Ctup Pais (C
(1)
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where A {01,..... ,,,,),,))))))))))) denotes the attribute set, Lo is the LLM, and Pats is the attribute discovery prompt.
Progressive Top-k Selection. Not all generated attributes are equally relevant. To refine them, each attribute a, is encoded by the CLIP text encoder and scored against the current template embedding using cosine similarity as follows:
8 = =oscosT (T .ag):
where 7 is the evolving template at iteration i and T ["A photo of (CLASS)") $j denotes the similarity score. At each step, the most relevant attribute is appended to the template and used to update 1(++1), This progressive refinement continues until attributes are retained as follows:
ACt.p
(3)
The iterative enrichment ensures that only the most discriminative attributes are preserved, while hallucinated or redundant ones are suppressed. Each selected attribute is then embedded into sentence template such "A photo of a (CLASS), which has fattribute)", providing structured semantics with fine-grained information for subsequent low-level cross-modal alignment.
Attribute Description Summarization. To further capture holistic semantics consistent with local
details, the selected attributes S are summarized into a fluent scientific description D; using an LLM with the prompt /"Generate a concise scientific class description from the attribute list into single fluent paragraph" ]. For instance, the Komondor may be described as: "The Komondor is a a... dog with massive size and uniquely corded white coat, which forms dense..." Formally:
D; = Lo ssmm (ACn)))
where Psum is the summarization prompt. These global descriptions complement local attributes, resulting in dual-level semantics from low-level to high-level, jointly enhancing visual features.
3.2 ADAPTIVE RL-GATED ATTENTION
Given visual tokens Hing and textual semantics Htext, we first normalize both modalities with a shared operator to harmonize statistics: Hing = Norm(Hmee) and H Norm NNorm(Hext).. RLA then applies two reciprocal attention operators. In the image-guided path, textual queries Q attend to visual keys K and values V by cross- aosss-attnnton, leading to a visually grounded representation that retrieves discriminative and class class-specific image regions conditioned text-guided path, Q, K, V originate from textual tokens, resulting in a textually grounded refinement of semantic relations. Both paths follow the standard scaled dot-product attention. Formally:
textual semantics. In the
A Altn(Wit text Hraxt: Wi Fring Wing Pligg) img
(5)
ñ Atta( Waa Frest, W Fexxt, We Hrest);
(6)
Attn(Q, K, softmax(QK"" /
where Wa/k/u are linear projections into dddiimesiionl hidden shared space. To adaptively balance the two cross-modal paths, RLA fuses their outputs with stochastic gate:
H H (1- a~To( s),
(8)
= = GAP(() || cos (osA((()H))) GAP((Hex)))
ao(a s) Beta( (s(). n po(s)))
(10)
where a E [0, 1] is the mixing weight between the image-guides output H and the text- text-guided output H: is a compact state summarizing the cross-modal context via global average pooling (GAP) and its cosine similarity; & is a lightweight MLP producing po(s) (0, 1); no Beta policy whose mean equals (s(s) and concentration x > 0 controls exploration versus determinism.
The gate trained with REINFORCE using a task-aware reward:
R₂ Amm cos(U GAP(H), t²) + Ximp (Acc; Accz-1);
(11)
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where t" is the 512-dimensional ground-truth text embedding encoded by the CLIP text encoder, and is linear projection mapping the fused feature H after GAP to the same text space. The first term promotes visual-text alignment, and the second term measures intra-episode accuracy improvement, with Acc, and A denoting query accuracy at the current and previous fusion steps. The weights Xsims Aimp 0 balance alignment and improvement. The policy gradient is estimated
VeJ= ( V, log T |
VoH((To() s))
(12)
where b, is apoonetial-movinngavrreee baseline for variance reduction and r > 0 adds an entropy bonus H(-) to prevent premature collapse.
The fused H is injected back to the backbone via a residual addition followed by concatenation as follows:
Output Concat Hing GAP(H). H).
(13)
where the global semantic context vector GHP E Rd is obtained by averaging all textual tokens and broadcast to all visual tokens for channel-wise visual enhancement. The modulated Hing is then concatenated with H to form the extended sequence for subsequent Transformer layers.
For the training objective, the RL loss aggregates per-block contributions as:
H H( 1109))) Σ QEQ
CRL =
Σ
(0) (9)) T Σ (Re - b,) No, (a 88 QEQ
(14)
Q
where ne, is the policy of the 4-th block. During training, a applied in soft-hard mixture (sampling for stop-gradient and averaging for differentiable paths), while at inference it is replaced by the expectation E.,,aa] Stacking multiple RLA blocks allows the policy to adaptively emphasize attribute-level cues in shallow layers and description-level semantics in deeper layers, enabling hierarchy-aware vision-language alignment few-shot learning.
Moreover, prototype classifier is adopted as the training objective. For each class prototype embedding &; is computed by averaging the support features extracted by the backbone sø:
K
G = K Σ fa(x))
(15)
k=1
For query instance x9, its similarity to each prototype is measured by cosine similarity, and probability of assigning x to class i is given by:
exp cos (x))
ply² = x9
(16)
ΣS exp cos(sf (xx))
where is temperature parameter and cos(,,) denotes the cosine similarity function. The predicted label corresponds to the class with the highest probability. Finally, the overall loss also includes the supervised loss between the probability p; of the query sample q the i-th class between the probability ps of the query sample q to the i-th class and the corresponding ground-trus -tuth label, as follows:
Lup Σ
(a) log Pyç 9EQ
(17)
Lioul Loup X LRL.
(18)
where À denotes the trade-off hyperparameter of RL weights. All learnable model parameters finetuned by minimizing Liotal using episodes randomly sampled from training classes.
EXPERIMENTS
4.1 EXPERIMENTAL DETAILS
Datasets. We evaluate DVLA-RL across three primary tasks of general FSL, fine- tne-grained FSL, and cross-doman FSL. For general FSL, experiments are conducted on three benchmarks, namely mini- ImageNet (Vinyals al. 2016), nieredlmageNet (Ren et al. 2018), and CIFAR-FS (Lee et al. 2019).
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Table Results (%) on minilmageNet, rieredImageNet, and CIFAR-FS. The average accuracy with 95% confidence interval is reported. Bold and Blue font indicates the best and suboptimal results.
minilmageNNe
Iaggeee
CIFAR-FS
Model
Venue
T-shot
5-shot
T-shot
5-shot
1-shot
5-shot
ProtuNet (Snell et al.
