Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification

• URL: http://arxiv.org/abs/2403.10254v1
• Date: Fri, 15 Mar 2024 12:44:35 GMT
• Title: Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification
• Authors: Pingping Zhang, Yuhao Wang, Yang Liu, Zhengzheng Tu, Huchuan Lu,
• Abstract summary: We propose a novel learning framework named textbfEDITOR to select diverse tokens from vision Transformers for multi-modal object ReID. Our framework can generate more discriminative features for multi-modal object ReID.
• Score: 64.36210786350568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-modal object re-identification (ReID) faces great challenges in maintaining robustness within complex visual scenarios. In contrast, multi-modal object ReID utilizes complementary information from diverse modalities, showing great potentials for practical applications. However, previous methods may be easily affected by irrelevant backgrounds and usually ignore the modality gaps. To address above issues, we propose a novel learning framework named \textbf{EDITOR} to select diverse tokens from vision Transformers for multi-modal object ReID. We begin with a shared vision Transformer to extract tokenized features from different input modalities. Then, we introduce a Spatial-Frequency Token Selection (SFTS) module to adaptively select object-centric tokens with both spatial and frequency information. Afterwards, we employ a Hierarchical Masked Aggregation (HMA) module to facilitate feature interactions within and across modalities. Finally, to further reduce the effect of backgrounds, we propose a Background Consistency Constraint (BCC) and an Object-Centric Feature Refinement (OCFR). They are formulated as two new loss functions, which improve the feature discrimination with background suppression. As a result, our framework can generate more discriminative features for multi-modal object ReID. Extensive experiments on three multi-modal ReID benchmarks verify the effectiveness of our methods. The code is available at https://github.com/924973292/EDITOR.

Related papers

• Towards Text-Image Interleaved Retrieval [49.96332254241075]
  We introduce the text-image interleaved retrieval (TIIR) task, where the query and document are interleaved text-image sequences. We construct a TIIR benchmark based on naturally interleaved wikiHow tutorials, where a specific pipeline is designed to generate interleaved queries. We propose a novel Matryoshka Multimodal Embedder (MME), which compresses the number of visual tokens at different granularity.
  arXiv  Detail & Related papers  (2025-02-18T12:00:47Z)
• SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection [73.49799596304418]
  This paper introduces a new task called Multi-Modal datasets and Multi-Task Object Detection (M2Det) for remote sensing. It is designed to accurately detect horizontal or oriented objects from any sensor modality. This task poses challenges due to 1) the trade-offs involved in managing multi-modal modelling and 2) the complexities of multi-task optimization.
  arXiv  Detail & Related papers  (2024-12-30T02:47:51Z)
• MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt [60.10555128510744]
  Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trained models like CLIP have demonstrated impressive performance in traditional single-modal object ReID tasks. We introduce a novel framework called MambaPro for multi-modal object ReID.
  arXiv  Detail & Related papers  (2024-12-14T06:33:53Z)
• DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification [25.781336502845395]
  Multi-modal object ReIDentification aims to retrieve specific objects by combining complementary information from multiple modalities. We propose a novel feature learning framework called DeMo for multi-modal object ReID, which adaptively balances decoupled features using a mixture of experts.
  arXiv  Detail & Related papers  (2024-12-14T02:36:56Z)
• Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation [26.737971605928358]
  We propose an ID-free MultimOdal TOken Representation scheme named MOTOR. We first employ product quantization to discretize each item's multimodal features into discrete token IDs. We then interpret the token embeddings corresponding to these token IDs as implicit item features. The resulting representations can replace the original ID embeddings and transform the original multimodal recommender into ID-free system.
  arXiv  Detail & Related papers  (2024-10-25T03:06:10Z)
• PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification [73.64560354556498]
  Vision Transformer (ViT) tends to overfit on most distinct regions of training data, limiting its generalizability and attention to holistic object features. We present PartFormer, an innovative adaptation of ViT designed to overcome the limitations in object Re-ID tasks. Our framework significantly outperforms state-of-the-art by 2.4% mAP scores on the most challenging MSMT17 dataset.
  arXiv  Detail & Related papers  (2024-08-29T16:31:05Z)
• Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation [12.455034591553506]
  Multimodal Emotion Recognition in Conversation (MERC) can be applied to public opinion monitoring, intelligent dialogue robots, and other fields. Previous work ignored the inter-modal alignment process and the intra-modal noise information before multimodal fusion. We have developed a novel approach called Masked Graph Learning with Recursive Alignment (MGLRA) to tackle this problem.
  arXiv  Detail & Related papers  (2024-07-23T02:23:51Z)
• TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation [64.65950381870742]
  We propose a cyclic token permutation framework for multi-spectral object ReID, dubbled TOP-ReID. We also propose a Token Permutation Module (TPM) for cyclic multi-spectral feature aggregation. Our proposed framework can generate more discriminative multi-spectral features for robust object ReID.
  arXiv  Detail & Related papers  (2023-12-15T08:54:15Z)
• Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification [27.75907274034702]
  We propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. To reduce the negative effect of modality gaps, we first take the gray-scale images as an auxiliary modality and propose a progressive learning strategy. To cope with the problem of large intra-class differences and small inter-class differences, we propose a Discriminative Center Loss.
  arXiv  Detail & Related papers  (2022-12-01T02:20:16Z)

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.