Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2503.20826v1
• Date: Wed, 26 Mar 2025 02:00:49 GMT
• Title: Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
• Authors: Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang, Shuo Wang, Zhijian Song,
• Abstract summary: Weakly Supervised Semantic (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Maps (CAMs)<n>Recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignment remains unexplored.<n>We propose ExCEL to explore CLIP's dense knowledge via a novel patchtext alignment paradigm for WSSS.
• Score: 19.26516470653798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignment remains unexplored. In this work, we propose ExCEL to explore CLIP's dense knowledge via a novel patch-text alignment paradigm for WSSS. Specifically, we propose Text Semantic Enrichment (TSE) and Visual Calibration (VC) modules to improve the dense alignment across both text and vision modalities. To make text embeddings semantically informative, our TSE module applies Large Language Models (LLMs) to build a dataset-wide knowledge base and enriches the text representations with an implicit attribute-hunting process. To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner. Then Learnable Visual Calibration (LVC) is further proposed to dynamically shift the frozen features towards distributions with diverse semantics. With these enhancements, ExCEL not only retains CLIP's training-free advantages but also significantly outperforms other state-of-the-art methods with much less training cost on PASCAL VOC and MS COCO.

Related papers

• LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation [16.864086165056698]
  Existing open-vocabulary approaches leverage vision-language models, such as CLIP, to align visual features with rich semantic features acquired through pre-training on large-scale vision-language datasets.<n>We propose to alleviate the issues by leveraging multiple large-scale models to enhance the alignment between fine-grained visual features and enriched linguistic features.<n>Our method achieves state-of-the-art performance across all major open-vocabulary segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-11-30T05:49:42Z)
• ITACLIP: Boosting Training-Free Semantic Segmentation with Image, Text, and Architectural Enhancements [0.6990493129893112]
  Recent advances in Vision Language Models have reshaped the evaluation paradigm in computer vision tasks.<n>These foundational models, especially CLIP, have accelerated research in open-vocabulary computer vision tasks.<n>In this study, we enhance the semantic segmentation performance of CLIP by introducing new modules and modifications.<n>Our training-free method, ITACLIP, outperforms current state-of-the-art approaches on segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-11-18T20:31:38Z)
• Contrastive Localized Language-Image Pre-Training [60.4967533101887]
  Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training vision encoders to generate image/text representations.<n>We propose Contrastive Localized Language-Image Pre-training (CLOC) by complementing CLIP with region-text contrastive loss and modules.<n>CLOC enables high-quality regional embeddings for image region recognition and retrieval tasks.
  arXiv  Detail & Related papers  (2024-10-03T17:56:09Z)
• DIAL: Dense Image-text ALignment for Weakly Supervised Semantic Segmentation [8.422110274212503]
  Weakly supervised semantic segmentation approaches typically rely on class activation maps (CAMs) for initial seed generation. We introduce DALNet, which leverages text embeddings to enhance the comprehensive understanding and precise localization of objects across different levels of granularity. Our approach, in particular, allows for more efficient end-to-end process as a single-stage method.
  arXiv  Detail & Related papers  (2024-09-24T06:51:49Z)
• Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning [70.64617500380287]
  Continual learning allows models to learn from new data while retaining previously learned knowledge. The semantic knowledge available in the label information of the images, offers important semantic information that can be related with previously acquired knowledge of semantic classes. We propose integrating semantic guidance within and across tasks by capturing semantic similarity using text embeddings.
  arXiv  Detail & Related papers  (2024-08-02T07:51:44Z)
• SILC: Improving Vision Language Pretraining with Self-Distillation [113.50400246862056]
  We introduce SILC, a novel framework for vision language pretraining. SILC improves image-text contrastive learning with the simple addition of local-to-global correspondence learning by self-distillation. We show that distilling local image features from an exponential moving average (EMA) teacher model significantly improves model performance on dense predictions tasks like detection and segmentation.
  arXiv  Detail & Related papers  (2023-10-20T08:44:47Z)
• Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition [77.93678598476149]
  We establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) By cascading the CLIP image encoder with the reversed CLIP text encoder, a symmetrical structure is built with an image-to-text feature flow. Extensive experiments demonstrate the effectiveness of CLIP-OCR with 93.8% average accuracy on six popular STR benchmarks.
  arXiv  Detail & Related papers  (2023-10-08T04:00:20Z)
• VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts [2.0434814235659555]
  Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention recently for its transferable visual representation learning. We propose to enhance CLIP via Visual-guided Texts, named VT-CLIP. In few-shot settings, we evaluate our VT-CLIP on 11 well-known classification datasets to demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2021-12-04T18:34:24Z)
• DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting [91.56988987393483]
  We present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP. Specifically, we convert the original image-text matching problem in CLIP to a pixel-text matching problem and use the pixel-text score maps to guide the learning of dense prediction models. Our method is model-agnostic, which can be applied to arbitrary dense prediction systems and various pre-trained visual backbones.
  arXiv  Detail & Related papers  (2021-12-02T18:59:32Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.