2017
NeuriPS
62.39...21
80.5331.4
68 23.1.
84.032.16
72.20..70 70
83.50a
AM3 (Xing et al 2019
NeurIPS
36.304.40
78. 10
60 28
82, 58
DeepEMDD (Zhnng el all 2020
CVPR
65.91 1:
82.441
71.16.2.8
86.033.55
SUN [Dog
2022
ECCV
67.801.
83. 3u
72. 2.0
26.74
FeWTURE Hiller el al. 2112
NeurIPS
68.02++.8 88
84.51 +.55
72.96+t.22
86.43+.00 67
72.80+ 88
86. 16.144.61
SVAE Xu & La 2024
CVPR
74.84+.2a
70.98+t.66
85.77+.60
Meta-AdaM (Son Crao 2023
NewrIP
59.89
77.92+..48
65.31
85.24+.35
ProtoDiff (D al. E026)
NewrIPS
06. 63
83. 18:.11
72. +..24
85.15
CPEA (Ha et al. 2026
ICCV
71.97
87. ...66....
70.93
90.12
77.82
88. 88.98:2.46
SP CCen et al. 2073
CVPR
72.31
12:1.00
78. 43.
88. 55.
82.18 32.188:44
24
ALFA (Baik et al 2024
TPAMI
66.61+.22
23:
70, +0.
86.17
76.32
86. 73+.a1
LastShot Ye el all ZUFA
TPAMI
07.35.1.20
82.584.14
72. 121
85. 82
76....1
87.49...2
NIW-Meta (Kmm et
2024
ICLR
68. 5.1..66
84.81
74. 5..3
89.76.2.23
BECLR (Poul et 201z4
ICLR
75.741..0
84.933.:33
76, 16.-44...00
84.85
SIFT (Pan 2020
ICV
77.31+.67
86.95...8
77 .77
89.89
KTPPTLeen Lar LUZA)
MM
+:377
86..46
80.80+
90.01 #:29
83. 67
90. 10.199: 19
SemFew Zimpar ma
CVPR
78.94+..6
82. 12..77+77
89. -9.89
84.34+.67
11
UAP H (014)
NewrIPS
11.63t22
79.05st.18
79.68 .9..0
76.78s:21
ECER
C 2025
AAAI
81. 144.11
81,81
86.01+.a6
CPL
Guo et a 2025
TPAMI
72.82+.61
87. 97.93
78. 78.05
00.80..44
78.82#.65
89.98+.44
DVLA-RL
ours
81.69:.38
88.25
83.02
91.71=:29
87.18
90.59
For fine-grained FSL, we adopt CUB-200-201 (CUB) (Wah et al. 201T), Stanford Cars (Krause et al,, 2013), and Stanford Dogs (Khosla et al. 201 which contain subtle intra-class variations that pose additional challenges for recognition. In the cross-doman FSL setting, we follow the common protocol (Teng et al. 2020; Zhou et al. 2023: Zou et al. 2024) by training on minilmageNet and testing on three unrelated domains, including CUB, Places (Zhou al., (017)), and ChestX (Wang) et al. 2017). The detailed dataset statistics regarding the number of categories and images are summarized in the Appendix.
Training Details.
For training, we adopt a common two-stage FSL strategy (Dong al., 2022).
consisting of scale pre-training and meta-tuning, Following recent few-shot studies (Li et al.
2024: Chen et al. 2023 Liu et al. 2025), the visual backbone chosen as ViT-based Vistormer-
Tiny Chen et al. 2021). The text encoder is ViT-B/16 CL.IP (Radford et al. 2021) with a 512- dimensional embedding space. To provide dual-level semantic supervision, Qwen2.5-VL-333
(Bai (et al. (2025) is employed to generate both attribute-level and description-level textual information. All input images are resized to 224 x 224 (Zhang et al. 2024a). The optimization is performed using AdamW (Lohhhhlov & Hutter 2017) ) with initial learning rate of 5 x 10 and cosine learning rate scheduler (Loshchilov & Hutter 2016). Pre- Pre-training is run for 300 epochs on tieredImageNet and 800 epochs on other datasets with a batch size of 512, followed by 100 epochs of episodic meta-tuming. Beta concentration x- = 10 in RLA while Agm and Xamp are set to 0.5 and 1.0, respectively. The reinforcement learning gate uses À = 0.1 for balancing the RL objective and T 0.2 according to validation accuracy. All experiments are conducted on an NVIDIA RTX 6000 Ada GPU.
Evaluation Protocol. For evaluation, we adopt the widely used episodic protocol CCen et al. 2023 Li al. 2024 Xing et al., 2019 Li et al. 2020 Xu & Le [022; Zhang et al., 2023) 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-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 COMPARISON WITH THE SOTA METHOOS
General Few-shot Classification. Table I reports results on minilmageNet, tieredImageNet, and CIS We highlight three observations. (1) Semantic- based methods (e.g., AM3, SP, SIFT, SemFew, and ECER) generally outperform pure metric- or optimization based approaches, showing the advantage of incorporating language priors into few-shot learning. (2) DVL.ARRI consistently

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Table 2: Results (%) on fine-grained CUB, Dogs, and Cars. The average accuracy with 95%
confidence interval is reported. Bold and Blue font indicates the best and suboptimal results.
CUB-200-2011
Stanford-
Stanford-Cars
Model
Venue
T-shot
5-shot
T-shot
5-shot
T-shot
5-shot
ProtoNet (Du et al. 2023
NeurIPS
63. 44
83. 13.17
41. 11.61
76.78 78
45.01
87. 87.194..1
FRN Wortheemer et 2021
CVPR
33.55a.11
92. 12.922.110
49.37
G7. 07.13
=8.900..2
79.65+..15
DAN Xuu
AAAI
72. 72.59..
86. 60..01
59.81
77.19
70.21
85.55
MPGN Yu al
ICAI
84.01+.39
84.01
71.81
86, 86.52:226
TDM (Lee el ul
2072
CVPR
84.36a.11
93.37...1
57.64:1.22
75.03
68. 68.36...12
86.144..1
BSTA Zha et al
Z024
TCSVT
86, 00
02.53+
69.58...5
2..59..3
88, 23.93....
