TRIM-ReID: Duplication-Aware Token Reduction and Modality-Aligned Interaction for Multi-Modal Object Re-Identification

Wanke Xia; Ruiding Zhu; Xingguo Xu; Zhengbo Zhang; Dongxia Liu; Yuan Jin; Taojie Zhu; Yiting Zhao

cs.CVOct 3, 2026

TRIM-ReID: Duplication-Aware Token Reduction and Modality-Aligned Interaction for Multi-Modal Object Re-Identification

Authors: Wanke Xia, Ruiding Zhu, Xingguo Xu, Zhengbo Zhang, Dongxia Liu, Yuan Jin, Taojie Zhu, Yiting Zhao,

Organizations: Tsinghua University · Anhui University · Dalian University of Technology · CASIA ·

Abstract

Multi-modal object re-identification exploits complementary RGB, near-infrared (NIR), and thermal-infrared (TIR) observations to retrieve target objects. However, existing methods commonly employ visual encoders optimized for global image-text alignment and select tokens using learned importance scores. Such designs fail to preserve fine-grained identity cues or explicitly account for token redundancy, resulting in underrepresented local evidence and duplicated tokens that lead to noisy and costly cross-modal interaction. To address this gap, we propose TRIM-ReID, a compact framework that unifies dense feature extraction, intra-modal token reduction, and inter-modal aligned interaction. Specifically, semantically rich and spatially coherent patch features are extracted by Dense Identity Representation (DIR), which leverages DINOv3 to preserve fine-grained identity information. We then introduce Token Diversity Mining (TDM) to identify complementary local evidence and construct compact modality-specific token sets by suppressing repetitive patches while preserving informative diversity. Retained tokens are subsequently fused by Modal Relational Interaction (MRI) to enable effective information exchange across modalities, while a triangular alignment loss explicitly regularizes their joint relationships to maintain cross-modal semantic consistency under independent token selection. Extensive experiments on RGBNT201, RGBNT100, and MSVR310 demonstrate that TRIM-ReID achieves state-of-the-art performance.

Figures & tables

Figure 1: Overall framework of TRIM-ReID. It comprises three core components: DIR, TDM and MRI.

Figure 2: Visualization of attention maps and token reduction.

Methods	Venue	RGBNT201				RGBNT100		MSVR310
Methods	Venue	mAP	R-1	R-5	R-10	mAP	R-1	mAP	R-1
PFNet [ 16 ]	AAAI’21	38.5	38.9	52.0	58.4	68.1	94.1	23.5	37.4
TOP-ReID [ 8 ]	AAAI’24	72.3	76.6	84.7	89.4	81.2	96.4	35.9	44.6
EDITOR [ 13 ]	CVPR’24	66.5	68.3	81.1	88.2	82.1	96.4	39.0	49.3
LRMM [ 11 ]	ESWA’25	52.3	53.4	64.6	73.2	78.6	96.7	36.7	49.7
IDEA [ 9 ]	CVPR’25	80.2	82.1	90.0	93.3	87.2	96.5	47.0	62.4

Table 1: Performance comparison of various methods on the datasets of RGBNT201, RGBNT100, and MSVR310. The best and second results are in bold and underlined , respectively. Higher values indicate better performance for all metrics.

Variants	Modules			Metrics
Variants	DIR	TDM	MRI	mAP	R-1
A	$\checkmark$	$\times$	$\times$	76.4	79.1
B	$\checkmark$	$\checkmark$	$\times$	80.6	84.6
C	$\checkmark$	$\checkmark$	$\checkmark$	85.2	88.0

Table 2: Ablation on the proposed modules of TRIM-ReID.

Encoders	mAP	R-1	R-5	R-10
CLIP [ 4 ]	74.8	80.5	87.2	90.8
DINOv3 [ 5 ]	85.2	88.0	92.7	95.5

Table 3: Comparison of different visual encoders.

