most citedTactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms

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cs.CV2026

Answer with Evidence: Consistency-Aware Grounded Visual Question Answering for Roadside Traffic Scenes

Runwei Guan, Rongsheng Hu, Shangshu Chen +10

Roadside traffic reasoning requires every free-form textual claim to be backed by visual evidence. Existing grounded multimodal large language models (MLLMs) frequently exhibit say…

cs.CV2026

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation

Xuanyu Liu, Zheng Fang, Hongyang He +2

Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA. However, pseudo-labels have mix…

cs.CV2026

Talk2Sensors: 3D Visual Grounding in Autonomous Driving via Sensor-Adaptive Physical Cue Matching

Runwei Guan, Di Tian, Ningwei Ouyang +9

As a key capability for embodied intelligence, 3D visual grounding (3DVG) has been predominantly studied in indoor scenes with RGB-D or point-cloud inputs, while existing outdoor e…

cs.CV2026

Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation

Runwei Guan, Shaofeng Liang, Jiacheng Weng +10

Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technolo…

cs.CV2026

Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval

Xianke Chen, Daizong Liu, Yushuo Lou +5

Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-interactive system to iteratively clarify and refine user intent through multi…

cs.CV2026

From Part to Whole: 3D Generative World Model with an Adaptive Structural Hierarchy

Bi'an Du, Daizong Liu, Pufan Li +1

Single-image 3D generation lies at the core of vision-to-graphics models in the real world. However, it remains a fundamental challenge to achieve reliable generalization across di…

cs.CV2026

Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective

Keke Tang, Xianheng Liu, Weilong Peng +5

Transferable adversarial attacks on point clouds remain challenging, as existing methods often rely on model-specific gradients or heuristics that limit generalization to unseen ar…

cs.CV2026

Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection

Runwei Guan, Jianan Liu, Shaofeng Liang +7

4D millimeter-wave (mmWave) radar has been widely adopted in autonomous driving and robot perception due to its low cost and all-weather robustness. However, point-cloud-based rada…

cs.CV2025

MMDrive: Interactive Scene Understanding Beyond Vision with Multi-representational Fusion

Minghui Hou, Wei-Hsing Huang, Shaofeng Liang +5

Vision-language models enable the understanding and reasoning of complex traffic scenarios through multi-source information fusion, establishing it as a core technology for autonom…

cs.CV2025

MILD: Multi-Layer Diffusion Strategy for Complex and Precise Multi-IP Aware Human Erasing

Jinghan Yu, Junhao Xiao, Zhiyuan Ma +6

Recent years have witnessed the success of diffusion models in image customization tasks. However, existing mask-guided human erasing methods still struggle in complex scenarios su…

cs.CV2025

Cognitive Disentanglement for Referring Multi-Object Tracking

Shaofeng Liang, Runwei Guan, Wangwang Lian +6

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tra…

cs.CV2025

Talk2PC: Enhancing 3D Visual Grounding through LiDAR and Radar Point Clouds Fusion for Autonomous Driving

Runwei Guan, Jianan Liu, Ningwei Ouyang +8

Embodied outdoor scene understanding forms the foundation for autonomous agents to perceive, analyze, and react to dynamic driving environments. However, existing 3D understanding…