25
MIJI
TIP
85.94
93350
76.32:1-17
28.25
C2-Ner Ma et al.
2024
AAAI
83.31#t.11
92.18+.25
75.50+t.4
87. 27.66#.22
88. 96
95.16+:20
SUITED Ma al.
2025b
AAAI
86.024.47
94.134t.1
76.55st.47
88, 88.862.27
89. 89.974t:0
96.53+.11
DVLA-KL
ouTs
91.931.22
95.06
89.642.30
92. 95.95:..5
9..59
Table 3: Results (%) on cross-domain minilmageNet -CUB Places, and ChestX. The average accuracy with 95% confidence interval is reported. Bold and Blue font indicates the best and suboptimal resuls..
CUB
Places
ChestX
Model
Venue
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
GNN (Gareia & Bruna 2018
ICLR
44.40
02.87+.66
52.42s.66 80
70.91
22 2.000..
25.27+.30
FWT
seng el ul 2020
ICLR
45.50
64.97
53.441..9
70.70a.c 67
22 2..44: 44
25. 15.118..4
AFA HHu & Ma 3020
ECCV
46.86
54.044.78
76.21#.00
22.92+.20
25 02:.20
UCD
(Oh
NeurIPS
40.651.00
58.544..0
51.841.72
72.19a.
22.644..00
26.26.1.45
ATA (Wang et al. ZUEAb
AU
45.00
66. 66.224.50
53.57
75.48s.40
22.10s.20
24.3221.40
LDP-nal 7hou al. 2026
CVPR
49.82a.70
70.34 66
53.822.71
72.901 63
23.011:.5
26.677..0
StyleAdV
CVPR
48.49
68.72+.67
58.58 88
77.73
22.64
26.07
FAP (Zhang cla (AAAA)
UCAA
50.561.75
64.17+.60
57.34+.72
72.05st:4
21.562.20
24.15+:00
FLok
Zou er a. 2024
CVPR
49.99
70.39
53.18
72.31+.62
23.11
26.70
MEFP
Zhou al 2024
NeurIPS
51.55
73.61 73.61#t.66
52.06
73.78 61
22.82
26.53+..10
SVasP 2026d
AAAI
49.494.72
68 06.995..c
59.07
77.78+..2
23:.33
26.87 26.874.38
DVLA-RL
67.46+.47
78.994.95
69.26::46
80.70s.36
23.47+.20
26.944.22
achieves the best or second-best accuracy across all datasets and both 1-shot and 5-shot settings, For instance, obtains 81.69%/88. 25%% on minilmageNet and 87.18% on CIFAR-FS, surpassing the strong baseline SemFew by 0.6%-2.8% (3) These results indicate that our dual-level semantic construction effectively complements fine- mne-grained attributes with global descriptions, while the RL--gated attention adaptively balances cross-modal fusion across network depths. Such hierarchical alignment allows DVLA-RL to suppress semantic hallucinations and better capture discriminative cues from limited samples, ultimately leading superior generalization in FSL.
Fine-grained Few-shot Classification. The classification results on three fine- fine-grained datasets are presented in Table 2 It can be observed that DVLA-RI achieves the best performance on all benchmarks, significantly surpassing the second-best SUITED by 5.4%-15.3% in the 1-shot setting. In particular, DVLA-RL reaches 91.93%/95 06% on CUB, 89. 109.64%991.00 14.4%% Dogs, and 92.95% % on Cars, consistently outperforming existing state-of-the-at methods. These results demonstrate that DVLA-RL effectively captures subtle inter-class differences and preserves intra- class consistency by leveraging dual-level semantic construction and adaptive RL-gated attention fusion, thus exhibiting strong generalization ability in challenging fine-grained FSL tasks.
Cross-Domaie Few-Shot Classification. As shown in Table DVLA-RL, consistently outperforms all baselines across both 1-shot and 5-shot tasks on CUB, Places, and ChestX. In particular, it achieves 67.46%/78. /7.99%% on CUB and 69.26%/80. 70% on Places, exceeding the second-best methods by 2.1%-7.29 2% under the 1-shot setting. Even on the highly challenging ChestX dataset, DVLA-RL still attains competitive improvements over prior approaches. These results demonstrate that DVLA-RL learns more transferable representations by hierarchically aligning fine- ine-grained attributes and global descriptions, and adaptively balancing cross-modal fusion, thereby generalizing effectively to novel domains under distribution shifts.
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Table Ablation study on three datasets under the 1-shot and 5-shot settings, Attr and Dese means attribute-level and description-leve semantics, respectively. Top-k means progressive Top-k attribute selection, and RLA denotes adaptive RL-gated attention block.
minilmageNet
CIFAR-FS
CUB
Attr
Dese
Top-k
RLA
I-shot
5-shot
1-shot
5-shot
1-shot
5-shot
68.671.34
82.97
76.67:.43
86.89:
79.47
80.63
75. 75.73+.36
85. 76.76.22
84.82+.39
88.31
88.63+.31
90.00=.20
V
76.561.35
86, 25.25...29
85.741.39
88.91 38.911.30
89.331.30
00.91
78.3611.00
86.72 4..722332
85.574.41
89.044.31
88,561.30
89.72=.30
V
81.69 36
88, 25 28
87.182.40
90.59
91.93
95.06:.19
4.3 MODEL ANALYSIS
Ablation Study. We conduct an ablation study on three datasets to evaluate the effectiveness of each component in DVLA-RL, as shown in Table 4 It is observed that (1) Using only attribute-Ievel semantics provides consistent gains over the baseline, verifying that fine-grained attributes can serve effective discriminative cues. Adding description-leve semantics further improves accuracy across datasets, showing the complementarity of local and global guidance.
(2) The progressive Top-k selection strategy yields additional improvements (e.g., +1.1% on CUB 1-shot), indicating that iteratively refining the template helps suppress irrelevant attributes and enrich semantic grounding. (3) RLA brings further performance boosts by dynamically balancing self- and cross-attentin especially in the 5-shot setting. (4) The best results are achieved when all components are combined, where DVLA-RL attains 81. /1.69%/88. 25%% on minilmageNet, 87.18 17.18%%%90559 on CIFAR-FS, and 91.93% 000% on CUB. These results confirm the necessity of both dual-level semantic construction and adaptive fusion, and highlight the effectiveness their joint integration.
