Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning

• URL: http://arxiv.org/abs/2406.12316v1
• Date: Tue, 18 Jun 2024 06:39:03 GMT
• Title: Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning
• Authors: Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, Meng Liu, Peng Wang,
• Abstract summary: We introduce the Modality-aware and Instance-aware Visual Prompts (MIP) network in our work. MIP is designed to effectively utilize both invariant and specific information for identification. Our proposed MIP performs better than most state-of-the-art methods.
• Score: 29.19130646630545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Visible-Infrared Person Re-identification (VI ReID) aims to match visible and infrared images of the same pedestrians across non-overlapped camera views. These two input modalities contain both invariant information, such as shape, and modality-specific details, such as color. An ideal model should utilize valuable information from both modalities during training for enhanced representational capability. However, the gap caused by modality-specific information poses substantial challenges for the VI ReID model to handle distinct modality inputs simultaneously. To address this, we introduce the Modality-aware and Instance-aware Visual Prompts (MIP) network in our work, designed to effectively utilize both invariant and specific information for identification. Specifically, our MIP model is built on the transformer architecture. In this model, we have designed a series of modality-specific prompts, which could enable our model to adapt to and make use of the specific information inherent in different modality inputs, thereby reducing the interference caused by the modality gap and achieving better identification. Besides, we also employ each pedestrian feature to construct a group of instance-specific prompts. These customized prompts are responsible for guiding our model to adapt to each pedestrian instance dynamically, thereby capturing identity-level discriminative clues for identification. Through extensive experiments on SYSU-MM01 and RegDB datasets, the effectiveness of both our designed modules is evaluated. Additionally, our proposed MIP performs better than most state-of-the-art methods.

Related papers

• Dynamic Identity-Guided Attention Network for Visible-Infrared Person Re-identification [17.285526655788274]
  Visible-infrared person re-identification (VI-ReID) aims to match people with the same identity between visible and infrared modalities. Existing methods generally try to bridge the cross-modal differences at image or feature level. We introduce a dynamic identity-guided attention network (DIAN) to mine identity-guided and modality-consistent embeddings.
  arXiv  Detail & Related papers  (2024-05-21T12:04:56Z)
• ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning [57.91881829308395]
  Identity-preserving text-to-image generation (ID-T2I) has received significant attention due to its wide range of application scenarios like AI portrait and advertising. We present textbfID-Aligner, a general feedback learning framework to enhance ID-T2I performance.
  arXiv  Detail & Related papers  (2024-04-23T18:41:56Z)
• DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion [82.2425759608975]
  Infrared-visible object detection aims to achieve robust even full-day object detection by fusing the complementary information of infrared and visible images. We propose a Dynamic Adaptive Multispectral Detection Transformer (DAMSDet) to address these two challenges. Experiments on four public datasets demonstrate significant improvements compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-01T07:03:27Z)
• Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification [43.05147831905626]
  We propose a novel Transferring Modality-Aware Pedestrian Attentive Learning (TMPA) model. TMPA focuses on the pedestrian regions to efficiently compensate for missing modality-specific features. experiments conducted on the benchmark SYSU-MM01 and RegDB datasets demonstrated the effectiveness of our proposed TMPA model.
  arXiv  Detail & Related papers  (2023-12-12T07:15:17Z)
• Modality Unifying Network for Visible-Infrared Person Re-Identification [24.186989535051623]
  Visible-infrared person re-identification (VI-ReID) is a challenging task due to large cross-modality discrepancies and intra-class variations. Existing methods mainly focus on learning modality-shared representations by embedding different modalities into the same feature space. We propose a novel Modality Unifying Network (MUN) to explore a robust auxiliary modality for VI-ReID.
  arXiv  Detail & Related papers  (2023-09-12T14:22:22Z)
• Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification [61.49219876388174]
  Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared feature space to reduce the modality discrepancy between visible and infrared modalities. We present a novel mutual information and modality consensus network, namely CMInfoNet, to extract modality-invariant identity features.
  arXiv  Detail & Related papers  (2023-08-29T06:55:42Z)
• Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification [90.39454748065558]
  Body shape is one of the significant modality-shared cues for VI-ReID. We propose shape-erased feature learning paradigm that decorrelates modality-shared features in two subspaces. Experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2023-04-09T10:22:10Z)
• Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification [78.08536797239893]
  We propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules. MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips. We show that MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.
  arXiv  Detail & Related papers  (2023-01-02T05:17:31Z)
• CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification [38.96033760300123]
  Cross-modality transformer-based method (CMTR) for visible-infrared person re-identification task. We design the novel modality embeddings, which are fused with token embeddings to encode modalities' information. Our proposed CMTR model's performance significantly surpasses existing outstanding CNN-based methods.
  arXiv  Detail & Related papers  (2021-10-18T03:12:59Z)
• Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition [86.31412529187243]
  Few-shot video recognition aims at learning new actions with only very few labeled samples. We propose a depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net.
  arXiv  Detail & Related papers  (2020-10-20T03:06:20Z)

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.