Part-level Semantic-guided Contrastive Learning for Fine-grained Visual Classification
Authors: Zhijian Lin
Hong Han
Venue: ICLR 2026 Poster (2026)
Keywords: Fine-grained Classification
Attention
Location
Scale
Vision-Language Learning
Contrastive Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Fine-Grained Visual Classification (FGVC) aims to distinguish visually similar subcategories within a broad category, and poses significant challenges due to subtle inter-class differences, large intra-class variations, and data scarcity. Existing methods often struggle to effectively capture both part-level detail and spatial relational features, particularly across rigid and non-rigid object categories. To address these issues, we propose Part-level Semantic-guided Contrastive Learning (PSCL), a novel framework that integrates three key components. (1) The Part Localization Module (PLM) leverages clearCLIP to enable text-controllable region selection, achieving decoupled and semantically guided spatial feature extraction. (2) The Multi-scale Multi-part Branch Progressive Reasoning (MMBPR) module captures discriminative features across multiple parts and scales, while reducing inter-branch redundancy. (3) The Visual-Language Contrastive Learning based on Multi-grained Text Features (VLCL-MG) module introduces intermediate-granularity category concepts to improve feature alignment and inter-class separability. Extensive experiments on five publicly available FGVC datasets demonstrate the superior performance and generalization ability of PSCL, validating the effectiveness of its modular design and the synergy between vision and language. Code is available at: https://github.com/joker-lin9/PSCL

Published as a conference paper at ICLR 2026
PART-LEVEL SEMANTIC-GUIDED CONTRASTIVE LEARNING FOR FINE-GRAINED VISUAL CLASSIFI-
CATION
Zhijian Lin
Hong Han
Guangzhou Institute of Technology
School of Electronic Engineering
Xidian University
Xidian University
Guangzhou, China
Xi'an, China
230212112528st wwt.ess.tst.iiiiinn.ne..a.m. edu. cn
hanh@mail xidixiiin aaiianeedu cn
ABSTRACT
Fine-Grained Visual Classification (FGVC) aims to distinguish visually similar subcategories within a broad category, and poses sigmificant challenges due to subtle inter-class differences, large intra-class variations, and data scarcity. Exist- ing methods often struggle to effectively capture both part-level detail and spatial relational features, particularly across rigid and non-rigid object categories. To address these issues, we propose Part-level Semantic-gumt (PSCL), a novel framework that integrates three key components. (1) The Part Lo- calization Module (PLM) leverages clearCLIP to enable text-controllable region selection. achieving decoupled and semantically guided spatial feature extraction. (2) The Multi-scale Multi-part Branch Progressive Reasoning (MMBPR) module captures discriminative features across multiple parts and scales, while reducing inter-branch redundancy. (3) The Visual-Language Contrastive Learning based on Multi-grained Text Features (VLCL-MG) module introduces intermediate- granularity category concepts to improve feature alignment and inter-class sep- arability. Extensive experiments on five publicly available FGVC datasets demon- strate the superior performance and generalization ability of PSCL, validating the effectiveness of its modular design and the synergy between vision and language. Code is available https:/githua.ccm//kkkll///////
Contrastive Learning
INTRODUCTION
Fine-Grained Visual Classification (FGVC) aims to accurately distinguish between subcategories that belong to the same high-level category yet exhibit subtle visual differences. Typical applica- tions include the classification of bird species (Wah et al. 2011 Van Horn et al., 2015). car brands (Krause et al. 2013), and aircraft (Maji al. 2013) models. As FGVC focuses fine-level dis- tinctions within specific domains, it has demonstrated unique practical malue-distinel from general visual classification tasks-in areas such intelligent transportation, medical image analysis, and ecological environment monitoring. However, FGVC remains a challenging task due to factors such as low inter-class variance, high intra-class variance, a large number of categories, and data scarcity.
We observe that existing models exhibit notable feature preferences when processing rigid and non- rigid objects. We argue that FGVC tasks require the modeling of two key types of features: (I) part- level fine-grained features that capture detailed local differences and (2) spatial relational features that describe inter-class differences in spatial structure. For rigid objects, inter-class differentiation often affected by external factors such viewpoint variation and occlusion. In contrast. non- rigid objects tend to exhibit more significant posture variations, leading to greater uncertainty in their spatial structural features. Different model architectures vary considerably in their capacity to capture these two types of features.
Some existing works have consciously incorporated mechanisms for modeling spatial structural fea- tures. For example, CAP (Behera et al. 2021) captures spatial relations through region consistency integration, while SFETrans Yuu al." 2025 extracts spatial features via phase spectrum analysis.

---

Published as a conference paper at ICLR 2026
(II)
(IV)
Two types of critical features in rigid and non-rigid objects. (l) Spatial deformation in non-rigid objects (e.g., birds) due to articulated motion; (II) Diverse part-level details in non-rigid objects; (III) Stable spatial structure in rigid objects (e.g., airplanes); (IV) Consistent part-level appearance in rigid objects.
Figure 1:
These methods have demonstrated effectiveness in improving classification performance for rigid objects. However, the core objective of FGVC lies in accurately modeling subtle inter-class dif- ferences. Since spatial relational features often rely on matching shared regions across categories, they may conflict with the precise representation of part-level details-partee paclllsl-prccllllyyy for non-rigid objects-potentially weakening the model's ability to focus on critical parts. Furthermore, current models generally adopt unified strategy for designing part-based branches across all categories, overlooking the homogeneity of part-level details among similar categories. This can lead to mis- classifications and redundant representations across branches.
To address these issues, we propose a novel framework called Part-level Semantic-guided Con- trastive Learning (PSCL). This model introduces Part Localization Module (PLM). which lever- ages clearCLIP (Lan et al. 2024) as an auxiliary component to enable text-guided region selection, therehy achieving effective decoupling of feature region selection and feature representation. Ad- ditionally, we design a Multi-scale Multi-part Branch Progressive Reasoning (MMBPR) module, where part-based branches represent fine-graine features of individual parts, while global branch adaptively integrates features based on spatial relations. Through progressive reasoning, MMBPR enables each branch refine its feature representations across multiple scales.
During the multi-scale feature fusion stage, we further design the Reverse-key Scale-aware Attention Fusion Module (ReSAF) suppress the influence of high-level features on semantically similar regions at lower levels, thereby encouraging the model to extract information from less similar areas. This effectively mitigates feature redundancy among branches.
Finally, in the classification phase, we introduce novel Visual-Language Contrastive Learning based on Multi-grained Text Features (VLCL-MG) module. By incorporating intermediate- category concepts, this module leverages prior knowledge to aggregate fine-grained categories into semantically coherent mid-level groups, promoting more meaningful clustering of similar subcate- gories in the feature space.
Our main contributions can be summarized as follows:
We propose Part Localization Module (PLM) that enables text-controllable spatial feature extraction via clcarCLIP:
We design Multi-scale Multi-part Branch Progressive Reasoning (MMBPR) module to reduce feature redundancy and enhance part-level and global representations;
We introduce Visual-Language Contrastive Learning module based on Multi-grained Text Features (VICCI-MG) to improve the semantic alignment of visually similar subcategories;
Extensive experiments on five publicly available FGVC datasets validate the effectiveness and generalization ability of our proposed PSCI. framework.
2