Table 5: Comparison with different cross-modal fusion and gating methods
(a) Cross-modal fusion mechanisms
(b) Gating mechanisms
minilmageNet
CIFAR-FS
minilmageNet
CIFAR-ES
Method
Method
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
SP
77.81
86.01
84.53
88.21
Sum
79.63
86.13
85.43
88.34
SemFew
78.59
86.77
85.82
89.19
MLP
80.05
86.92
85.75
89.43
ECER
79.35
87.12
86.62
90.15
Sigmoid
80.83
87.55
86.27
89.63
81.69
88.25
87.18
90.59
81.69
88 25
87.18
90.59
Comparison with Fusion and Gating Methods. As shown in Table 5a strategies of cross-modal fusion by replacing our RLA module with representative cross-modal fusion FSL methods. The same set of LLM-generated textual semantics is utilized for all methods. SP employs a simple MLP fusion and performs the weakest due to its limited ability to capture semantic relationships. SemFew and ECER enhance the fusion with larger-scale and multi-level MLP, but their static alignment strategy fails to adapt to varying visual hierarchies, resulting in performance that still falls short of our RLA module. Table 55 shows the replacement of our reinforcement learning gating mechanism with alternative gating mechanisms in RLA. Direct summation ignores task-specific relevance, MLP yields static fusion weights, and Sigmoid MLP provides only marginal nonlinearity. In contrast, our reinforcement learning gate dynamically adjusts the contribution of self-attention and cross-modal attention through swardd-gudded exploration, leading to consistent improvements.
compare different
Table 6: Comparison of computational overhead tieredImageNet 1-shot tasks
Method
Parums/GFLOPS
Memory (GB)
Semantics (h)
Training (min)
Inference (ms)
SP Chen et al. 2023
10.8M/ /1.
4.0
27
88
SemFew (Zhang el al 2024a ECER Liu
19.8M /355
8.3
1.5
33
107
26.5M 5
10.5
0.7
46
121
12.87/ / 2.0
4.8
1.0
22
80
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Comparison of computational overhead. The experiment of computational overhead is conducted on tieredImageNet 1-shot tasks with the same server configuration. The comparison with SP (Chen et al., 2023) (without LLM), SemFew (Zhang et al. 2024a), and ECER (Liu et al. 2025) (with I.LMs) shown in Table The semantic construction stage takes 1.0 h, which is comparable to or lower than existing L.M--ased methods. DVLA-RI. achieves the shortest training time (22 min) and the lowest inference latency (80 ms). Relative to ECER, DVLA-RL reduces training time by 52% and inference latency by 34%. Moreover, relative to SemFew, DVLA-RL cuts GPU memory consumption from 8.3 G to 4.8 G and reduces inference time by 25%. the plug-in and lightweight design of DVLA-RL. Texts are generated once offline and used directly in cross-modal fusion, avoiding the extra LLM-based pretraining in ECER. During training/inference DVLA-RL employs only a lightweight RL gating for fusion rather than the larger MLP module with 7.3M parameters in SemFew.
25%.Thes. These advantages arise from
5-way 1-shot setting
5-way 1-shot setting
88
w/o Too-
87
Naliv Top-
86
Ours
86
82
80
78
minimacshat
80
74
HorodimageNer
79
misinageeee
MerealmageWer
CHRAPIF
72
10
Top-k
Figure 2: The effect of different selection strategies and Top-k number.
The Effect of Top-k in DSC. As shown in Figure strategies. It can be observed that both the naive Top-k and our progressive Top-k strategy outperform the setting without attribute filtering, verifying the necessity of selecting discriminative attributes. Moreover, our progressive strategy consistently achieves the highest accuracy across datasets, showing that iterative template updating helps retain more relevant semantics. We further study the impact of the Top-k number. Performance improves as k increases, and reaches the peak at around k = 5 across datasets, after which accuracy drops when too many attributes are included. This demonstrates that moderate number of attributes provides sufficient semantic richness, while excessive ones introduce noise that harms generalization.
first compare different attribute selection
Baseline
test, minilmageNet
test, CIFAR-FS
test, Dogs
test, Places
Ours
Figure 3: T-SNE visualization on novel classes from four datasets.

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T-SNE Visualization. We present T-SNE (Maaten & Hinton 2008 visualizations of novel classes from minilmageNeet CIFAR-FS, Dogs, and Places in Figure T-SNE, as a low-dimensional projection technique, focuses highlighting differences in overall distribution structures. To more clearly observe the feature distribution effects, 100 support images are randomly sampled from each category. The baseline is constructed by removing our DSC and RLA modules while keeping the same backbone and the same sampled support images to ensure fair comparison. Compared with the baseline, where class clusters are entangled and boundaries are ambiguous, our method produces more compact and well- clusters across all datasets. This indicates that dual-level semantic construction provides complementary local and global guidance, while the RL-gated fusion adaptively aligns rross-modal features, resulting in more discriminative and transferable representations. These visualizations intuitively confirm the effectiveness of DVLA-RL in enhancing class separability under diverse scenarios.
Table 7: Comparison with different LLMs for classification and semantic generation on minilmageNer, CIFAR-FS, and CUB. The average accuracy with 95% confidence interval is reported.
minilmageNet
CIFAR-FS
CUB
Model
Role
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
Qwen2.5-VL-322
Classification
82.41+.87 57
83, 53,22x.n
85.98+.77
86.92+.78
87.85-.89
88.50
GPT-4 turbo
Semantics
81.121
88.911220
86, 72. 40
00.211.00
91.41
94.671.18
GPT-4o
Semantics
82.04+.32 32
87.79
86,61+.41
90.074.30
92.45=.27
94.274.20
Qwen2.5-
Semantics
81.69+.30 36
88,25 28
87. 18= 40
90.59 81
91.93
95.06=.19
The Effect of Different LLMs. Table compares Qwen2.5-VL-32BB, GPT-4 turbo, and GPT-4o on three datasets. To ensure fairness, the direct classification evaluation strictly follows the same N-way K-shot evaluation protocol in FSI., where frozen (wven.5--VI--321 receives all support images and the query image via a unified multimodal prompt (Given K support examples for N classes as above, please identify which class the query image belongs to"] and produces a class prediction through generative decoding. However, Awve2..5VVL-322 performs relatively poorly (83.22% on minilmageNet under 5-shot setting). showing the difficulty of adapting LLMs to few-shot recognition. In contrast, using LLMs for semantic generation substantially improves results. GPT-4o excels in the 1-shot setting (92.45% on CUB), while Qwen2.5-VL-32BB leads 5-shot scenarios (95.06% on CUB). These findings highlight that OVLA-RL is robust across different LLMs, and that LLMs are far more effective as semantic generators than direct classifiers.
