ICLR 2025 论文列表

Contrastive Language-Image Pre-training (CLIP) excels in global alignment with language but exhibits limited sensitivity to spatial information, leading to strong performance in zero-shot classification tasks but underperformance in tasks requiring precise spatial understanding. Recent approaches have introduced Region-Language Alignment (RLA) to enhance CLIP's performance in dense multimodal tasks by aligning regional visual representations with corresponding text inputs. However, we find that CLIP ViTs fine-tuned with RLA suffer from notable loss in spatial awareness, which is crucial for dense prediction tasks. To address this, we propose the Spatial Correlation Distillation (SCD) framework, which preserves CLIP's inherent spatial structure and mitigates above degradation. To further enhance spatial correlations, we introduce a lightweight Refiner that extracts refined correlations directly from CLIP before feeding them into SCD, based on an intriguring finding that CLIP naturally capture high-quality dense features. Together, these components form a robust distillation framework that enables CLIP ViTs to integrate both visual-language and visual-centric improvements, achieving state-of-the-art results across various open-vocabulary dense prediction benchmarks.

In Open Vocabulary Semantic Segmentation (OVS), we observe a consistent drop in model performance as the query vocabulary set expands, especially when it includes semantically similar and ambiguous vocabularies, such as ‘sofa’ and ‘couch’. The previous OVS evaluation protocol, however, does not account for such ambiguity, as any mismatch between model-predicted and human-annotated pairs is simply treated as incorrect on a pixel-wise basis. This contradicts the open nature of OVS, where ambiguous categories may both be correct from an open- world perspective. To address this, in this work, we study the open nature of OVS and propose a mask-wise evaluation protocol that is based on matched and mis- matched mask pairs between prediction and annotation respectively. Extensive experimental evaluations show that the proposed mask-wise protocol provides a more effective and reliable evaluation framework for OVS models compared to the previous pixel-wise approach on the perspective of open-world. Moreover, analy- sis of mismatched mask pairs reveals that a large amount of ambiguous categories exist in commonly used OVS datasets. Interestingly, we find that reducing these ambiguities during both training and inference enhances capabilities of OVS mod- els. These findings and the new evaluation protocol encourage further exploration of the open nature of OVS, as well as broader open-world challenges. Project page: https://qiming-huang.github.io/RevisitOVS/.

Vision-language models such as CLIP have demonstrated strong zero-shot performance, but their considerable size and inefficient inference limit customizable deployment for users. While knowledge distillation is a solution, it still requires the original data, which is not always available due to copyrights and privacy concerns. For many users seeking open-vocabulary customization, Data-Free Knowledge Distillation (DFKD) emerges as a promising direction. Upon rethinking DFKD, we find that existing methods fail on CLIP due to their heavy reliance on BatchNorm layers, which are unexpectedly unusable in CLIP. Based on our findings, we adopt image-text matching to achieve DFKD for CLIP, enabling customization based on arbitrary class texts. This involves (i) inversing a surrogate dataset from CLIP based on text prompts; and (ii) distilling a student model from CLIP using the surrogate dataset. Specifically, we introduce style dictionary diversification to enhance the diversity of synthetic images. To prevent uncontrollable semantics introduced by diversification, we propose a class consistency maintaining strategy to ensure the consistency of synthetic images. Based on synthetic images with various styles, we further propose meta knowledge distillation to train the student model with good generalization ability. Moreover, we introduce a simple yet effective method to enable customization based on few example images. Comprehensive experiments showcase the superiority of our approach across twelve customized tasks, achieving a 9.33\% improvement compared to existing DFKD methods.