---

Published as a conference paper at ICLR 2026
2
RELATED WORK
2.1 FINE-GRAINED VISUAL CLASSIFICATION
Fine- tene-grained visual classification (FGVC) methods primarily focus on capturing subtle inter-class differences through refined feature representation and part localization. Early feature representation approaches relied on high-level features (Lin et al. 2015 Zheng et al. [011 Sun et al. 2020), later incorporating multi-scale fusion techniques such AP-CNN (Ding et al. (2221) and PMG (Du et al, 2020 as well as autention-based mechanisms like MA-CNN (Zheng et al. 2017), OSME (Sun et al.
2018), and Transformer- based methods such TransFG (He et al. (2022) and CAMF (Miao al.,
20211. MDCM (Zhang et al., 2025) introduces a multi-scale ViT framework that improves fine- grained bird recognition by activating, selecting, and aggregating discriminative cues across scales. Part localization methods identify discriminative regions through cropping and scaling strategies. This line of work aims to locate category-relevant regions within the input image by analyzing attention maps generated by the backbone network. The identified regions are then cropped and reprocessed to retain high-resolution, fine-grained details that are critical for elassification. This strategy explicitly extracts spatial structural features by emphasizing salient parts, often leading to superior classification performance. While early approaches like Part-based R-CNN (Zhang et al.] 2014) and Pose Normalized CNN (Branson al. (014)) relied on strong supervision, recent methods have shifted to weak supervision for better scalability. Notable examples include MGE-CNN (Zhang et al. 2019), P2P-Net Yang et al., 2022), CAP (Behera al., 2021), TBMSL-Net (Zhang al., 2021 and PART (Zhao et al.) ), which explore part-level semantics via multi-scale learning, Transformer-basee architectures. CSQA-Net (Xu al., (025)) introduces
context modeling, Part Navigator module to assign saliency scores to different image regions, enabling discriminative region segmentation without strong part annotations.
2.2 VISION-LANGUAG LEARNING
Vision-language models (VLMs), particularly CLIP (Radford al. 2021), have demonstrated strong potential in open-vocabulary tasks by learning joint representations from large-scale image-text pairs. While early FGVC-related works using CLIP (Li et al.) 2023; Wang et al. 2023b) empha- sized alignment between descriptive text and novel categories, MP-FGVC (Jiang al. 2024) intro- duced CLIP to closed-set FGVC by leveraging multimodal prompts to enhance category discrimi- nation. For region-level tasks, CLIP's utility has been extended to apen-vocabulary segmentation. MaskCLIP (Zhou et al., 2022) revealed that dense patch-level features from CLIP's attention layers could be aligned with textual representations. Building on this, ClearCLIP (Lan et al., 2024) demon- strates that by removing residual connections in CLIP, enabling self-attention, and eliminating the feed-forward network, open-vocabulary semantic segmentation can be achieved directly without ad- ditional training. We empirically demonstrate that ClearCIII is also effective for part-level semantic concepts.
APPROACH
The proposed PSCL architecture is illustrated in fig. 2 In the visual pathway, the input image is first processed separately by the backbone and ClearCLIP. ClearCLIP generates part masks by com- puting matching scores and applying channel selection, while the backbone produces multi-scale features. For single-scale backbones such as ViT, these features can be regarded as multi-level representations extracted from different transformer layers, which serves an equivalent role in our framework and does not affect the overall conclusion. The two outputs are combined using the Hadamard product to obtain multi-scale lert-level features, forming the Part Localization Module. The designed Multi-scale Multi-part Branch Progressive Reasoning module processes the resulting visual features, progressively enhancing the model's confidence in its predictions from low-level to high-level features. This confidence enhancement is achieved through combination of hyperparam- eters for contrastive loss weights across different scales and noise parameters. In the text pathway, contrastive loss leverages intermediate- graaiind textual priors input, generating multi- -rained tex- tual features for different categories. These features are then rearranged and restructured to produce multi-grained textual representations for each ine-grained label.
3

---

Published as a conference paper at ICLR 2026
aediphh meea
Part Localization Modale
Multi-scale Multi-part Branch Progressive Reasening
Batheandd produet
mribbee
Enonder Lamr
ROSAF
Saadlo Laye
Enralo Eayrs
Clas Embodiing
Eearaoble Parsoneter
Eeput truge
Backlone
Inferance
Char CLLP
Laelle Dver
Magg
Part Test
Eight
Staps
Tinegraleed
Coanegrainel
Vision- LLsgunLLaggune Contrastive Learning Module based on Multi-grained Text Features
Eriheddiigg
Emabedding
Cantractive
Prodiction Confsteace
Figure 2: Detailed illustration of Part-level Semantic-guided Contrastive Learning model (PSCL).
3.1 PART LOCALIZATION MODULE
The proposed Part Localization Module (PLM) is designed address the conflicting requirements of modeling fine mme-grained part-level features and spatial relational features in FGVC. This conflict is particularly pronounced for non-rigid objects, where posture variation undermines the stability of spatial structures and affects precise part representation. To resolve this, PLM processes the input image E RC through two separate branches: one for capturing difference-aware features and the other for localizing discriminative parts, enabling more effective and targeted feature learning across object types.
The branch responsible for representing differences processes the input x to produce multiscale features, with features denoted f. RCxx xW, across multiple stages. When low-level features are less relevant for classification, only higher stages may be selected, such that
{Smins-... ... 4),
Smin 1,
where Smin denotes the earliest stage used, which can be adjusted based on task requirements.
The resulting is then passed into the Reverse-key Scale-aware Atten- tion Fusion Module (ReSAF) sup- press redundant channel representa- tions across scales, as illustrated in fig. In the figure, AAP denotes Adaptive Average Pooling, and the positional encoding is implemented as learnable parameter. By flipping key vector directions, ReSAF inverts similarity scores, guiding high-level queries to attend away from similar low-level regions. This contrastive attention promotes the extraction of complementary.
fie(1...))
ie(1,2,3)
Cross Atteation
Positional Encoding
f4
AAP
MLP
Self Attention
f/
Figure 3: Illustration of ReSAF.
The branch responsible for identifying the parts of interest is built upon the ClearCLIP backbone. The input image is encoded by the image encoder fung, producing patch-level image features F img Fing (x) E IR H T T2, } are processed by the text encoder fiess yielding part-specific text feature representations Fieu = (fieu (Ti), (Te) (ex((( E R.Nxd To align image patches with text descriptions, the similarity tensor S is computed via matrix multiplication.
Similarly, the textual prompts corresponding N parts, denoted as
Fing F S X

---

Published as a conference paper at ICLR 2026
To generate the final part mask M, the indices of the maximum similarity scores across the N channels are first determined as
max_indices = arg max Sli]- je N).
(3)
Using these indices, one-hot-like tensor constructed:
s , max_indices,
0, otherwise.
The one-hot-like tensor undergoes morphological refinement by first applying dilation to expand regions, followed by erosion to refine connectivity and remove noise:
M = (S)
(5)
where denotes the structuring element (kernel), instantiated tion; @ represents the morphological dilation operator; and e denotes the erosion operator.
3 kernel in our implementa-
The multi-scale multi-part features Gs,aa can be expressed as:
Gen concat (f% @ M f. @ 1), (1. {1,2, +
where 3 denotes the Hadamard product, concat(-) denotes the concatenation operation, is matrix of ones, representing the global mask, fn 1, captures the global features. The global features are subsequently processed by the global branch, which adaptively aggregates part-level information according to spatial relationships, aiming model spatial relational features.
3.2 BRANCH PROGRESSIVE REASONING
Our proposed Multi-scale Multi-part Branch Progressive Reasoning (MMBPR) module extends the
multi-scale reasoning framework introduced by PMG (Du et al. 2020) and PART (Zhao al. 202T). It progressively enhances supervision from low-level to high-level feature branches. In addition to multi-scale progressive reasoning, the framework integrates part-level branches for fine-grained lo- cal modeling and global branch for capturing spatial relational representations via part aggregation.
Although multi- muulti-part-brrg part-branch designs have become standard in FGVC, most existing methods rely on shared feature extractor for both localization and classification, resulting in weakly grounded parts biased toward classification objectives. For example, PART (Zhao al. [02I]) selects regions via class activation maps and saliency ranking, which does not ensure consistent semantic corre- spondence across samples. Under occlusion or pose variation, a branch may attend to different components across images, leading to redundancy and unstable learning.
In contrast, PLM leverages text prompts and ClearCLIP- localization to achieve semantically grounded and controllable part selection, where each branch corresponds to a predefined part. By decoupling region selection from representation learning, and deactivating occluded parts via the mask formulation in eq. (6). PLM maintains stable optimization.
Following the ViT architecture, we adopt Class Embedding Learnable Parameters to extract category-specific visual representations. We utilize three class tokens comprehensively represent intermediate categories.
The overall input to MMBPR is G.,n' obtained from eq. (6), and the progressive reasoning process begins from the lowest-level features Goa.nn,, n', as formulated below:
flatten
(Vomin' m m E (1, p2
Spis
The flattened tokens are concatenated with class tokens:
Z Z concat( (Cels m Cets 22,2 Cele, .), [V Ve V Ve where n' (j (,)) are class tokens.
(8)
The sequence Z is then processed through an Encoder Layer:
Z' Z' nn' = LLL(N ( + MHSA A mm
(9)
Z D' LN "mn..' + MLP(Z' smm
5

