[CLS] Token is All You Need for Zero-Shot Semantic Segmentation

• URL: http://arxiv.org/abs/2304.06212v1
• Date: Thu, 13 Apr 2023 01:35:07 GMT
• Title: [CLS] Token is All You Need for Zero-Shot Semantic Segmentation
• Authors: Letian Wu, Wenyao Zhang, Tengping Jiang, Wankou Yang, Xin Jin, Wenjun Zeng
• Abstract summary: We propose an embarrassingly simple yet highly effective zero-shot semantic segmentation (ZS3) method, based on the pre-trained vision-language model CLIP. Specifically, we use the [text] token output from the text branch, as an auxiliary semantic prompt, to replace the navigation [text] token in shallow layers of the ViT-based visual encoder. Our proposed ZS3 method achieves a SOTA performance, and it is even comparable with those few-shot semantic segmentation methods.
• Score: 60.06653755695356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose an embarrassingly simple yet highly effective zero-shot semantic segmentation (ZS3) method, based on the pre-trained vision-language model CLIP. First, our study provides a couple of key discoveries: (i) the global tokens (a.k.a [CLS] tokens in Transformer) of the text branch in CLIP provide a powerful representation of semantic information and (ii) these text-side [CLS] tokens can be regarded as category priors to guide CLIP visual encoder pay more attention on the corresponding region of interest. Based on that, we build upon the CLIP model as a backbone which we extend with a One-Way [CLS] token navigation from text to the visual branch that enables zero-shot dense prediction, dubbed \textbf{ClsCLIP}. Specifically, we use the [CLS] token output from the text branch, as an auxiliary semantic prompt, to replace the [CLS] token in shallow layers of the ViT-based visual encoder. This one-way navigation embeds such global category prior earlier and thus promotes semantic segmentation. Furthermore, to better segment tiny objects in ZS3, we further enhance ClsCLIP with a local zoom-in strategy, which employs a region proposal pre-processing and we get ClsCLIP+. Extensive experiments demonstrate that our proposed ZS3 method achieves a SOTA performance, and it is even comparable with those few-shot semantic segmentation methods.

Related papers

• Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation [72.47110803885235]
  We introduce a novel framework named Cascade-CLIP for zero-shot semantic segmentation. Our framework achieves superior zero-shot performance on segmentation benchmarks like COCO-Stuff, Pascal-VOC, and Pascal-Context.
  arXiv  Detail & Related papers  (2024-06-02T08:32:51Z)
• 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)
• CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model [55.321010757641524]
  We introduce CLIP4STR, a simple yet effective STR method built upon image and text encoders of CLIP. We scale CLIP4STR in terms of the model size, pre-training data, and training data, achieving state-of-the-art performance on 11 STR benchmarks.
  arXiv  Detail & Related papers  (2023-05-23T12:51:20Z)
• CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation [19.208559353954833]
  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.
  arXiv  Detail & Related papers  (2022-12-16T06:23:59Z)
• Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning [82.70453633641466]
  We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss. We show that PACL is also applicable to image-level predictions and when used with a CLIP backbone, provides a general improvement in zero-shot classification accuracy.
  arXiv  Detail & Related papers  (2022-12-09T17:23:00Z)
• 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)
• SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation [26.079055078561986]
  We propose a CLIP-based model named SegCLIP for the topic of open-vocabulary segmentation. The main idea is to gather patches with learnable centers to semantic regions through training on text-image pairs. Experimental results show that our model achieves comparable or superior segmentation accuracy.
  arXiv  Detail & Related papers  (2022-11-27T12:38:52Z)
• 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.