Number Setting	mAP	R-1	R-5	R-10
$N_{r}=32$	81.5	84.8	89.1	91.9
$N_{r}=48$	82.6	89.0	93.0	95.0
$N_{r}=64$	80.3	83.4	89.6	92.7
$N_{r}=80$	85.2	88.0	92.7	95.5
$N_{r}=96$	80.7	83.9	91.3	94.1
$N_{r}=112$	83.3	87.0	91.5	94.0

Table 4: Ablation on the number of retained tokens in TDM.

Number Setting	mAP	R-1	R-5	R-10
$N_{p}=2$	82.1	82.8	90.7	94.4
$N_{p}=4$	85.2	88.0	92.7	95.5
$N_{p}=8$	83.7	86.4	92.7	93.9

Table 5: Ablation on the number of pivot tokens in TDM.

Multimodal Fusion

Multimodal fusion combines information from different data types to improve prediction, perception, and decision-making.

See all 772 papers

STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification
Feb 28, 2026Xingguo Xu, Zhanyu Liu, Weixiang Zhou +5Cross-Modal Representation Learning
School of Mathematical Sciences, Dalian University of Technology, China · College of Computer Science and Technology, Zhejiang University, China · School of Future Technology, Dalian University of Technology, China +1
Multi-Modal Object Re-Identification with Prompt-S6 and Semantic-Aware Knowledge Guidance
Jul 26, 2026Weixiang Zhou, Jiabei Zuo, Yuhao Wang +3Cross-Modal Alignment Multimodal Fusion
School of Mathematical Sciences and the Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian University of Technology, Dalian, 116024, China · School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China · School of Future Technology, School of Artificial Intelligence, Dalian University of Technology, Dalian, 116024, China +1
Multi-Modal Object Re-Identification with Dual Semantic Guidance and Global-Local Mutual Modulation
Jul 31, 2026Weixiang Zhou, Xingguo Xu, Yuhao Wang +4Cross-Modal Representation Learning Multimodal Robustness
School of Mathematical Sciences and the Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian University of Technology, Dalian, 116024, China · School of Future Technology, School of Artificial Intelligence, Dalian University of Technology, Dalian, 116024, China · Department of Radiology and Biomedical Imaging, University of California, San Francisco, 94107, USA +1

Feb 28, 2026J/K move · Enter open · S save

STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification

Xingguo Xu, Zhanyu Liu, Weixiang Zhou, Yuansheng Gao+4

School of Mathematical Sciences, Dalian University of Technology, China · College of Computer Science and Technology, Zhejiang University, China · School of Future Technology, Dalian University of Technology, China+1

Cross-Modal Representation Learning

Open paper AskPDF arXiv

Multi-modal object Re-Identification (ReID) aims to exploit complementary information from different modalities to retrieve specific objects. However, existing methods often rely on hard token filtering or simple fusion strategies, which can lead to the loss of discriminative cues and increased background interference. To address these challenges, we propose STMI, a novel multi-modal learning framework consisting of three key components: (1) Segmentation-Guided Feature Modulation (SFM) module leverages SAM-generated masks to enhance foreground representations and suppress background noise through learnable attention modulation; (2) Semantic Token Reallocation (STR) module employs learnable query tokens and an adaptive reallocation mechanism to extract compact and informative representations without discarding any tokens; (3) Cross-Modal Hypergraph Interaction (CHI) module constructs a unified hypergraph across modalities to capture high-order semantic relationships. Extensive experiments on public benchmarks (i.e., RGBNT201, RGBNT100, and MSVR310) demonstrate the effectiveness and robustness of our proposed STMI framework in multi-modal ReID scenarios.

Abstract

Multimodal Fusion

Explore similar work

STMI: Segmentation-Guided Token Modulation with Cross-Modal Hypergraph Interaction for Multi-Modal Object Re-Identification

Multi-Modal Object Re-Identification with Prompt-S6 and Semantic-Aware Knowledge Guidance

Multi-Modal Object Re-Identification with Dual Semantic Guidance and Global-Local Mutual Modulation