---

Published as a conference paper at ICLR 2026
where LN(-) is Layer Nonmalization, MHSA(-) is Multi-Head Self-Attention, MLP(-) ) is feed- forward neural network, and the model weights are not shared across layers. The resulting output is:
Ci Te mf = [Cel..em nn,1; ela nn 2: Cols, Smi 31' smmm nr
Vir
(10)
The output 2mmn.. is then divided into two parts: Class tokens, representing the visual features of the categories at the stage I. eCsss sainnn ,1 C Cesssmmnnnn 22 C Cls, "cis,sii. 33 are passed to VICL-MG for contrastive learning. Feature tokens "mi.n'.1i... Vi where they are concatenated with the flattened high-level features.
Smin. nt ,pa] pe] are forwarded to the next stage,
By ensuring non-interference between the lower and higher branches, this design enables the higher- level feature branch to acquire stronger discriminative capabilities, thereby leading to more confident category-speciffe visual representations.
The process is recursively applied to the next stage:
Buii 1. = concat((Caka (Cess 1,,1,11 Cets smis n/ Cels, smin 11 ,3);
(II)
(Veii ,13
V Vemo+1, _P2] [V' suin n111 .i:...;.. V
41 pal).
This procedure is iteratively applied until the highest-level feature branch completes its reasoning process, and the reasoning is conducted in both the global branch and the part-level branches.
3.3 VISION-LANGUAGE CONTRASTIVE LEARNING
Our proposed Vision-Langual Contrastive Learning Module based on Multi-grained Text Features (VLCL-MG) constrains the inter-class differences of visual features to align with real-world distinc- tions by introducing intermediate category constraints, which are primarily implemented through the model structure.
Additionally, terms of loss computation, since FGVC tasks involve highly similar subcategories, argue that absolute model outputs unnecessarily enlarge inter-class distances. If the top- scoring category is incorrect, the actual category score may rank lower due to small score differences among other categories. To mitigate this, employ label smoothing (Szegedy regularization. Meanwhile, given the limited number samples, models are prone to overfitting, making it crucial to learn more from hard-to-classify samples. Therefore, focal loss (Lin al. 2017) is also essential. Based on these considerations, we propose the Focal-Smooth Contrastive Loss as complement our model structure.
al., 2016 for
Specifically, we first obtain intermediate categories for fine-grained labels. For example, between the coarse-grained category airplane and the fine-grained category Boeing 737-200, inter- mediate categories include narrow bodyy airliner and twinjet. This expert knowledge can be efficiently obtained with minimal effort, requiring only one-time retrieval per category rather than per-image annotation. For most datasets, we employ ChatGPT-40 for semi-automated knowledge retrieval, whereas for the NABirds dataset, we directly utilize the dataset's built-in class hierarchy.
The multi-grained textual labels corresponding to each tne-grained category are represented
tcls = {an, bng Jnr} E RC,
(12)
where an and bag represent two types of intermediate categories, and for corresponds to fine- grained categories, C' represents the number of fine-grained categories. These labels are processed by the ClearCLIP text encoder, yielding multi-grainee text features:
tp
fwei (tels)
(13)
To prevent text features of all grained levels from clustering too closely in the embedding space, we subtract the coarse- boarse-rrained category feature fiea(c) from the multi -ualtee text features tnp and then apply normalization.
Te morm(t., frea(coarse))
(14)
This operation ensures a more discriminative distribution of text embeddings across different grained levels. To avoid redundant computations, all category label texts first processed by the text

---

Published as a conference paper at ICLR 2026
encoder, and then rearranged according the intermediate-graiin categories corresponding each fine-grained label, as detailed in fig.
The predicted probability distribution P. is obtained by applying softmax normalization over the similarity scores between the linearly projected feature L,,' extracted from eq. (TO) at stage s, and the final-stage prototype representation T uup- To ensure dimensional compatibility, each feature I E Pi is first transformed by a learnable linear projection W E Dr The wliss-wise probability for the n'-th branch defined
P P = a(T ((WI..) T + +)
(15)
where T E RC denotes learnable temperature scaling vector, 3 E RC is a learnable bias term, and (.) represents the softmax function applied over all classes c 1 I.....C.. C.
N+1 The Focal-Smooth Contrastive Loss at stage denoted as FSL, (P s: v). is formulated as:
FS.,(P..)) = E - Paa e)) Venn' ,e log Pa,
(16)
where is the focusing factor that adjusts the impact of misclassified examples. The smoothed target distribution e is given by:
Dan'ie = -1--111-- f1-
i Vam'
(17)
(c((C- otherwise,
where e, gradually decreases the stage index s increases, enabling the MMBPR module gen- erate progressively more confident predictions.
The final loss is defined as the weighted sum of the stage-wise losses FSL, y), where the weight é, increases with the stage index s (serving role analogous to but exhibiting an opposite monotonic trend). Formally,
Linal Σ FSL.
(18)
Suit.
At inference time, the prediction from the final stage utilized, and the inference strategy relies solely on the global branch:
iinference = =P
(19)
in which ClearCLIP and redundant part-level branches are removed during inference, thereby en- abling substantially faster computation.
4 EXPERIMENTS
4.1
EXPERIMENTAL SETUP
Datasets We comprehensively evaluate PSCL.
Table 1: Statistics of benchmark datasets.
on the FGVC Airerafi (Maji al. 2013). Stan- ford Dogs(Khosla et al. 2011). Stanford Cars
Dataset
Class
Train
Test
Krause al.] (013). CUB-200-2011 (Wah
FGVC Aircraft (AIR)
100
6,667
3,333
et al. 2011) and NABirds (Van Horn et al.
Stanford Dogs (DOG)
120
12,000
8,580
2015) datasets, which are widely used FGVC
Stanford Cars (CAR)
196
8,144
8,041
benchmarks. In all experiments, we do not utilize part annotations and follow the same train/test split. The details of the five datasets are presented in table
CUB-200-2011 (CUB)
200
5,994
5,794
NABirds (NAB)
555
23,929
24,633
Implementation Details We adopt ResNet-50 (He al. 2016), Vision Transformer (Dosovitskiy et al. 2021). and Swin Transformer (Liu et al. 2021) as the backbone architectures. The input reso- two input resolutions:
lutions are 448 x 448 for RN50 and 384 x 384 for Swin-B. For ViT-B, 448 x 448 for fair comparison with other methods, and 518 x 518 as the default setting that achieves the best performance. Unless otherwise specified, ViT-B denotes the 518 518 model. The impact
7

---

The best results for Published as a conference paper at ICLR 2026
of different resolutions is shown in appendix H During training, we apply standard data augmen- tation techniques, including random cropping, random erasing, horizontal flipping, Gaussian blur, color jittering, and rotation. All models trained for 100 epochs using the Adam optimizer with a batch size of 16 and a weight decay of 0.01. The initial learning rate is set to x 10-4 for RN50 and x 10 for both ViT-B and Swin-B. A warm-up phase of 10 epochs is applied, and the learning rate follows cosine annealing schedule. The focusing parameter y is set to 4, and the smoothing noise factor e, follows [0.4, 0.3, 0.2, 0.1], while ég [0.1, 0.2, 0.4, 1.0]. The hyperparameter settings are summarized in table
4.2 COMPARISON WITH OTHER METHODS
Table 2:
Performance comparison FGVC benchmark datasets (Accuracy %). each dataset are highlighted in bold.
Method
Backhone
Input Resolution
AIR
CAR
CUB
NAB
DOG
CMN Deng al. 2022
RN50
448x448
93.8
94.9
88.2
87.8
P2P-Net Yang al. 2022
RN50
448x448
94.2
95.4
90.2
GDSMP-Ner Ke el a..
2023
RN50
448x448
94.4
95.3
89.9
89.0
SIA-Nct (Wang et al. 2023c
RN50
448x448
94.3
95.5
90.7
PSCL (ours)
RN50
448x448
95.1
95.6
89.1
89.0
90.1
TransFG (He et al.
2022
ViT-B
448x448
94,8
91.7
90.8
92.3
MpT-Trans Wang
2023a
ViT-B
448x448
92.2
93.8
92.0
91.3
ACC-VIT (Zhang
2024)
VIT-B
448x448
94.9
91.8
91.4
MP-FGVC (Jrang cl al.
2024
ViT-B
448x448
91.8
91.0
91.0
PSCL (ours)
VIT-B
448x448
94.3
95.1
92.2
92.5
91.0
PSCT. (ours)
VIT-B
518x518
96.5
96.4
92.3
93.7
92.3
ViT-NeT (Kim ot al. 2022
Swin-B
224
95.0
91.6
90.9
90.3
TransIFC+ (Luu al 2023
Swin-B
448x446
91.0
90.9
HERBS Chou
Swin-B
384x384
92.3
93.0
CSQA-Net (Xu et al. 2025
Swin-B
448x448
94.7
95.6
92.6
92.3
PSCL (ours)
Swin-B
384x384
95.3
95.5
93.0
93.8
94.7
We evaluate our method on five benchmark datasets using three backbone architectures and com- pare it with state-of-the-art models, as summarized table The results demonstrate the superior performance and strong generalization ability of PSCL across diverse FGVC benchmarks. PSCL consistently achieves state-of-the-an or highly competitive accuracy across datasets and back- bones (RN50, ViT-B, and Swin-B). It delivers substantial improvements under Transformer-B bbasdd backbones, and remains competitive under the CNN-based RN50, particularly on AIR and CAR datasets. These results highlight PSCL's adaptability to different architectures and its effectiveness in capturing both local and structural discriminative cues. Furthermore, its consistent performance across datasets underscores its robustness. Notably, the large-scale NAB dataset, the availabil- ity of an accurate and professionally curated category hierarchy enables precise intermediate-lever grouping, further enhancing accuracy and demonstrating the effectiveness of the VLCL-MG mod- ule. The strong performance on multiple non-rigid datasets such as CUB and NAB demonstrates that PSCL effectively models the characteristies of non-rigid objects.
4.3 EFFECTIVENESS OF MODULE OPERATION
Locating Relevant Parts We resize eq. ( match the original image dimensions for visualiz- ing the part localization results. As observed in fig. the PLM structure effectively identifies the locations of the parts.
Reverse-key Scale-aware Attention Fusion Module To assess the effectiveness of our proposed ReSAF module, we conduct a comparative study with two alternative intermediate mechanisms: multilayer perceptron (MLP) and cross-attention. All experiments are performed using the RN50 backbone on the AIR dataset. The quantitative results, summarized in table demonstrate that Re- SAF consistently outperforms the other two variants, highlighting its superior capability in capturing scale-aware feature interactions. More comparative results and analyses are provided in appendix[]
8