5 CONCLUSION
In this work, we proposed DVLA-RL, a novel framework for hierarchical and adaptive vision- language alignment in Few-Shot Learning (FSL). The Dual-level Semantic Construction (DSC) module progressively extracts fine-grained attributes and synthesizes coherent class descriptions, offering complementary low-level and high-level semantics. To dynamically integrate these semantics, the RL-gated Attention (RLA) module formulates cross-modal fusion as sequential decision process, adaptively balancing self- and cros-aattnntion between visual and textual tokens with reinforcement learning across network layers. This design enables shallow layers to capture local details and deeper layers model global context, leading more discriminative and generalizable representations. Extensive experiments on nine popular benchmarks across three distinct FSL scenarios demonstrate that our DVLA-RU achieves new state-of-the-arr results.
ACKNOWLEDGMENT
This work was supported in part by the Natural Science Foundation of China under Grant 62176139 and 62406177, in part by the Jinan Science and Technology Project under Grant 202428008, in part by the Shandong Excellent Young Scientists Fund (Oversea) under Grant 2024HWYQ-007,, in part by the Natural Science Foundation of Shandong province, China under Grant ZR2023QF124, in part by the Young Scholars Program of Shandong University.
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ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics Our study does not involve human subjects, sensitive personal data, potentially harmful applications. The proposed method does not introduce risks of misuse, privacy concerns, or discrimination.
REPRODUCIBILITY STATEMENT
We have taken extensive steps to ensure the reproducibility of our work. All datasets used in our experiments are publicly available and referenced in the paper. The implementation details are thoroughly described in the paper. We are committed to ensuring fairness and transparency both experimentation and reporting. An code link of model training and testing will be released after paper acceptance to facilitate reproducibility of the reported results.
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A APPENDIX
LLM USAGE STATEMENT
Large language models (LLMSs) were used in this work assistive tool to improve the clarity and fluency of writing. All technical content, including model development and empirical evaluations, was conceived, implemented, and validated by the authors. The authors take full responsibility for the correctness and integrity of the paper.
A.1 TRAINING ALGORITHM
Algorithm I:
Meta-training algorithm of the proposed DVLA-RL.
Input: Training set DD visual backbone fap text encoder g. LLM Lg. RL policies Jé while converged do
1. Sample an N-way K-shot task T = {S, 2 from D by the episodic strategy; for each class ys in S do
2. Query LLM to extract candidate attributes A ACi.y = {a),...... Us) according to Eq.
Perform progressive Top-k attributes selection for A A as in Eq.
4. Summarize into coherent description D, via LLM as in Eq.
for each support image r S do
5. Extract visual tokens Hine with intermediate layer fo and textual tokens Hạet with
for layer = 1 to L do
Select layer-specific text tokens: attribute for shallow layers, description for deep:
7. Compute image-guiae gagge-guieed// A and text-guided H. followed by fusion H via Eq.
8. Construct state Sy and sample gate a from RL policy To, via Eq.
10:
9. Integrate Hi with H by residual connection according to Eq. 13
10. Compute rewards R₂ and the RL loss LRL according to Eq.
14
10. Construct class prototype C, by averaging support features via Eq. 15 for each query image žj in Q do
11. Compute the prediction scores Pr, between fxp())) and prototypes C2 in Eq. 16
12. Calculate the cross-entropy loss Luup between Pry and the ground-truth yj in Eq. 17
13. Compute the overall loss Lioal Lup + ACRL via Eq. 18
14. Update fop and [0,} } by backpropagation;
Output: Meta-trained backbone fa for testing.
We describe the entire training procedure DVLA-RL in detail in Algorithm T The training process follows an episodic meta-learning paradigm and incorporates two key modules: (1) Dual-level Semantic Construction (DSC), which derives complementary low-level attributes and high-level descriptions from LLMs, enabling both fine-graine' grounding and holistic class understanding and (2) Adaptive RL-gated Attention (RLA), which dynamically fuses image-guided and text- text-guided paths using reinforcement-leatee learned stochastic gates to achieve hierarchical cross-moda alignment. These modules jointly enable the construction of prototypes that integrate dual-level semantics with discriminative visual features. The complete process, including semantic generation, feature extraction, cross-modal fusion, prototype construction, loss computation, and parameter updates, is formalized in Algorithm
A.2 DATASETS
We conduct comprehensive evaluations eight widely adopted few-shot learning benchmarks, encompassing three representative scenarios. For standard few-shot classification, we employ mini- ImageNet, tieredImageNet, and CIFAR-FS. For fine-go inn-ggained few-shot classification, adopt CUB- 200-2011, Stanford-Can and Stanford-Don -Dogs which require recognizing subtle inter-class variations. For cross-domain sho classification, include Places and ChestX, which assess the robustness
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Table The splits of categories and the number of categories/images in each few-shot dataset.
#(Class)
#(Image)
Dataset
D irain
D
Diest
Test
Total
minilmageNet Vinyals al.
2016)
64
16
20
12,000
60,000
fieredImageNer [Ren et al.
2018)
351
97
160
206,209
779,165
CIFAR-FS (Lee et al. 2019)
64
16
20
12,000
60,000
CUB-200-201 Wah et al.
2011)
100
50
50
2,958
11,788
Stanford-Can Krause et
2013
130
17
49
4,103
16,185
Stanford-Doge (Khosla et al.
201T
70
20
30
5,115
20,580
Places (hou et al. (017)
183
91
18,200
73,000
ChestX (Wang et al.
