CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2212.09506v3
• Date: Thu, 23 Mar 2023 03:18:12 GMT
• Title: CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
• Authors: Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, Xiaofei He
• Abstract summary: This paper explores the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES.
• Score: 19.208559353954833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) focus on confident regions. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://github.com/linyq2117/CLIP-ES.

Related papers

• PosSAM: Panoptic Open-vocabulary Segment Anything [58.72494640363136]
  PosSAM is an open-vocabulary panoptic segmentation model that unifies the strengths of the Segment Anything Model (SAM) with the vision-native CLIP model in an end-to-end framework. We introduce a Mask-Aware Selective Ensembling (MASE) algorithm that adaptively enhances the quality of generated masks and boosts the performance of open-vocabulary classification during inference for each image.
  arXiv  Detail & Related papers  (2024-03-14T17:55:03Z)
• Spectral Prompt Tuning:Unveiling Unseen Classes for Zero-Shot Semantic Segmentation [20.880942041889444]
  We propose SPT-SEG, a one-stage approach that improves CLIP's adaptability from image to pixel. Specifically, we introduce Spectral Prompt Tuning (SPT), incorporating spectral prompts into the CLIP visual encoder's shallow layers. We demonstrate the superiority of our method over state-of-the-art approaches, performing well across all classes and particularly excelling in handling unseen classes.
  arXiv  Detail & Related papers  (2023-12-20T04:27:13Z)
• 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)
• CLIP Is Also a Good Teacher: A New Learning Framework for Inductive Zero-shot Semantic Segmentation [6.181169909576527]
  Generalized Zero-shot Semantic aims to segment both seen and unseen categories only under the supervision of the seen ones. Existing methods adopt the large-scale Vision Language Models (VLMs) which obtain outstanding zero-shot performance. We propose CLIP-ZSS (Zero-shot Semantic), a training framework that enables any image encoder designed for closed-set segmentation applied in zero-shot and open-vocabulary tasks.
  arXiv  Detail & Related papers  (2023-10-03T09:33:47Z)
• Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond [0.0]
  We propose a visual and textual Prior Guided Mask Assemble Network (PGMA-Net) It employs a class-agnostic mask assembly process to alleviate the bias, and formulates diverse tasks into a unified manner by assembling the prior through affinity. It achieves new state-of-the-art results in the FSS task, with mIoU of $77.6$ on $textPASCAL-5i$ and $59.4$ on $textCOCO-20i$ in 1-shot scenario.
  arXiv  Detail & Related papers  (2023-08-15T02:46:49Z)
• TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation [53.974228542090046]
  Contrastive Language-Image Pre-training (CLIP) has recently shown great promise in pixel-level zero-shot learning tasks. Existing approaches utilizing CLIP's text and patch embeddings to generate semantic masks often misidentify input pixels from unseen classes. We propose TagCLIP (Trusty-aware guided CLIP) to address this issue.
  arXiv  Detail & Related papers  (2023-04-15T12:52:23Z)
• Side Adapter Network for Open-Vocabulary Semantic Segmentation [69.18441687386733]
  This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named Side Adapter Network (SAN) A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias. Our approach significantly outperforms other counterparts, with up to 18 times fewer trainable parameters and 19 times faster inference speed.
  arXiv  Detail & Related papers  (2023-02-23T18:58:28Z)
• ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation [35.60888272729273]
  Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme. While effective, such a scheme requires two image encoders, one for proposal generation and one for CLIP, leading to a complicated pipeline and high computational cost. We propose a simpler-and-efficient one-stage solution that directly extends CLIP's zero-shot prediction capability from image to pixel level.
  arXiv  Detail & Related papers  (2022-12-07T12:05:00Z)
• Learning to Compose Diversified Prompts for Image Emotion Classification [5.586293129420233]
  Contrastive Language-Image Pre-training (CLIP) represents the latest incarnation of pre-trained vision-language models. CLIP has recently shown its superior power on a wide range of downstream vision-language tasks like Visual Question Answering. We propose a general framework that shows how CLIP can be effectively applied to Image Emotion Classification.
  arXiv  Detail & Related papers  (2022-01-26T14:31:55Z)
• A Simple Baseline for Zero-shot Semantic Segmentation with Pre-trained Vision-language Model [61.58071099082296]
  It is unclear how to make zero-shot recognition working well on broader vision problems, such as object detection and semantic segmentation. In this paper, we target for zero-shot semantic segmentation, by building it on an off-the-shelf pre-trained vision-language model, i.e., CLIP. Our experimental results show that this simple framework surpasses previous state-of-the-arts by a large margin.
  arXiv  Detail & Related papers  (2021-12-29T18:56:18Z)
• DenseCLIP: Extract Free Dense Labels from CLIP [130.3830819077699]
  Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition. DenseCLIP+ surpasses SOTA transductive zero-shot semantic segmentation methods by large margins. Our finding suggests that DenseCLIP can serve as a new reliable source of supervision for dense prediction tasks.
  arXiv  Detail & Related papers  (2021-12-02T09:23:01Z)

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.