---

Published as a conference paper at ICLR 2026
(IV)
(VI
(VI)
(VIII)
Figure 4: Part score visualization. PLM uses the following textual prompts: (I) mouth; (II) head;
(III) body; (IV) foot; (V) landing gear; (VT) tail; (VII) fuselage; (VIII) engine.
(I) stage
(II) stage 2
(II)) stage? 3
(IV) stage 4
(V) stage
(VI) stage
(VIl) stage
(Vill) stage
Figure 5: Attention maps of ReSAF (I)-IVV show the relative attention among patches, while (V)- (VIII) present the accumulated spatial attention. It can be observed that shallow features primarily serve complementary role for deep features.
Performance comparison of different in- termediate mechanisms on the AIR dataset. Ac- curacy (%) reported.
Table 3:
Hyperparameter Selection All hyperparame- ters except the learning rate searched and selected exclusively on the AIR dataset with RN50. Results are shown in table table Suble and fig. 6 The trends of é, and 7 suggest that pro- gressive inference improves performance, and increasing the focus on hard samples via y fur- ther enhances results. We note that if hyperpa- rameter tuning were performed specifically for target dataset, our proposed PSCL could po- tentially achieve even better performance.
Intermediate Mechanism
Accuracy (%)
MLP
94.71
Pross-Attention
94.99
ReSAF (Ours)
95.14
Ablation Studies The ablation results in ta- ble 6 across RN50, Swin-B, and ViT-B CUB, AIR, and CAR verify the effectiveness of each proposed module. PLM and VLCL- MG individually yield notable gains, reflecting their strengths in part localization and seman- tic alignment. Because both are designed to address the same underlying issue, combining them may show diminishing marginal gains property rooted in the data itself. Fine-grained classes sharing the same intermediate category often exhibit similar part-level structures (e.g., Anser- iformes birds with webbed feet and long necks; off-road vehicles with high chassis and traction- oriented tires), which reduces the additional benefits of stacking PLM and VLCL-MG. MMBPR further improves performance through multi-scale reasoning. Although the modules differ in their roles, all aim enhance semantic consistency, and despite the potential overlap of their effeets in low-redundancy settings, the full model consistently achieves the best results, confirming their overall complementarity.
Accuracy
512 768
1024
Focasing parameter Embedding Dimension
Figure 6: Accuracy (%) on the AIR dataset (RN50) under different settings. Left: focusing parameter Right: encoder hidden dimension.

---

Published as a conference paper at ICLR 2026
Table 4: Accuracy (%) on AIR dataset (RN50)
Table 5: Accuracy (%) AIR dataset (RN50)
under smoothing noise factor Cy-
under multi-scale loss weight coefficient č.-
Accuracy (%)
Accuracy (%)
10.0, 0.0, 0.0, 0.0)
89.92
0.0, 0.0, 1.0
94.65
0.6, 0.4,0.2,000
94.22
0.1 0.1, 0.2, 1.0
94.77
0.7, 0.5, 0.3, 0.1
94.83
0.4, 002,, 0.4,0,
95.14
00.4 0.3, 0.2,
95.14
94.89
Table Ablation study three FGVC datasets using different backbones. Accuracy (%) is reported for each configuration. The best results for each column are highlighted in bold. Features are indicated by check mark (V) or (X).
RN50 AIR
Swin-B
VTT-B AIR
PLM MMBPR VLCL-MG
CUB
CAR
CUB
AIR
CAR
CUB
CAR
X
85.09
91.56
91.90
92.32
94.14
94.86
90.23
94.57
95.60
X
X
88.82
94.54
95.46
92.68
94.60
94.74
91.99
96.13
96.05
/
X
89.09
94.54
95.54
92.51
95.05
95.04
92.34
96.19
96.17
X
X
87.90
94.39
95.32
92.65
94.87
95.51
90.94
95.08
96.26
89.13
95.14
95.59
93.01
95.32
95.54
92.34
96.48
96.44
4.4 TRAINING AND INFERENCE EFFICIENCY
Table 7: Computational cost.
Additional Computational Cost During Training. Although PSCL introduces additional computation, overall cost remains comparable many contemporary Transformer models. Since different backbones have varying hidden dimensions, the projection MLP to the coder space incurs minor differences in FLOPs (within a few tenths of a GFLOP). For consistency, the FL.PSs ported in table 7 assume backbone hidden dimension of 768. Notably, when 8min = 4, ReSAF
Component
GFLOPs
ClearCLIP
17.35
MMBPR
15.72 (N + 1)
ReSAF
5.78
others
Negligible
reduces single MLP mapping backbone features to the encoder dimension.
Inference. At test time, only the global branch is retained, resulting in lightweight pipeline con- sisting of the backbone, one ReSAF module, and three encoder layers. Detailed efficiency statistics are provided in appendix Compared with the backbone alone, PSCI. incurs only modest overhead and remains practical for deployment.
CONCLUSION
We introduce Part-level Semantic-guided Contrastive Learning (PSCL), framework for Fine- Grained Visual Classification that jointly models part-level details and spatial relations for both rigid and non-rigid objects. PSCL employs Part Localization Module (PLM) with ClearCLIP for semantically guided, interpretable part extraction, Multi-scale Multi-part Branch Progressive Reasoning (MMBPR) module to fuse fine-grained and global features, and Visual-Lanm Lasuua-L Con- trastive Learning module with Multi-grained Text Features (VLCL-MG) to align subcategories via intermediate-level semantics. Experiments on five FGVC benchmarks demonstrate PSCL's robust performance and strong generalization.
DISCUSSIONS
Limitations. PSCL has two primary limitations. First, it is fundamentally constrained by task definition. The framework designed for fine-grained recognition within a single coarse-grained domain where semantic parts can be consistently defined. When multiple heterogeneous coarse- grained domains with incompatible morphological structures are involved (e.g.. iNaturalist), con- structing unified part vocabulary becomes infeasible, requiring separate models for different do- mains. Second, PSCL relies on ClearCLIP for peteh-level part localization. When objects or seman- tic parts small, localization becomes sensitive to input resolution and downsampling. Moreover, ambiguous part boundaries may introduce noisy masks, requiring additional processing. more precise open- vocabulaay segmentation model could further improve robustness in such cases.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our work focuses on fine-grained visual classification using publicly available datasets. No hu- man subjects, personally identifiable information, sensitive content are involved. All datasets employed, including AIR, CAR, NAB, DOG, and others, are used strictly for research purposes in accordance with their respective licenses. We acknowledge the potential societal impacts of deploy- ing FGVC models, such as reinforeing biases present in the training data, and emphasize that PSCI. should be applied responsibly, with consideration for fairness and ethical implications.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, provide detailed descriptions of all model components, including the Part Localization Module (PLM), Multi-scale Multi-part Branch Progressive Reasoning (MMBPR), and Visual-Language Contrastive Learning with Multi-grained Text Features (VLCL-MG). Hyper- parameters, training procedures, and evaluation protocols are specified in the manuscript. We release the code, trained model checkpoints, and the dataset, enabling other researchers reproduce periments under the same settings.
REFERENCES
Ardhendu Behera, Zachary Wharton, Pradeep RPG Hewage, and Asish Bera. Context-aware atten- tional pooling (cap) for fine-grained visual classification. Proceedings of the AAAI conference on artificial intelligence, volume 35, pp. 929-937, 2021.
Steve Branson, Grant Van Horn, Serge Belongie, and Pietro Perona. Bird species categorization using pose normalized deep convolutional nets. arXiv preprint arXiv:1406.2952, 2014.
Po- Yung Chou, Yu-Yung Kao, and Cheng-Hung Lin. Fine-grained visual classification with high- temperature refinement and background suppression. arXiv preprint TrXiv:2303.06422 2023.
Weijian Deng, Joshua Marsh, Stephen Gould, and Liang Zheng. Fine-grained classification via categorical memory networks. JEEE Transactions Image Processing, 31 31:4186 144186-4196, 2022.
Yifeng Ding, Zhanyu Ma, Shaoguo Wen, Jiyang Xie, Dongliang Chang, Zhongwei Si, Ming Wu, and Haibin Ling. Ap-cnn: Weakly supervised attention pyramid convolutional neural network for fine-grained visual classification. IEEE Transactions on Image Processing, 0112826-2836 2021.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszko- reit, and Neil Houlsby. An image worth 16x16 words: Transformers for image recognition scale, 2021. URL httpt::////// httppp:://aaxv....//a orq /20ea.2010..mm 2010.
Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia, Jiyang Xie, Zhanyu Ma, Yi-Zhe Song, and Jun Guo. Fine-grad -nnaiir visual classification via progressive multi quutt--ranuularit training of jigsaw patches. In European Conference Computer Vision, pp. 153-168. Springer, 2020.
Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, and Changhu Wang- Transfg: A transformer architecture for fine-grate finennedined recognition. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pp. 852-860, 2022.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp- 770-778, 2016.
Xin Jiang, Hao Tang, Junyao Gao, Xiaoyu Du, Shengfeng He, and Zechao Li. Delving into multi- modal prompting for fine- fine-grained visual classification. In Proceedings of the AAAI conference artificial intelligence, volume 38, pp. 2570-2578, 2024.
Xiao Ke, Yuhang Cai, Baitao Chen, Hao Liu, and Wenzhong Guo. Granularity distillation and structure modeling region proposal network for fine- fine-grained image classification. Pattern Recognition, 137:109305, 2023.
11