2017
/
7
25,847
25,847
of models under significant distribution shifts. The detailed category splits and image statistics of all datasets are provided Table
Standard Few-Shot Classification. minilmageNet is subset of ILSVRC-12 containing 100 categories with 600 images per class. We use 64 classes for training. 16 for validation, and 20 for testing. IieredImageNet is larger subset of ILSVRC-12, organized into high-level categories to reduce semantic overlap between training and test classes. It consists of 608 classes in total, with 351/97/160 classes for rrinning/vaidaaion//esing respectively, and over 770,000 images. CIFAR-FS is derived from CIFAR-100 and contains 100 classes with 600 images per class. The standard split includes 64 training classes, 16 validation classes, and 20 test classes, each with lower-resolution images (32x32), providing a more challenging setting.
Fine-Grained Few-Shot Classification. CUB-200-2011 (CUB) is fine- Ane-grained bird species dataset with 200 categories and 11,788 images. We follow the split of 100/50/50 classes for training/- validation/testingg. Stanford-Cars contains 196 car categories with 16,185 images, covering different models and production years. We adopt the split of 130 training classes, 17 validation classes, and 49 test classes. Stanford-Dogs a fine-grained dog breed recognition dataset with 120 categories and 20,580 images. We split the classes into 70/20/30 for training/validatiinntttnngg
Cross-Domain Few-Shot Classification. Places a large-scale scene recognition dataset. We follow the split of 183 training classes, 91 validation classes, and 91 test classes, with 73,000 images in total. This dataset evaluates the ability to generalize from object-centric training sets to scene- centric categories. ChestX is medical imaging dataset with 25,847 chest X-ray images across 7 categories. Unlike natural images, ChestX poses significant domain shift challenges, making it a rigorous benchmark for testing cross-domain generalization.
Table 9: Performance comparison with different FSL branch on minilmageNet 1-shot tasks.
Backbone
Params (M)
Strategy
Ace (%)
Training (min)
Memory (GB)
Visformer-T
10.0 86.7
Meta-tuning LoRA
81.69
1.2
4.3
CLIP ViT-B/16
83.10
11.6
23.9
A.3 COMPARISON WITH CLIP-BASED METHODS
The CLIP-based FSL line focuses on adapting powerful vision-language model pretrained on hundreds of millions of image-text pairs, typically through prompt tuning (e.g., CoCoOp [1]) or lightweight adapters (e.g., LDC [2]). In these approaches, the CLIP backbone is fixed or lightly updated, and the performance gains largely stem from leveraging the extensive semantic prior encoded in large-scale CLIP pretraining. Additionally, their tuning scale and procedure closely resemble standard ImageNet-Ie Ik classification. episodic FSL paradigm. The backbone is trained from scratch on scay-scale few-shot datasets such as minilmageNet, where training and test classes are strictly disjoint. As a result, the goals, assumptions, and data regimes of the two FSL settings differ fundamentally.
contrast, the proposed method follows the standard
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Additionally, the proposed DVLA-RL framework is applied CLIP vision encoder. After semanties are generated offline, DVLA-RL performs cross-modal alignment during visual feature extraction. The CLIP vision encoder processes visual tokens at each Transformer layer that structurally consistent with our ViT-hased backbones. Therefore, DVLA-RI can be seamlessly integrated without architectural modification. To further verify this, an additional experiment is conducted on minilmageNet 1-shot tasks using CLIP ViT-B/16 vision encoder. As shown in the Table below, DVLA-RL yields consistent improvements (e.g., +1.4% with LoRA tuning), confirming that the proposed framework can also enhance CLIP- based few-shot learners. However, CLIP--ased training results in an increase of over five times in both time and memory usage due to the large-scale CLIP Transformer, introducing significantly higher computational overhead. This goes beyond the scope of the traditional FSL setting.
Table 10: Ablation study of different semantics.
minilmageNet
CIFAR-FS
Semantics
1-shot
S-shot
1-shot
5-shot
Name CChen ct al 2023
78.96 = 0.31
85.66 + 0.27
85.31 + 0.41
88.31 1 0,28
Definition ZZhang et a 20
80.66 0.32
87.09 +: 0.26
86.02 0.39
90.08 + 0.30
Attribute Liu 2025
81.01
0.30
86.81 + 0.26
86.53 0.400
89.71 1 0.29
Ours
81.69 1 0.36
88.25 + 0.28
87.18 1 0.40
90.59 + 0.31
A.4 MORE DISCUSSION ON VISION-LANGUAGE ALIGNMENT
Prior semantic- FSL methods typically fuse text only at the classifier or prototype layers without cross-modal interaction during feature extraction, resulting in largely unaligned features between modalities. Even recent works that introduce more text (e.g., attributes (Liu al., (025).
or descriptions (Zhang et al.) (024a)) into intermediate layers (Chen et al.) 2023 Liu et al. 2025 Zhang et al., 2024a) still (i) rely on single-level semantics and (ii) apply static MLP fusion across all depths. This static and layer-agnostic design fails to account for the hierarchical nature of visual features where shallow layers capture fine local details while deeper layers focus on global abstract
context, resulting in weak cross-modal alignment.
In the DVLA-RL, structured textual semantics act as an inductive prior that constrains the visual feature extractor at the appropriate level. Fine-grained attributes correspond naturally to low-level patterns (c.g., texture, color, local shape), whereas holistic descriptions correspond to high-level conceptual context. Introducing semanties aligned with the visual hierarchy provides the model with an additional structural prior, enabling each layer to attend to semantically meaningful visual evidence. To achieve adaptive cross-modke alignment across network layers, the RLA module formulates cross moosl fusion as a sequential decision process, allowing the model to dynamically emphasize self-attention or cross-attention based on the layer's semantic relevance. As a result, shallow layers capture fine local details while deeper layers focus on global abstract context, enabling more precise tross-modal alignment.
The empirical results in the manuscript further support this observation. As shown in Table 4 below, a single-layer semantics of either attributes or descriptions yields limited benefits. Furthermore, shown in Table 5a below, static MLP-based fusion in SP, SemFew, and ECER with identical text consistently underperforms. In contrast, the best results of our DVLA-RE demonstrate that dual-level semantic structure and adaptive mross-modal alignment crucial for effectively bridging the modality gap.
