SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning

• URL: http://arxiv.org/abs/2503.10252v1
• Date: Thu, 13 Mar 2025 10:59:51 GMT
• Title: SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning
• Authors: Zhi Chen, Zecheng Zhao, Jingcai Guo, Jingjing Li, Zi Huang,
• Abstract summary: We introduce semantically contextualized VIsual Patches (SVIP) for Zero-shot learning (ZSL)<n>We propose a self-supervised patch selection mechanism that preemptively learns to identify semantic-unrelated patches in the input space.<n>SVIP achieves state-of-the-art performance results while providing more interpretable and semantically rich feature representations.
• Score: 38.507994878183474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot learning (ZSL) aims to recognize unseen classes without labeled training examples by leveraging class-level semantic descriptors such as attributes. A fundamental challenge in ZSL is semantic misalignment, where semantic-unrelated information involved in visual features introduce ambiguity to visual-semantic interaction. Unlike existing methods that suppress semantic-unrelated information post hoc either in the feature space or the model space, we propose addressing this issue at the input stage, preventing semantic-unrelated patches from propagating through the network. To this end, we introduce Semantically contextualized VIsual Patches (SVIP) for ZSL, a transformer-based framework designed to enhance visual-semantic alignment. Specifically, we propose a self-supervised patch selection mechanism that preemptively learns to identify semantic-unrelated patches in the input space. This is trained with the supervision from aggregated attention scores across all transformer layers, which estimate each patch's semantic score. As removing semantic-unrelated patches from the input sequence may disrupt object structure, we replace them with learnable patch embeddings. With initialization from word embeddings, we can ensure they remain semantically meaningful throughout feature extraction. Extensive experiments on ZSL benchmarks demonstrate that SVIP achieves state-of-the-art performance results while providing more interpretable and semantically rich feature representations.

Related papers

• Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning [114.59476118365266]
  We propose AENet, which endows semantic information into the visual prompt to distill semantic-enhanced prompt for visual representation enrichment.<n> AENet comprises two key steps: 1) exploring the concept-harmonized tokens for the visual and attribute modalities, grounded on the modal-sharing token that represents consistent visual-semantic concepts; and 2) yielding semantic-enhanced prompt via the visual residual refinement unit with attribute consistency supervision.
  arXiv  Detail & Related papers  (2024-06-05T07:59:48Z)
• Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning [56.65891462413187]
  We propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT) ZSLViT first introduces semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement.
  arXiv  Detail & Related papers  (2024-04-11T12:59:38Z)
• Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation [13.001629605405954]
  We study universal zero-shot segmentation in this work to achieve panoptic, instance, and semantic segmentation for novel categories without any training samples. We introduce a generative model to synthesize features for unseen categories, which links semantic and visual spaces. The proposed approach achieves impressively state-of-the-art performance on zero-shot panoptic segmentation, instance segmentation, and semantic segmentation.
  arXiv  Detail & Related papers  (2023-06-19T17:59:16Z)
• Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning [74.48337375174297]
  Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain. We deploy the dual semantic-visual transformer module (DSVTM) to progressively model the correspondences between prototypes and visual features. DSVTM devises an instance-motivated semantic encoder that learns instance-centric prototypes to adapt to different images, enabling the recast of the unmatched semantic-visual pair into the matched one.
  arXiv  Detail & Related papers  (2023-03-27T15:21:43Z)
• Patch-level Representation Learning for Self-supervised Vision Transformers [68.8862419248863]
  Vision Transformers (ViTs) have gained much attention recently as a better architectural choice, often outperforming convolutional networks for various visual tasks. Inspired by this, we design a simple yet effective visual pretext task, coined SelfPatch, for learning better patch-level representations. We demonstrate that SelfPatch can significantly improve the performance of existing SSL methods for various visual tasks.
  arXiv  Detail & Related papers  (2022-06-16T08:01:19Z)
• What Remains of Visual Semantic Embeddings [0.618778092044887]
  We introduce the split of tiered-ImageNet to the ZSL task to avoid the structural flaws in the standard ImageNet benchmark. We build a unified framework for ZSL with contrastive learning as pre-training, which guarantees no semantic information leakage. Our work makes it fair for evaluating visual semantic embedding models on a ZSL setting in which semantic inference is decisive.
  arXiv  Detail & Related papers  (2021-07-26T06:55:11Z)
• Learning Robust Visual-semantic Mapping for Zero-shot Learning [8.299945169799795]
  We focus on fully empowering the semantic feature space, which is one of the key building blocks of Zero-shot learning (ZSL) In ZSL, the common practice is to train a mapping function between the visual and semantic feature spaces with labeled seen class examples. Under such a paradigm, the ZSL models may easily suffer from the domain shift problem when constructing and reusing the mapping function.
  arXiv  Detail & Related papers  (2021-04-12T17:39:38Z)
• Information Bottleneck Constrained Latent Bidirectional Embedding for Zero-Shot Learning [59.58381904522967]
  We propose a novel embedding based generative model with a tight visual-semantic coupling constraint. We learn a unified latent space that calibrates the embedded parametric distributions of both visual and semantic spaces. Our method can be easily extended to transductive ZSL setting by generating labels for unseen images.
  arXiv  Detail & Related papers  (2020-09-16T03:54:12Z)

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.