---

Published as a conference paper at ICLR 2026
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li. Novel dataset for fine- grained image categorization: Stanford dogs. In Proc. CVPR workshop on fine-graine grained visual categorization (FGVC), volume 2, 2011.
Sangwon Kim, Jaeyeal Nam, and Byoung Chul Ko. Vit-net: Interpretable vision transformers with neural tree decoder. In International conference on machine learning, pp. 11172. 2022.
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision work- shops, pp. 554-561, 2013.
Mengcheng Lan, Chaofeng Chen, Yiping Ke, Xinjiang Wang, Litong Feng, and Wayne Zhang. Clearclip: Decomposing clip representations for dense vision-language inference. European Conference on Computer Vision, pp. 143-160. Springer, 2024.
Siyuan Li, Li Sun, and Qingli Li. Clip-reid: exploiting vision-language model for image identification without concrete text lahels. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pp. 1405-1413, 2023.
Tsung-Yi Yi Lin, Priya Goyal, Ross Girshick. Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999-3007, 2017. doi: 10.1 10.11////..222....24444
Tsung-Yu Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear enn models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, pp.
1449 1050-1457,222 2015. 2015.
Hai Liu, Cheng Zhang, Yongjian Deng, Bochen Xie, Tingting Liu, and You-Fu Li. Transife: Invari- ant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Transactions on Multimedia, 27:1677-1690. 2023.
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 10012-10022, 2021.
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv: 1306,551,, 2013.
Zhuang Miao, Xun Zhao, Jiabao Wang, Yang Li, and Hang Li. Complemental attention multi feature fusion network for fine-grained classification. IEEE Signal Processing Letters, 28:1983-1983 2021.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp. 8748-8763. PMLR, 2021.
Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, and Ling Shao. Fine-grained recog- nition: Accounting for subtle differences between similar classes. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp. 12047-12054, 2020.
Ming Sun, Yuchen Yuan, Feng Zhou, and Errui Ding. Multi-attention multi-class constraint for fine-grained image recognition. In Proceedings of the european conference on computer vision (ECCV), pp. 805-821, 2018.
Christian Szegedy, Vincent Vanhoucke, Sergey loffe. Jon Shlens, and Zbigniew Wojna. Rethink- ing the inception architecture for computer vision. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 2818-2826, 2016.
Grant Van Hor, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry. Panos Ipeirotis, Pietro Perona, and Serge Belongie. Building bird recognition app and large scale dataset with citizen scientists: The fine print in fine-graine grained dataset collection. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 595 604, 2015.
12

---

Published as a conference paper at ICLR 2026
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie. The caltech-ucsd birds-200-2011 200-2001 Californaa Institute Technology. 2011.
dataset. Technical Report
Chuanming Wang, Huiyuan Fu, and Huadong Ma. Multi-part token transformer with dual con- trastive learning for fine-gre ime-ggained image classification. In Proceedings of the 31st ACM Interna- tional Conference on Multimedia, pp. 7648-76566 2023a.
Shijie Wang, Jianlong Chang, Haojie Li, Zhihui Wang. Wanli Ouyang, and Qi Tian. Open-set fine-grained retrieval via prompting vision-language evaluator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 19381-19391, 2023b.
Shijie Wang, Zhihui Wang, Haojie Li, Jianlong Chang, Wanli Ouyang, and Qi Tian. Semantic- guided information alignment network for fine-grained image recognition. IEEE Transactions on Circuits and Systems for Video Technology, 33(1 1):6558-6557 2023c.
Qin Xu, Sitong Li, Jiahui Wang, Bo Jiang, Bin Luo, and Jinhui Tang. Context-semantic quality awareness network for fine-grained visual categorization. Pattern Recognition, pp. 112033, 2025.
Xuhui Yang, Yaowei Wang, Ke Chen, Yong Xu, and Yonghong Tian. Fine-grained object clas-
sification via self-supervised pose alignment. In Proceedings of the IEEE/CVF Conference Computer Vision and Puttern Recognition, pp. 7399-7408, 2022.
Ying Yu, Wei Wei, Cairong Zhao, Jin Qian, and Enhong Chen. Structural feature enhanced trans- former for fine- fineggrained image recognition. Pattern Recognition, pp. 111955, 2025.
Fan Zhang, Meng Li, Guisheng Zhai, and Yizhao Liu. Multi-branch and multi-scale attention learn- ing for fine-grained visual categorization. MultiMedia Modeling: 27th International Con- ference, MMM 2021, Prague, Czech Republic, June 22-24, 2021, Proceedings, Part 27, pp- 136-147. Springer, 2021.
Lianbo Zhang. Shaoli Huang, Wei Liu, and Dacheng Tao. Learning a mixture of granularity-spectie experts for fine-grained categorization. In Proceedings of the IEEF/CVF international conference on computer vision, pp. 8331-8340, 2019.
Ning Zhang, Jeff Donahue, Ross Girshick, and Trevor Darrell. Part-based r-cnns for fine-grained cat- egory detection. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzer- land, September 6-12, 2014, Proceedings, Part 13, pp. 834-849. Springer, 2014.
Zhicheng Zhang, Hao Tang, and Jinhui Tang. Multi-scale activation, selection, and aggregation: Exploring diverse cues for fine-gratd finennriied bird recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. (0385-10393, 2025.
Zi-Chao Zhang, Zue Chen, Yongxin Wang, Xin Luo, and Xin-Shun Xu. A vision transformer for fine rree-grrnndd classification by reducing noise and enhancing discriminative information. Pat- tern Recognition, 145:109979, 2024.
Yifan Zhao, Jia Li, Xiaowu Chen, and Yonghong Tian. Part-guided relational transformers for fine- grained visual recognition. IEEE Transactions on Image Processing, 30:9470-9481, 2021.
Heliang Zheng, Jianlong Fu, Tao Mei, and Jiebo Luo. Learning multi- attention convolutional neural network for fine-grained image recognition. In Proceedings the IEEE international conference on computer vision, pp. 5209-5217, 2017.
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo. Learning deep bilinear transformation for fine-grained image representation. Advances in Neural Information Processing Systems, 32, 2019.
Chong Zhou, Chen Change Loy, and Bo Dai. Extract free dense labels from clip. In European Conference on Computer Vision, pp. 696-712. Springer, 2022.
13

