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20232026
most citedSAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

10 citations · 22 across the 31 of their papers we have counts for

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13 papers · 1 filter

cs.CV2025

UniHPR: Unified Human Pose Representation via Singular Value Contrastive Learning

Zhongyu Jiang, Wenhao Chai, Lei Li +3

In recent years, there has been a growing interest in developing effective alignment pipelines to generate unified representations from different modalities for multi-modal fusion…

cs.CL2025★ 1 cited

Attention Consistency for LLMs Explanation

Tian Lan, Jinyuan Xu, Xue He +2

Understanding the decision-making processes of large language models (LLMs) is essential for their trustworthy development and deployment. However, current interpretability methods…

cs.GR2025

Causal Reasoning Elicits Controllable 3D Scene Generation

Shen Chen, Ruiyu Zhao, Jiale Zhou +3

Existing 3D scene generation methods often struggle to model the complex logical dependencies and physical constraints between objects, limiting their ability to adapt to dynamic a…

cs.LG2025

Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy

Yunchuan Guan, Yu Liu, Ke Zhou +4

Meta-learning is a powerful paradigm for tackling few-shot tasks. However, recent studies indicate that models trained with the whole-class training strategy can achieve comparable…

cs.CV2025

Warehouse Spatial Question Answering with LLM Agent

Hsiang-Wei Huang, Jen-Hao Cheng, Kuang-Ming Chen +8

Spatial understanding has been a challenging task for existing Multi-modal Large Language Models~(MLLMs). Previous methods leverage large-scale MLLM finetuning to enhance MLLM's sp…

cs.LG2025

Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV Cache Compression

Kunjun Li, Zigeng Chen, Cheng-Yen Yang +1

Visual Autoregressive (VAR) modeling has garnered significant attention for its innovative next-scale prediction approach, which yields substantial improvements in efficiency, scal…

cs.CV2025

Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking

Cheng-Yen Yang, Hsiang-Wei Huang, Pyong-Kun Kim +5

We present an effective approach for adapting the Segment Anything Model 2 (SAM2) to the Visual Object Tracking (VOT) task. Our method leverages the powerful pre-trained capabiliti…

cs.CV2025

Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025

Kunjun Li, Cheng-Yen Yang, Hsiang-Wei Huang +1

This report introduces ReID-SAM, a novel model developed for the SkiTB Challenge that addresses the complexities of tracking skier appearance. Our approach integrates the SAMURAI t…

cs.LG2025

Gradient Imbalance in Direct Preference Optimization

Qinwei Ma, Jingzhe Shi, Can Jin +3

Direct Preference Optimization (DPO) has been proposed as a promising alternative to Proximal Policy Optimization (PPO) based Reinforcement Learning with Human Feedback (RLHF). How…

cs.AI2025

ChatMotion: A Multimodal Multi-Agent for Human Motion Analysis

Lei Li, Sen Jia, Jianhao Wang +4

Advancements in Multimodal Large Language Models (MLLMs) have improved human motion understanding. However, these models remain constrained by their "instruct-only" nature, lacking…

cs.CV2025

Bayesian Optimization for Controlled Image Editing via LLMs

Chengkun Cai, Haoliang Liu, Xu Zhao +6

In the rapidly evolving field of image generation, achieving precise control over generated content and maintaining semantic consistency remain significant limitations, particularl…

cs.LG2025

Learning to Learn Weight Generation via Local Consistency Diffusion

Yunchuan Guan, Yu Liu, Ke Zhou +3

Diffusion-based algorithms have emerged as promising techniques for weight generation. However, existing solutions are limited by two challenges: generalizability and local target…

cs.CV2025

PackDiT: Joint Human Motion and Text Generation via Mutual Prompting

Zhongyu Jiang, Wenhao Chai, Zhuoran Zhou +3

Human motion generation has advanced markedly with the advent of diffusion models. Most recent studies have concentrated on generating motion sequences based on text prompts, commo…