CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation

• URL: http://arxiv.org/abs/2411.13836v1
• Date: Thu, 21 Nov 2024 04:54:30 GMT
• Title: CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation
• Authors: Lin Sun, Jiale Cao, Jin Xie, Xiaoheng Jiang, Yanwei Pang,
• Abstract summary: Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on various image-level tasks. We present a novel hierarchical framework, named CLIPer, that hierarchically improves spatial representation of CLIP. Our proposed CLIPer achieves the state-of-the-art performance on seven segmentation datasets.
• Score: 45.036137066263386
• License:
• Abstract: Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on various image-level tasks, leading to the research to adapt CLIP for pixel-level open-vocabulary semantic segmentation without additional training. The key is to improve spatial representation of image-level CLIP, such as replacing self-attention map at last layer with self-self attention map or vision foundation model based attention map. In this paper, we present a novel hierarchical framework, named CLIPer, that hierarchically improves spatial representation of CLIP. The proposed CLIPer includes an early-layer fusion module and a fine-grained compensation module. We observe that, the embeddings and attention maps at early layers can preserve spatial structural information. Inspired by this, we design the early-layer fusion module to generate segmentation map with better spatial coherence. Afterwards, we employ a fine-grained compensation module to compensate the local details using the self-attention maps of diffusion model. We conduct the experiments on seven segmentation datasets. Our proposed CLIPer achieves the state-of-the-art performance on these datasets. For instance, using ViT-L, CLIPer has the mIoU of 69.8% and 43.3% on VOC and COCO Object, outperforming ProxyCLIP by 9.2% and 4.1% respectively.

Related papers

• Self-Calibrated CLIP for Training-Free Open-Vocabulary Segmentation [19.749490092520006]
  Self-Calibrated CLIP (SC-CLIP) is a training-free method that calibrates CLIP to produce finer-language representations. SC-CLIP boosts the performance of vanilla CLIP ViT-L/14 by 6.8 times.
  arXiv  Detail & Related papers  (2024-11-24T15:14:05Z)
• ResCLIP: Residual Attention for Training-free Dense Vision-language Inference [27.551367463011008]
  Cross-correlation of self-attention in CLIP's non-final layers also exhibits localization properties. We propose the Residual Cross-correlation Self-attention (RCS) module, which leverages the cross-correlation self-attention from intermediate layers to remold the attention in the final block. The RCS module effectively reorganizes spatial information, unleashing the localization potential within CLIP for dense vision-language inference.
  arXiv  Detail & Related papers  (2024-11-24T14:14:14Z)
• 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. These foundational models, especially CLIP, have accelerated research in open-vocabulary computer vision tasks. In this study, we enhance the semantic segmentation performance of CLIP by introducing new modules and modifications. Our training-free method, ITACLIP, outperforms current state-of-the-art approaches on segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-11-18T20:31:38Z)
• SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference [11.453253140479166]
  We enhance contrastive language-image pretraining's potential for semantic segmentation. By rethinking self-attention, we find that CLIP can adapt to dense prediction tasks. We replace the traditional self-attention block of CLIP vision encoder's last layer by our CSA module.
  arXiv  Detail & Related papers  (2023-12-04T03:18:46Z)
• CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement [65.47237619200442]
  Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. We augment CLIP training with task-specific vision models from model zoos to improve its visual representations. This simple setup shows substantial improvements of up to 16.3% across different vision tasks.
  arXiv  Detail & Related papers  (2023-10-21T20:20:13Z)
• CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction [67.43527289422978]
  We propose an approach named CLIPSelf, which adapts the image-level recognition ability of CLIP ViT to local image regions without needing any region-text pairs. We achieve new state-of-the-art performance on open-vocabulary object detection, semantic segmentation, and panoptic segmentation across various benchmarks.
  arXiv  Detail & Related papers  (2023-10-02T17:58:52Z)
• 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)
• CLIP2GAN: Towards Bridging Text with the Latent Space of GANs [128.47600914674985]
  We propose a novel framework, i.e., CLIP2GAN, by leveraging CLIP model and StyleGAN. The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input latent space of StyleGAN.
  arXiv  Detail & Related papers  (2022-11-28T04:07:17Z)
• Learning Where to Learn in Cross-View Self-Supervised Learning [54.14989750044489]
  Self-supervised learning (SSL) has made enormous progress and largely narrowed the gap with supervised ones. Current methods simply adopt uniform aggregation of pixels for embedding. We present a new approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial information of features.
  arXiv  Detail & Related papers  (2022-03-28T17:02:42Z)
• 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)

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.