---

Published as a conference paper at ICLR 2026
A THE USE OF LARGE LANGUAGE MODELS
In this work, we leverage large language models (LLMs), specifically ChatGPT-4o, to construct intermediate category hierarchies. Beyond this, LLMs also employed to assist in code develop- ment and manuscript refinement. All outputs from the LLM are carefully verified to ensure aceu- racy, However, the intermediate category hierarchies may contain some errors due their reliance on domain-specific expert knowledge, representing potential source of noise in the experimental results.
B TRAINING AND HYPERPARAMETER SETTINGS FOR DIFFERENT BACKBONES
Table 8: Hyperparameter settings for different backbones.
Hyperparameter Setting
ResNet-50
ViT-B
Swin-B
Input resolution
448 x 448
518 x 518
384 x 384
Batch size
16
16
16
Weight decay
0.01
0.01
0.01
Optimizer
Adam
Adam W (0.9, 0.95)
Adam W (0.9, 0.95)
Optimizer
(0.9, 0.95)
Optimizer e
1e-8
1e-8
1e-8
Initial learning rate
1e-4
1e-5
1e-5
Learning rate schedule
Cosine annealing
Cosine annealing
Cosine annealing
Warm-up epochs Epochs
10
10
10
100
100
100
Focusing parameter
4
4
4
Smoothing noise factor
[0.4,0.33.02,00.]] (0.1,0.2,0.4.1.00)
[0.4.0.3,0.2,0.11 [0.1,0.2.004.1.00]
[0.4.0.3.0.2.0.1]] [0.1,0.2.0.4.1.0]]
Multi-scale loss weight é.
Table 9: Training time (hours) and peak VRAM (GB) for each model and dataset.
RN50
ViT-B
Swin-B
Dataset
Time (h)
VRAM (GB)
Time (h)
VRAM (GB)
Time (h)
VRAM (GB)
AIR
3.7
18
5.1
22
4.8
23.8
CAR
3.8
15.1
5.4
19.1
5.1
21.6
CUB
3.0
16.5
4.2
20.6
3.9
22.7
NAB
12
16.5
16.6
20.6
15.4
22.7
DOG
5.5
15.1
8.0
19.1
7.5
21.6
Table 9 shows the training time and peak VRAM for each backbone and dataset. RN50 is generally faster and uses less memory than VTT-B and Swin-B, while larger datasets (e.g., NAB) require more time.
C PART TEXT FOR DIFFERENT DATASETS
Dataset
Part Text
AIR
background of plane, tail of plane, logo of plane, engine of a plane, landing gear of a plane, fuselage of a plane
CUB
background of a bird, head of bird, foot of a bird, body of a bird, mouth of a bird background of a car, head of a car, body of car, back of car background of a bird, head of bird, foot of a bird, body of a bird, mouth bird background of a dog, head dog, foot dog, body of a dog
CAR
NAB
DOG
14

---

Published as a conference paper at ICLR 2026
D OTHER RESULTS.
Table 10: Classification accuracy (%) fine-grained datasets using different embedding/masking strategies.
ResNet50
ViT-B
Method
CUB
Aircraft
Car
CUB
Aircraft
Car
Random text embeddings (Flex)
88.93
94.75
95.38
92.04
95.45
96.36
Random masking (S))
88.12 12
94.93
95.25
91.76
95.26
96.26
Part text embeddings
89.13
95.14
95.59
92.34
96.48
96.44
We conducted experiments which either Fix or S(k) was randomized. Both random strate- gies can be viewed as mutually exclusive data augmentation methods based on random masking. However, Random Text Embeddings tend to occlude semantically similar regions, whereas Ran- dom Masking hides regions randomly. Our proposed PSCL architecture demonstrates considerable robustness: thanks to the MMBPR and VLCL-MG modules, the model can still learn to focus on relevant regions autonomously. Nevertheless, providing targeted human guidance could further im- prove the efficiency of this process.
Table 11: Effect of intermediate-category text annotations on NAB classification performance using VīT-B. Accuracy (%) is reported.
Intermediate-Categon Text
Accuracy (%)
Expert annotations (precise hierarchy) Generated via ChatGPT-4o semii-uuoomatcc)
93.74
93.48
Random-text control group
92.83
We posit that the NAB dataset benefits substantially from its inherent, precise hierarchical category structure, resulting in a significant performance boost. Accordingly, we employed ChatGPT-44 in semi-automatic manner generate intermediate-caten catteooy text annotations, while also creating random-text control group, and conducted comparative experiments using the ViT-B backbone. The results demonstrate that accurate expert annotations effectively activate the VI.CL--MG module, yet even the generated intermediate-catege text can improve classification accuracy to a certain extent.
Table 12: Performance of different numbers of part texts on AIR and CUB datasets
Dataset
Part text
N
Accuracy %
background of a plane, tail of a plane, Logo of plane, engine of plane, landing gear of plane, fuselage of plane
AIR
95.14
background of plane, tail of a plane, head of plane, fuselage of plane
AIR
94.74
AIR
background of plane, plane plane
2
94.75
AIR
94.66
background of bird, head of bird, foot of bird, body of bird, mouth of a bird
CUB
99.13
CUB
background bird, head of bird, body of bird
88,83
CUB
background of bird, bird bird
2
88.47
CUB
88.44
We conducted experiments to analyze the effect of the number of part texts (N). The results below suggest that 4-6 part texts offer a good balance between performance and complexity. Using even single part text (e.g., "bird") still yields competitive results, other effective components (like
15

---

Published as a conference paper at ICLR 2026
progressive learning) contribute significantly. While using parts increases computational cost, the performance gains diminish marginally. The experiments were performed using RN50.
E EXAMPLES OF SOME INTERMEDIATE CATEGORIES
Table 13: Intermediate classes for the AIR dataset
Fine-grained
Intermediate- antermediateggrained
snermedditt-graiieee 2
737-900
narrow-body airliner
twinjet
747-100
wide-body airliner
four-engined jet aircraft
A330-300
wide-body airliner
twinjet
A340-200
wide-body airliner
four-engined jet aircraft
Cessna 525
business jet
twinjet
Challenger 600
business jet
twinjet
DC-10
wide-body airliner
trijet
DC-3
cargo aircraft business jet
twin-turboprop
Gulfstream V
twinjet
Hawk TI
light aircraft
single-engine jet four-engined jet aircraft trijet
11-76
cargo aircraft
L--1011
wide-body airliner
MD-11
wide-body airliner
trijet
Table 14: Intermediate classes for the CUB dataset
Fine-grained
Intermediate-graaneed
Intermediate-grainedd 2
Frigatebird
Seabirds
Waterbirds
Gadwall
Ducks
Waterbirds
American Goldfinch
Finches
Songbirds
Boat-tailed Grackle
Grackles
Songbirds
American Crow
Crows
Corvids
Fish Crow
Crows
Corvids
Black-billed Cuckoo
Cuckoos
Songbirds
Rusty Blackbird
Blackbirds
Songbirds
Yellow-headed Blackbird
Blackbirds
Songbirds
Indigo Bunting
Buntings
Songbirds
Table 15: Intermediate classes for the CAR dataset
Fine-graine Fine-grained
Intermediate- grained
Intermediate Intermediate-graaneee -rainted--randddddeee 2
Audi S4 Sedan 2007
Sedan
Performance Vehicle
Audi TT RS Coupe 2012
Coupe
Performance Vehicle
BMW ActiveHybrid 5 Sedan 2012
Sedan
Hybrid Vehicle
BMW Series Convertible 2012
Convertible
Luxury Vehicle
BMW Series Coupe 2012
Coupe
Luxury Vehicle
Acura Integra Type R 2001
Coupe
Performance Vehicle
Acura ZDX Hatchback 2012
Hatchback
Luxury Vehicle
Aston Martin V8 Vantage Convertible 2012
Convertible
Luxury Vehicle
Chrysler Crossfire Convertible 2008
Convertible
Performance Vehicle
Chrysler PT Cruiser Convertible 2008
Convertible
Family Car
Daewoo Nubira Wagon 2002
Wagon
Family Car
16

