Target Refocusing via Attention Redistribution for Open-Vocabulary Semantic Segmentation: An Explainability Perspective

• URL: http://arxiv.org/abs/2511.16170v1
• Date: Thu, 20 Nov 2025 09:16:33 GMT
• Title: Target Refocusing via Attention Redistribution for Open-Vocabulary Semantic Segmentation: An Explainability Perspective
• Authors: Jiahao Li, Yang Lu, Yachao Zhang, Yong Xie, Fangyong Wang, Yuan Xie, Yanyun Qu,
• Abstract summary: We propose a training-free approach that emulates human distraction-refocusing behavior to redirect attention from distraction tokens back to target regions.<n>Our method achieves SOTA performance on eight benchmarks while maintaining high inference efficiency.
• Score: 47.99651635870674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open-vocabulary semantic segmentation (OVSS) employs pixel-level vision-language alignment to associate category-related prompts with corresponding pixels. A key challenge is enhancing the multimodal dense prediction capability, specifically this pixel-level multimodal alignment. Although existing methods achieve promising results by leveraging CLIP's vision-language alignment, they rarely investigate the performance boundaries of CLIP for dense prediction from an interpretability mechanisms perspective. In this work, we systematically investigate CLIP's internal mechanisms and identify a critical phenomenon: analogous to human distraction, CLIP diverts significant attention resources from target regions to irrelevant tokens. Our analysis reveals that these tokens arise from dimension-specific over-activation; filtering them enhances CLIP's dense prediction performance. Consequently, we propose ReFocusing CLIP (RF-CLIP), a training-free approach that emulates human distraction-refocusing behavior to redirect attention from distraction tokens back to target regions, thereby refining CLIP's multimodal alignment granularity. Our method achieves SOTA performance on eight benchmarks while maintaining high inference efficiency.

Related papers

• Generalized Decoupled Learning for Enhancing Open-Vocabulary Dense Perception [71.26728044621458]
  DeCLIP is a novel framework that enhances CLIP by decoupling the self-attention module to obtain content'' and context'' features respectively.<n>It consistently achieves state-of-the-art performance across a broad spectrum of tasks, including 2D detection and segmentation, 3D instance segmentation, video instance segmentation, and 6D object pose estimation.
  arXiv  Detail & Related papers  (2025-08-15T06:43:51Z)
• ATAS: Any-to-Any Self-Distillation for Enhanced Open-Vocabulary Dense Prediction [7.353998772647553]
  Any-to-Any Self-Distillation (ATAS) is a novel approach that simultaneously enhances semantic coherence and fine-grained alignment.<n>ATAS achieves substantial performance gains on open-vocabulary object detection and semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2025-06-10T10:40:10Z)
• DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception [21.87721909270275]
  DeCLIP is a novel framework that enhances CLIP with content'' and context'' features respectively.<n>It significantly outperforms existing methods across multiple open-vocabulary dense prediction tasks.
  arXiv  Detail & Related papers  (2025-05-07T13:46:34Z)
• Refining CLIP's Spatial Awareness: A Visual-Centric Perspective [10.936397225984107]
  Contrastive Language-Image Pre-training excels in global alignment with language but exhibits limited sensitivity to spatial information.<n>Recent approaches have introduced Region-Language Alignment to enhance CLIP's performance in dense multimodal tasks.<n>We propose the Spatial Correlation Distillation (SCD) framework, which preserves CLIP's inherent spatial structure and mitigates the above degradation.
  arXiv  Detail & Related papers  (2025-04-03T07:04:56Z)
• Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning [81.02648336552421]
  We propose a Multi-Constraint Consistency Learning approach to facilitate the staged enhancement of the encoder and decoder.<n>Self-adaptive feature masking and noise injection are designed in an instance-specific manner to perturb the features for robust learning of the decoder.<n> Experimental results on Pascal VOC2012 and Cityscapes datasets demonstrate that our proposed MCCL achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-23T03:21:33Z)
• 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)
• ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference [32.852004564832455]
  We re-investigate the architecture of CLIP, and identify residual connections as the primary source of noise that degrades segmentation quality. We propose ClearCLIP, a novel approach that decomposes CLIP's representations to enhance open-vocabulary semantic segmentation.
  arXiv  Detail & Related papers  (2024-07-17T09:52:20Z)
• Open-Vocabulary Segmentation with Semantic-Assisted Calibration [68.41025728960176]
  We study open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with contextual prior of CLIP.<n>We present a Semantic-assisted CAlibration Network (SCAN) to achieve state-of-the-art performance on open-vocabulary segmentation benchmarks.
  arXiv  Detail & Related papers  (2023-12-07T07:00:09Z)
• Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning [61.902254546858465]
  Methods based on Contrastive Language-Image Pre-training have exhibited promising performance in few-shot adaptation tasks. We propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics.
  arXiv  Detail & Related papers  (2023-11-08T05:18:57Z)
• 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.