A.5 MORE DISCUSSION ON LLM GENERATION QUALITY
The proposed DVLA-RL is designed to effectively address the correctness of L.M --geerrreteeed se- mantics. First, DSC conditions the LLM on both class names and support images to generate more precise and visually grounded semantics, rather than on class names alone as in prior works. This eliminates the common failure mode where the LLM imagines text inconsistent with the actual image object. typically requiring expensive manual (Zhang et al.) 2024a) or LLM-based pretraining (Liu) ctt al., 2025) corrections. Second, DSC is designed to be tolor-tolerant. The progressive Top-k filtering stage explicitly removes noisy inconsistent attributes. The subsequent abstraction step
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Table 1I: Radiologist verification of representative ALM-generated attributes on four ChestX classes.
Class
Attributes (abbrev.)
Expert evaluation
Discriminativeness Moderate (can still overlap with effusion or consolidation).
Atelectasis
Reduced lung volume; mediastinal shift toward the lesion; elevated ipsilateral di- aphragm: displaced or stretched fissure; focal increased opacity.
All attributes are textbook radiographic signs of atelectasis and clinically valid. All findings are ac- curate for pleural ef- fusion; no incorrect fabricated concepts observed.
Effusion
Blunting of the costophrenic angle; meniscus-shaped fluid level; homoge- neous increased density in the lower lung field; diaphragmatic elevation; possible contralateral mediastinal shift.
Moderate (correct but sometimes subtle on frontal chest X-rays).
Infiltration
Patchy or ill-defined opacities; blurred lesion margins; asymmetric density changes; air bronchogram sign; possible rapid temporal progression absorption.
Consistent with stan- dard descriptions of pulmonary infiltration; hallucinated termi- nology.
Low (semantic pat- terns shared with pneumonia, edema other parenchy- mal diseases).
Nodule
Lesion < cm; smooth or spiculated mar- gins; benign malignant calcification patterns; solitary or multiple distribution; peripheral predominance.
All attributes match clinical diagnostic cri- teria; hallucination detected.
Low (substantial over- lap with mass, granu- lomas, and other focal lesions).
Pneumothorax
Sharp pleural line; absence of lung mark- ings beyond the line; increased translu- cency of the affected side; mediastinal shift in tension pneumothorax; ipsilateral diaphragmatic depression or flattening.
All attributes are spe- cific and correct for pneumothorax; con- firmed by the radiolo- gist.
High (most dis- tinctive semantic pattern among ChestX
classes).
further consolidates the remaining semantics into a coherent class-level description, making the representation less sensitive to isolated attribute errors. Third, the RLA is the adaptive alignment mechanism. The RL gate dynamically reduces unreliable textual tokens and relies more heavily on visual evidence, further preventing incorrect semantics from dominating the fusion process.
These robustness properties are validated through empirical studies in the manuscript. (1) As shown in Fig removing Top-k filtering introduces significant semantic noise and notably degrades perfor- mance. (2) Table further shows that DSC exhibits robustness across different LLMs, consistently achieving superior performance. In addition, we compare different forms of textual semantics under the same setting as shown Table TO Class name-based semantics yield the lowest performance due minimal semantic guidance. SemFew definitions and ECER attributes offer richer coarse and fine-ge anne-granedd semantics but still suffer from lack of visual grounding and semantic structure. In contrast, the strong results of DSC confirm the effectiveness of the prevention and the mitigation of semantic errors.
Table 12: Ablation the Beta concentration x and RL weight À
(a) Beta concentration
(b) RL weight À
minilmageNet
Iiaged
minilmageNet
IieredImageNet
Value
Value
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
I-shot
5-shot
5
81.69
88.25
83,02
91.71
0.1
81.69
88.25
83,02
91.71
10
81.77
88.17
82.91
91.63
0.3
81.44
88.02
82.81
91.51
15
81.63
88.19
82.96
91.68
0.5
81.32
88.09
82.50
91.46
20
81.58
88.14
82.89
91.76
81.08
89.78
82.41
91.30
A.6 HYPERPARAMETER ANALYSIS IN RLA
To fully explore tuning ranges and sensitivity of heypppaarameersss in RLA, we additionally performed complete ablation over Beta concentration к and RL weight A. For the Beta concentration. we evaluate {5. 10, 15. 20), and the results show that performance variations remain within 0.2%, indicating that DVLA-RL is highly insensitive to k. For the RL weight, we sweep À E {0.1, 0.3, 0.5, 1.0); 0.1 yields the best accuracy, while larger values slightly degrade performance because the RI.
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reward begins dominate the supervised alignment signal. These results confirm that DVLA-RL is robust to both hyperparameters and does not rely on delicate tuning.
A.7 MORE DISCUSSION ON
CHESTX
To assess whether the limited gains on ChestX arise from hallucinated semantics, additional radiologist audit of the LLM-generated attributes is conducted as shown in the Table below. The expert confirmed that all attributes correspond clinically accurate radiographic signs, with no fabricated or incorrect concepts. Therefore, the factor that truly limits performance on ChestX not semantic hallucination, but the intrinsic characteristics of thoracic imaging and the severe cross-domain shift. Many ChestX categories (e.g., effusion, consolidation, infiltration, edema) exhibit highly overlapping radiographic manifestations, which inherently restrict the discriminative power that semantic cues can provide. Moreover, chest X-rays are grayscale, low-texture, and anatomy- constrained, differing substantially from natural-image distributions used during training. As a result, although DVLA-RL produces clinically correct semantics, the separable semantic space in this domain is much narrower than in datasets such as CUB or Places.
Despite these challenges, DVLA-RL still achieves state-of-the-art results ChestX, obtaining 23.47% (1-shot) and 26.94% (5-shot), which exceed the competitive MEFP method [1] by 0.7% and 0.4%, respectively. This indicates that DVLA-RL avoids the negative transfer commonly observed in medical cross-domain settings and remains one of the most robust approaches even without any medical-domain pretraining.