---

Published as a conference paper at ICLR 2026
Table 16: Intermediate classes for the DOG dataset
Fine-grained
Interneddite--raaneed 1
Intermeddiate-raained 2
Blenheim Spaniel
Sporting
Spaniel
Papillon
Toy
Toy-group
Toy Terrier
Toy
Terrier-toy
Rhodesian Ridgeback
Hound
Sighthound
Afghan Hound
Hound
Sighthound
Weimaraner
Sporting
Pointer
Staffordshire Bullterrier
Terrier
Bull-type
Cocker Spaniel
Sporting
Spaniel
Pug
Toy
Toy-group
Great Pyrenees
Working
Working-group group
Irish Water Spaniel
Sporting
Spaniel
Kuvasz
Working
Working-grow group
Groenendael
Herding
Herding-grout
Table 17: Intermediate classes for the NAB dataset
Fine-grained
Intermediate-graaned
ceammediate-grainned
Black-bellied Whistling-Dun -uck
Black-bellied Whistling Duck
Ducks, Geese, and Swans Plovers, Sandpipers, and Allies Pelicans, Herons, Ibises, and Allies Plovers, Sandpipers, and Allies
Semipalmated Plover
Semipalmated Plover American White Pelican
American White Pelican
Killdeer
Killdeer
Chimney Swift
Chimney Swift
Swifts and Hummingbirds Plovers. Sandpipers, and Allies Ducks, Geese, and Swans
American Oystercatcher
American Oystereatcher
Ross's Goose
Ross's Goose
Barn Owl
Barn Owl
Owls
Turkey Vulture
Turkey Vulture
Hawks, Kites, Eagles, and Allies Pelicans. Herons, Ibises, and Allies Grouse, Quail, and Allies Pigeons and Doves
Brown Pelican
Brown Pelican
Scaled Quail
Scaled Quail
Rock Pigeon
Rock Pigeon
Black-necked Stilt
Black-necked Stilt
Plovers, Sandpipers, and Allies
17

---

Published as a conference paper at ICLR 2026
F LLM PROMPT
We use ChatGPT-4o with the following prompt (AIR example):
"Please classify the following fine-grained categories based visually dis- cernible characteristics. Each category must belong to two distinct intermediate- grained categories. Output line per category: [Fine-grained)), [Intermediate-1] [Intermediate-2]: 707-320 727-200 737-200
G LLM WOISE-SENSITIVE EXPERIMENTS
We additionally evaluated DeepSeek-RI and Qwen-2.5-24 2.5-Max under greedy decoding. The results are summarized in table [8]
Table Noise-sensitivity experiment results (accuracy %).
Setting
Model
Accuracy
Baseline
ChatGPT-40
95.14
Re-label using same intermediate-grainee categories
DeepSeek-R1
95.08
Qwen-2.5-Mar
95.11
Re-generate intermediate-graiiee categories
DeepSeek-R1
94.83
Qwen-2.5-Max
95.21
Only DeepSeek-RI's regenerated taxonomy became overly coarse (e.g., engine type, wing type), but LLM variation overall shows robustness. All evaluated models outperform the version without VLCL-MG (94.54%).
H INPUT RESOLUTION OUR MODEL.
For all RN50 models. the input resolution is consistent. For Swin-B, we adopt 384 x 384 as in HERBS, while TransIFC+ and CSQA-Net use 448 448. ViT-NeT uses 224 224.
The ViT-B model is somewhat special. After multiple trials, we selected ViT-B/14 with 518 518 resolution as the optimal choice. Some additional results for ViT-B models with different input resolutions are shown in table 19
Table 19; Performance of ViT-B models under different input resolutions (accuracy %.).
Model
Resolution
AIR
CUB
DOG
ViT-B/14
518 x 518
96.48
92.34
92.27
VIT-B/14
336
336
92.62
87.21
90.02
VIT-B/16
448 448
94.31
92.23
91.01
VIT-B/16
384 x 384
93.58
88.65
92.66
The performance of ViT-B on AIR improves significantly at higher resolutions. We attribute this to the increased resolution and smaller patch size, which allows the model to capture finer details of parts such "logo" and "engine".
We note that reporting the best best-performing resolutions might raise concerns regarding fairness. To address this, in Comparison with Other Methods we explicitly report both 448 448 and 518 x 518 settings for ViT-B. The 448 X 448 configuration ensures direct and fair comparison with existing methods, while the 518 x 518 setting reflects the commonly adopted optimal performance. Even when evaluated the same resolution (448 448), ViT-B models still achieve strong performance across datasets, demonstrating that the observed improvements are not due resolution advantages unfair comparisons.
18

---

Published as a conference paper at ICLR 2026
1 RESAF OPERATION DETAILS
Additional ablation studies on ReSAF variants (with and without eq.
concatenation) are pro-
vided below, conducted on the AIR dataset with RN50 backbone:
Table 20: Ablation study of ReSAF variants on the AIR dataset
Mechanism
w/o Eq. Concat (%)
w/ Eq.11 Concat (%)
SAE Xu al. 2025)
93.67
93.79
SAE Positional Encoding
94.33
94.43
Prog. Cross-Attention
94.60
94.99
Prog. Flipped Key Cross-Attention
94.42
94.78
ReSAF (Ours)
94.51
95.14
ReSAF leverages hierarchical feature roles: deep layers drive classification, while shallow layers assist. The flipped-key mechanism ensures complementary shallow information is captured in re- gions overlooked by deep features. Eq. [T] concatenation delays mallow-feature fusion, allowing the encoder and loss function to select the most useful features and preventing interference between shallow and deep layers. The experimental results above validate the effectiveness and reliability of ReSAF.
INFERENCE EFFICIENCY
We report the computational cost of PSCL across different backbones using single NVIDIA RTX 4090 GPU. The encoder hidden dimension set to 768, and the number of stages fixed (m Table 21 summarizes per-image inference time, throughput, and peak VRAM for different batch sizes.
Table 21: Inference statistics. Time in ms/image; Throughput in images/s; PSCL: prediction head; Backb.: backbone; VRAM: peak GPU memory (GB).
Batch
Model
Time (ms)
Throughput (img/s)
VRAM
PSCL
Backb.
PSCL
Backb.
(GB)
VIT-B
17.26
6.39
57.94
156.59
4.54
RN50
18.29
6.56
54.66
152.47
3.65
Swin-B
31.41
21.75
31.84
45.98
4.71
8
VIT-B
5.18
3.97
193.13
251.91
4.78
8
RN50
3.11
1.38
321.89
724.25
3.93
Swin-B
5.01
3.45
199.79
289.70
5.04
K PERFORMANCE USING ONLY LAST STAGE OUTPUT AND INFERENCE FEATURE SELECTION
During inference, our model exclusively utilizes the global branch at the final stage. This choice is supported empirical results and design considerations summarized as follows.
1. ROLE PART-LEVEL BRANCHES AND INFERENCE EFFICIENCY
Part-level branches function as regional prompts and feature refinement modules during training. However, the slight performance improvement they offer inference time does not justify their computational cost. Therefore, we exclude these branches during deployment.
Comparisons between using only the global branch prediction pelebal
P =s and aggregating
part-level predictions psmm N+ A. are shown below:
19

---

Published as a conference paper at ICLR 2026
Table 22: Comparison of inference strategies using only the global branch vs. summing part-level
predictions. Accuracy (%) is reported for CUB, AIR, and CAR datasets across different backbones.
Method RN50 (CUB/AIR/CAR) Swin-B (CUB/AIR/CAR) VIT-B (CUB/AIR/CAR)
Pglobal 89.13/95.14/98 psum
93.01/95.32955544 93.02/95.38/95555
92.34/96.48/96. 92.29996..36/6.444
19.08/95.17/95555
2. PROGRESSIVE CONFIDENCE ENHANCEMENT VLCL-MG
The VLCL-MG module introduces progressively strengthened confidence constraints across stages. As features propagate through the hierarchy, earlier-stage representations are processed and con- catenated to later stages (eq. (II),, allowing the final stage to integrate comprehensive multi-stage information. To examine the effectiveness of different inference strategies, we compare:
pl
Σ
Pst
P
Σ 9min
P. P
Sannn
Results on the CUB dataset validate the superiority of using only the final stage:
Table 23: Comparison of different inference strategies on the CUB dataset. Accuracy (%) is reported for each backbone.
Method
RN50
Swin-B
ViT-B
P
88.40
92.71
90.95
p2
88.95
93.09
92.12
p3
89.13
93.01
92.34
Overall, relying solely on the final-stage prediction (P*) provides the best balance between accuracy and computational efficiency. While minor fluctuations may appear in certain individual cases, the final-stage strategy (P*) remains the most reliable and effective option when considering overall performance and practical deployment constraints.
L ADDITIONAL VISUALIZATIONS OF PART SCORES
())
(II)
(II)
(IV)
(VI)
(VII)
(VIII)
(IX)
Figure 7: Additional visualizations part scores. PLM uses the following textual prompts: (I)
logo; (II) wing; (III) windows; (IV) wing: (V) tail; (VI) eyes; (VII) wheels; (VIII) windows; (IX)
headlights.
20


