Rethinking BCE Loss for Multi-Label Image Recognition with Fine-Tuning

Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models

PuRF is introduced, a novel PuRiFication-driven cache-based method for multi-label test-time adaptation of vision-language models that consistently outperforms state-of-the-art methods on ViT-B/32 across five datasets.

TextFM: Robust Semi-dense Feature Matching with Language Guidance

CLIP-Guided Label-Free Discriminative Region Scoring for Fine-Grained Classification

Recent vision models such as CLIP and SAM enable training-free segmentation and semantic encoding for fine-grained classification. A common approach is to compare the representations of segmented image regions with the text prompt embeddings of the corresponding labels. However, it remains unclear how different local regions and CLIP-based scoring strategies affect the selection of discriminative evidence, especially when ground-truth labels are unavailable. In this paper, we propose a unified CLIP-guided label-free region scoring framework for fine-grained classification. The framework evaluates cosine similarity-based, margin-based, and entropy-based scoring strategies using both SAM-generated masks and random crops, and introduces two label-free pseudo-label variants based on global image embeddings and local region embeddings. We conduct experiments on five fine-grained classification datasets to systematically compare different region generation methods and scoring strategies. The results show that Soft Negative Margin scoring achieves the strongest performance, and pseudo-label scoring closely approximates true-label performance. Although SAM produces semantically meaningful masks, random-crop-based pseudo-label scoring consistently outperforms SAM-based scoring across all datasets, suggesting that random crops preserve surrounding information and provide more stable semantic context when pseudo-labels are noisy. In addition, SAM masks benefit from aggregating embeddings from all regions, whereas random crops tend to perform better with a smaller top-k subset. These findings provide new insights for fine-grained classification.

Discrepancy-guided parameter suppression for robust fine-tuning and domain generalization

Fine-tuning large pre-trained models on specific tasks can improve in-distribution (ID) accuracy but often degrades out-of-distribution (OOD) generalization. Discrepancy-Guided Parameter Suppression (DPS) is a fine-tuning approach that Discrepancy-Guided Parameter Suppression (DPS) is a fine-tuning approach that freezes the top K percent of model parameters based on the magnitude of their changes. By ranking parameters according to a discrepancy score (the magnitude of change during fine-tuning) and suppressing the highest-discrepancy parameters, DPS encourages the model to learn task-relevant features without overfitting to spurious ID-specific patterns. We evaluate DPS on the challenging iWildCam dataset under camera-induced domain shifts, providing the first in-depth assessment of DPS on a real-world distribution shift benchmark. Using a CLIP ViT-B/16 backbone and identical hyperparameters for a cross-entropy baseline and DPS, we observe improvements on OOD metrics: OOD macro-F1 increases from 35.14% to 36.15% and OOD accuracy from 73.92% to 74.95%, roughly a 1% absolute gain on both. We also provide ablation analyses of the discrepancy metric and suppression rate, showing that normalized, channel-wise suppression at high freezing ratios yields the strongest OOD robustness, consistent with prior findings.

RoFLIP: Robust and Fine-Grained Alignment for Vision-Language Compositional Reasoning

The Robust and Fine-grained training framework for CLIP-based vision-language models (RoFLIP) is proposed, enhancing both the robustness and granularity of vision-language alignment and underscore RoFLIP’s compositional reasoning and generalization abilities.

CANNE: CLIP-Based ANNE Selection for Noisy-Label Learning

Learning with noisy labels (LNL) remains challenging, especially when the identification of clean samples relies heavily on the predictions of the model being trained. In such cases, early-stage selection errors may be reinforced during iterative optimization, leading to unreliable supervision. To alleviate this issue, a two-stage framework, termed CANNE, is proposed by combining Contrastive Language–Image Pre-training (CLIP)-based conservative offline cleaning with Adaptive Nearest Neighbors and eigenvector-based sample selection (ANNE)-based online refinement. Specifically, a high-confidence clean seed set is first constructed using two complementary probability sources derived from frozen CLIP representations and reliability criteria, including class-wise loss modeling and prediction consistency. This seed set is then used as a set of reliable anchors during the subsequent ANNE training process, where online feature- and neighborhood-based refinement further recovers and adjusts sample partitions. In this way, CANNE uses external vision–language priors to provide conservative and persistent guidance while preserving the adaptive recovery ability of online noisy-label learning. Experimental results on CIFAR-10, CIFAR-100, Animal-10N, and Mini-WebVision, together with additional evaluation under open-set noise, show that the proposed method achieves competitive performance across diverse noisy-label settings. In particular, CANNE achieves 96.6% and 96.3% best accuracies on CIFAR-10 under 80% and 90% symmetric noise, respectively, and 81.0% and 79.0% on CIFAR-100 under 20% and 50% symmetric noise. Additional repeated-run, threshold-sensitivity, and runtime analyses further indicate that the CLIP-based seed set provides stable guidance with only moderate computational overhead.