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Reviews:
[{"review_id": "XVPAk4szuz", "reviewer": "Reviewer_jSnj", "summary": "This paper proposes a few-shot learning (FSL) method DVLA-RL to conduct controlled cross-modal alignment between vision and language from low-level to high-level semantics. In details, Dual-level Semantic Construction (DSC) generates details description of visual attributes via LLMs and Adaptive RL-gated Attention (RLA) that balances the self-attention and cross-attention operation between visual and text tokens. The proposed method achieves strong performance in general FSL, fine-grained FSL, and cross-domain FSL tasks.", "questions": "- In recent years, another line of works focus on enhancing few-shot learning performance on CLIP vision encoder [R1,R2]. Can the proposed method also be applied on CLIP vision encoder? Additionally, it would be nice if the author could discuss the difference between the two few-shot learning settings. \n\nRefs:\n\nR1. Learning to prompt for vision language models. IJCV 2022\n\nR2. Logits deconfusion with clip for few-shot learning. CVPR 2025\n- In table 6, the author compares the alternative of using Qwen2.5-VL-32B for direct classification. Can the author elaborate more on the implementation details of the experiment?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The idea of conducting both low-level and high-level semantics alignment is well-motivated, and the paper is clearly written.\n- The proposed method achieves strong results on several FSL tasks and is supported by several analytical experiments.", "weaknesses": "- The training efficiency of the method could be a major problem due to the time-costly LLM inference and reinforcement learning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1EaGPcKI5F", "reviewer": "Reviewer_byUR", "summary": "This paper presents Dual-level Vision–Language Alignment with Reinforcement Learning gating (DVLA-RL), which comprises Dual-level Semantic Construction (DSC) and RL-gated Attention (RLA). This framework addresses the issue of neglecting progressive and adaptive alignment between vision and language, ranging from low-level to high-level semantics.", "questions": "see Weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Integrating reinforcement learning and cross-attention mechanisms to enhance the performance of few-shot models sounds good.\n\n2. Experiments have demonstrated that using reinforcement learning can improve few-shot performance.", "weaknesses": "1.The motivation for the proposed method requires further clarification.​\nLimitations of Prior Work: The paper does not sufficiently explain why existing methods for vision-language alignment are limited. Given that visual and textual data are inherently different modalities with a fundamental modality gap, the justification for why simply adding more textual attribute descriptions effectively bridges this gap is unclear.\n​​Reliance on LLM Correctness:​A critical concern is the dependence on the correctness of attribute descriptions generated by the LLM. The paper lacks a discussion on how the accuracy of these descriptions is ensured. What is the impact on the model's performance if the LLM generates hallucinated, biased, or incorrect attributes? Without addressing the robustness to potential errors in the semantic input, the claim of obtaining high-quality advanced feature representations may be undermined.\n\n2. Ambiguity in Experimental Setup and Questions on Visualization​​\nThe experimental details for the T-SNE visualization are unclear, leading to questions about the validity of the results.\n​​Missing Experimental Conditions: The specific baseline model and the number of samples used to generate the T-SNE plots in Figure 3 are not specified. This omission makes it difficult to interpret the visualization accurately and assess the comparative improvement.\n​​Inconsistent Performance Portrayal: The quantitative results in Table 1 show a performance improvement of approximately 2% over the strong baseline SemFew. However, the T-SNE visualization appears to depict a near-perfect (100%) separation of classes, which seems to suggest an improvement far exceeding what the quantitative results indicate. This significant discrepancy between the quantitative metrics and the qualitative visualization raises questions about the authenticity and representativeness of the presented T-SNE results.\n\n3.The definition of the reward function Rt in Equation (7) is somewhat ambiguous. The term \"episodic accuracy improvement\" requires a more precise explanation. Does it refer to the accuracy improvement compared to a baseline model, or does it denote the change in accuracy during the training process within the same episode? A clearer definition is necessary to understand how the reinforcement learning signal is constructed.\n\n4.For the comparative experiments presented in Table 5, could the authors confirm that the baseline comparisons with methods like SP, SemFew, and ECER were conducted under identical experimental settings? This includes using the same visual backbone, pre-training data, and Large Language Model (LLM). If DVLA-RL employs a more powerful LLM (e.g., Qwen2.5-VL-32B) or a different pre-training strategy, a portion of the performance gain might be attributed to these factors rather than the core proposed modules. It would strengthen the claims if the comparisons were ensured to be fair and controlled.\n\n5.While the paper emphasizes the performance improvements, it does not discuss the computational overhead introduced by DVLA-RL. The Dual-level Semantic Construction (DSC) module requires calls to an LLM for generating attributes and descriptions, and the RL-gated Attention (RLA) module involves reinforcement learning training. It would be valuable to analyze whether these components significantly increase the training and inference time compared to baseline methods. A discussion on parameter efficiency and computational cost would provide a more comprehensive view of the method's practicality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ivvyvcTLkz", "reviewer": "Reviewer_pD94", "summary": "The paper proposes DVLA-RL, a framework for few-shot learning (FSL) that integrates large language models (LLMs) for hierarchical vision–language alignment. It introduces two main components:\n1. Dual-level Semantic Construction : generates low-level attributes and high-level class descriptions from class names and support samples, using progressive Top-k filtering to avoid irrelevant or hallucinatory attributes.\n2. Adaptive RL-Gated Attention : formulates cross-modal fusion as a reinforcement learning (RL) process that dynamically balances self-attention and cross-attention between visual and textual tokens across layers.\nDVLA-RL achieves state-of-the-art results across nine benchmarks in three FSL scenarios.", "questions": "1. How are the two attention paths in RLA (Eq. 5–6) distinct if both use text queries? Could the authors clarify it?\n2. What are the values and tuning ranges for the Beta concentration κ and RL weights λ?\n3. Why does ChestX show minimal improvement, are there examples of failed attribute generation or hallucinated semantics?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Clear motivation and design: The dual-level semantic structure effectively bridges fine-grained and global representations, addressing the limitations of single-level methods like SemFew or ECER.\n2. Novel adaptive mechanism: The RL-gated attention introduces a lightweight yet dynamic policy to control fusion between self- and cross-attention, enabling layer-wise semantic alignment.\n3. Strong results: DVLA-RL achieves SOTA across all nine datasets, with significant gains.", "weaknesses": "1. The paper describes RLA as containing both “image-guided” and “text-guided” paths, yet both equations use text-based queries while only differing in key and value sources. The notion of “image-guided” does not match the presented formulation, leaving the two paths conceptually indistinguishable.\n2. The output fusion step is written as adding the fused representation to image features and then concatenating, but the dimensional correspondence between the two is not specified. If text and image features differ in length or channels, the operation is not well-defined. The formulation leaves the dimensional alignment unclear.\n2. Minor writing inconsistencies: Mixing of terms “RLA/ARL”, and unclear notation (e.g., U, t*) reduce readability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)