Reviews:
[{"review_id": "4GHrLHt7kc", "reviewer": "Reviewer_E1mk", "summary": "The paper proposes PSCL for fine-grained visual classification, decomposing the problem into three parts: (i) a PLM that uses ClearCLIP text prompts to localize part regions and decouple selection from encoding; (ii) MMBPR for multi-scale, multi-branch progressive reasoning with ReSAF (a reverse-key scale-attention fusion) to reduce cross-scale redundancy; (iii) VLCL-MG that aligns image features with coarse/mid/fine textual concepts. Results are reported on AIR/CAR/CUB/NABirds/DOG with RN50/ViT-B/Swin-B. The masking pipeline is “argmax → morphological refinement,” and a component-level GFLOPs table is provided. The abstract claims an anonymous code link.", "questions": "Is the reverse smoothing in Eq. (17) intentional? If yes, provide intuition (e.g., effect on hard examples/confidence distribution) and a small ablation on AIR & CUB comparing standard vs. reverse smoothing, including interaction with γ.\n\nProvide an end-to-end resource table for RN50 / ViT-B / Swin-B × number of branches N: FLOPs, params, peak VRAM, and latency at B=1 and B=8. Include comparisons to TransFG/CSQA-Net and a strong CLIP-head baseline (e.g., CoOp/CoCoOp/Tip-Adapter) under matched resolution/budget.\n\nPLM reproducibility: exact ClearCLIP release/ckpt, input resolution; complete part-prompt lists; morphology kernel/iterations; whether logits are normalized or temperature-scaled pre-argmax; plus a short sensitivity study.\n\nMid-level text protocol & robustness: release prompts, sampling/filters, and the final mid-level lists for AIR/CAR/CUB/DOG; quantify performance variance across multiple LLM samples to bound noise.\n\nReSAF intuition: add a histogram or case study of cross-scale similarities to show how the “reverse key” steers attention away from redundant regions rather than only relying on Table-level gains.\n\nHyperparameter fairness: provide an equal-budget tuning comparison or justify why transferring AIR-tuned hypers doesn’t bias outcomes.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Clear problem decomposition. The “where to look / how to represent / how to align” split matches FGVC pain points and keeps the design modular.\n\nInterpretability. The PLM produces intuitive part masks from ClearCLIP similarity maps; visualizations help readers see what the model attends to.\n\nSolid component study. ReSAF has reasonable motivation and ablations against MLP / cross-attention baselines; gains are consistent rather than cherry-picked.\n\nBreadth of evidence. Improvements hold across multiple datasets and backbones, suggesting the method isn’t overly tuned to a single regime.\n\nThis separation also makes the ablations easier to interpret.", "weaknesses": "“Reverse” label smoothing in Eq. (17). The formula appears to assign a smaller weight to the true class and larger to others, which is the opposite of standard label smoothing. I suspect this might be a typo; if it’s intentional, please explain the intuition and provide a small ablation.\n\nNo end-to-end compute/latency. The paper lists component GFLOPs (and notes branch-count growth), but omits whole-model FLOPs/parameter count/peak VRAM / latency (e.g., batch=1 & batch=8). That makes deployment comparisons hard. If I missed this in the appendix, please point me to the exact page/table.\n\nReproducibility inconsistency. The abstract links to an anonymous repo, yet the Reproducibility Statement says “code available upon request.” Please unify this to an anonymous public repo with full training/eval scripts, configs, and (ideally) weights. If this is already in the appendix, please point me to the exact page/table.\n\nPLM details are insufficient for reproduction. To replicate the masks: specify ClearCLIP version/checkpoint and input resolution; provide the full part-prompt lists per dataset; describe kernel shape/size/iterations for morphology; state whether similarity logits are normalized or temperature-scaled before argmax.\n\nMid-level text generation is under-specified. For datasets without native hierarchies, the mid-level labels are LLM-generated. Please release prompts/sampling & screening rules, and include a broader noise-sensitivity study beyond one dataset.\n\nTuning fairness. Most hyperparameters (except LR) are tuned on AIR+RN50 and transferred elsewhere. Add an “equal-budget tuning” table or a light per-dataset retune to rule out hidden advantages.\n\nMinor writing issues. There are a few typos (e.g., “TWe utilize…”). Figure 2 is dense; consider trimming labels and adding a one-page “Training Recipe.”", "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": "J1v9Jq50HP", "reviewer": "Reviewer_WQPm", "summary": "This paper proposed Part-level Semantic-guided Contrastive Learning to capture both part-level detail and spatial relational features for FGVC task.\nAuthors combine vision–language alignment, part reasoning, and progressive confidence modeling into an unified framework.", "questions": "Please refer to weakness.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Consistent SOTA results on 5 datasets and across CNN/Transformer backbones.\n\nThe CLIP-based masks yield interpretable part attention for recognition.", "weaknesses": "The biggest problem with this paper is that the design is overly complex, while the improvement is relatively limited.\n\nIs the number of part texts predetermined? How does the method adapt to different tasks, especially in cases where the number of part texts varies across categories within the same classification task?\n\nCan large language models accurately describe the differences between categories? Coarse-grained differences are easy to express, such as head color or beak shape. However, some subtle differences are difficult to localize and describe using language.", "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": "vsIEkGrAQd", "reviewer": "Reviewer_UrRj", "summary": "This paper proposes a new FGVC framework that leverages text-guided part localization, multi-level class embedding, and multi-grained vision-language contrastive learning. This framework is compatible with different backbones and achieves superior performance over baseline methods across benchmarks.", "questions": "My questions are listed above in the weaknesses part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation for overcoming the limitation of the common part-level interaction-based methods, such as CAP, is reasonable and well demonstrated in Figure 1 and supported by Figure 4. \n\n2. The proposed method is compatible with different pretrained backbones and achieves consistent improvement.\n\n3. The idea that leverages a single VLM to localize parts and conduct contrastive learning is novel. \n\n4. The code is provided to ensure reproducibility.", "weaknesses": "1. The writing lacks some important details. For example, how to apply a multi-stage module on the single-scale ViT? Does ReSAF only work between f_{1,2,3} and f_4? Does it also work between f_{1,2} and f_3?\n\n2. The explanation of Eq. 5 is confusing. How does this min-max operation perform in detail?\n\n3. There is a typo in L249-250: \"TWe\".\n\n4. The computation cost needs to be compared with baseline models, such as TransFG. It would be better to add at least one baseline model for different backbones.\n\n5. What is the performance if only the last stage output is leveraged during training? Which feature is used for final categorization during inference?", "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": "nVgBvMtm30", "reviewer": "Reviewer_fUYe", "summary": "This paper focuses on the task of fine-grained visual classification and proposes the part-level semantic-guided contrastive learning approach. It consists of three modules, i.e., part localization module (PLM), multi-scale multi-part branch progressive reasoning (MMBPR), and visual-language contrastive learning based on multi-grained text features (VLCL-MG). Experiments are conducted on five fine-grained image datasets.", "questions": "My questions are shown in weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The writing is easy to follow.\n- Experiments are conducted on five datasets with three different backbones.", "weaknesses": "- The core idea of PLM for localizing part regions appears similar to [a]. PLM leverages CLIP to select N part masks, but the paper does not clarify how N is chosen or how performance changes with different numbers of parts. \n- The paper claims that s_min can be adjusted based on task requirements; however, this is unclear. What is its influence? And the idea of multi-scale features has been widely used.\n- MMBPR works simply by concatenating the class token with multi-scale multi-part features, then processing the result with LayerNorm (LN), multi-head self-attention (MHSA), and an MLP. As shown in Table 6, the gain from MMBPR (line 3 vs. line 2) is marginal, leaving its effectiveness unconvincing.\n- Although VLCL-MG leverages label smoothing, focal loss, and auxiliary knowledge from intermediate categories, the observed improvements are marginal, as evidenced by Table 6 (line 5 versus line 3).\n- Table 2 indicates that the proposed method yields only marginal gains over SOTA baselines, particularly on CUB, CAR, and AIR. An additional evaluation on iNaturalist is recommended. Besides, this method utilizes extra information, including CLIP, textual information, and intermediate categories, which are not used in SOTA methods.\n- Since image resolution has a direct impact on performance, the paper should specify the resolutions used in all compared methods to ensure a fair comparison.\n- Figure 1 highlights the spatial structures of non-rigid vs. rigid objects, but the method does not explicitly model them. Incorporating spatial structure could be promising.\n- There are issues with grammar and presentation. On line 250, “TWe” appears to be a typo. In addition, Figure 2 may be overly complex and hard to follow.\n\n\n[a] Part-guided Relational Transformers for Fine-grained Visual Recognition", "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)