Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification

• URL: http://arxiv.org/abs/2605.19527v1
• Date: Tue, 19 May 2026 08:29:01 GMT
• Title: Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification
• Abstract summary: We propose a novel Dual Prompt Learning ReID (DPL-ReID) model for occluded person ReID.<n>It incorporates a Dual Prompt Learning (Dual-PL) strategy, which can utilize textual cues to capture complete pedestrian semantics.<n>We also design a Weighted Gated Feature Fusion (WGFF) method, which in corporates LSNet to capture global information and act as a feature-gating mechanism.
• Score: 9.153700778053691
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Occluded person re-identification focuses on matching partially visible pedestrians across multiple camera views. However, occlusions disrupt body-region cues, thereby complicating cross-view matching. Most person ReID methods built on pretrained vision-language models only focus on enhancing prompt-based feature learning while ignoring the semantic information of occluders. Based on the success of CLIP-ReID, we propose a novel Dual Prompt Learning ReID (DPL-ReID) model for occluded person ReID. It incorporates a Dual Prompt Learning (Dual-PL) strategy, which can utilize textual cues to capture complete pedestrian semantics and keep robustness against occlusion, and a Real-World Occlusion Augmentation (RWOA) method that realistically simulates occlusion scenarios encountered in real word to enrich occluded samples. In addition, we also design a Weighted Gated Feature Fusion (WGFF) method, which in corporates LSNet to capture global information and act as a feature-gating mechanism. This mechanism can effectively guide the CLIP visual encoder toward generating more comprehensive feature representations. Extensive experiments on several benchmark occluded ReID datasets show that our proposed DPL-ReID achieves the state-of-the art performance. The occlusion instance library are available at https://github.com/stone-qiao/DPL-ReID.

Related papers

• R2LED: Equipping Retrieval and Refinement in Lifelong User Modeling with Semantic IDs for CTR Prediction [23.668401664583758]
  We propose a novel paradigm that equips retrieval and refinement in Lifelong User Modeling with SEmantic IDs (R2LED)<n>First, we introduce a Multi-route Mixed Retrieval for the retrieval stage. On the other hand, a mixed retrieval mechanism is proposed to efficiently retrieve candidates from both collaborative and semantic views.<n>For refinement, we design a Bi-level Fusion Refinement, including a target-aware cross-attention for route-level fusion and a gate mechanism for SID-level fusion.
  arXiv  Detail & Related papers  (2026-02-06T11:27:20Z)
• Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion [31.189038928192648]
  Co2S is a semi-supervised RS segmentation framework that fuses priors from vision-language models and self-supervised models.<n>An explicit-implicit semantic co-guidance mechanism is introduced that utilizes text embeddings and learnable queries.<n>Experiments on six popular datasets demonstrate the superiority of the proposed method.
  arXiv  Detail & Related papers  (2025-12-28T18:24:19Z)
• From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models [81.43473418572567]
  Click-Through Rate (CTR) prediction is a core task in recommendation systems.<n>We propose a novel generative framework to address embedding dimensional collapse and information redundancy.<n>We show that SFG consistently mitigates embedding collapse and reduces information redundancy, while yielding substantial performance gains.
  arXiv  Detail & Related papers  (2025-12-16T03:17:18Z)
• Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification [81.3063589622217]
  Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets.
  arXiv  Detail & Related papers  (2025-09-15T05:10:43Z)
• Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID [82.12123628480371]
  Unsupervised person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations for model learning.<n>Previous methods unify pseudo-labels of cross-modality images through label association algorithms and then design contrastive learning framework for global feature learning.<n>We propose a Semantic-Aligned Learning with Collaborative Refinement (SALCR) framework, which builds up objective for specific fine-grained patterns emphasized by each modality.
  arXiv  Detail & Related papers  (2025-04-27T13:58:12Z)
• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• Robust Ensemble Person Re-Identification via Orthogonal Fusion with Occlusion Handling [4.431087385310259]
  Occlusion remains one of the major challenges in person reidentification (ReID) We propose a deep ensemble model that harnesses both CNN and Transformer architectures to generate robust feature representations.
  arXiv  Detail & Related papers  (2024-03-29T18:38:59Z)
• Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification [36.86516784815214]
  We propose a transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. To imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise. We also develop a Dual-Path Constraint Module (DPC) that can obtain preferable supervision information from holistic images.
  arXiv  Detail & Related papers  (2023-03-20T09:56:35Z)
• Feature Completion Transformer for Occluded Person Re-identification [25.159974510754992]
  Occluded person re-identification (Re-ID) is a challenging problem due to the destruction of occluders. We propose a Feature Completion Transformer (FCFormer) to implicitly complement the semantic information of occluded parts in the feature space. FCFormer achieves superior performance and outperforms the state-of-the-art methods by significant margins on occluded datasets.
  arXiv  Detail & Related papers  (2023-03-03T01:12:57Z)
• Triplet Contrastive Learning for Unsupervised Vehicle Re-identification [55.445358749042384]
  Part feature learning is a critical technology for fine semantic understanding in vehicle re-identification. We propose a novel Triplet Contrastive Learning framework (TCL) which leverages cluster features to bridge the part features and global features.
  arXiv  Detail & Related papers  (2023-01-23T15:52:12Z)
• Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning [8.015703163954639]
  Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects. Most existing methods utilize the off-the-shelf pose or parsing networks as pseudo labels, which are prone to error. We propose a novel Occlusion Correction Network (OCNet) that corrects features through relational-weight learning and obtains diverse and representative features without using external networks.
  arXiv  Detail & Related papers  (2022-12-09T07:48:47Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

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.