1 citations · 1 across the 13 of their papers we have counts for
13 papers
Remember Smarter: Visual History Compressor and Hyperbolic Experience Space for Robotic Memory
Dai Zhou, Jiexi Yan, Tong Li +2
Long-horizon robot policies require compact access to recent observations and reusable experience without expanding the vision-language-action (VLA) context. We introduce Remember…
Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation
Yujia Cai, Boxuan Li, Chenghao Xu +1
Query-based image retrieval (QBIR) requires retrieving relevant images given diverse and often stylistically heterogeneous queries, such as sketches, artworks, or low-resolution pr…
Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs
Guangtao Lyu, Qi Liu, Chenghao Xu +5
LVLMs have achieved strong multimodal reasoning capabilities but remain prone to hallucinations, producing outputs inconsistent with visual inputs or user instructions. Existing tr…
Beyond Global Alignment: Fine-Grained Motion-Language Retrieval via Pyramidal Shapley-Taylor Learning
Hanmo Chen, Guangtao Lyu, Chenghao Xu +3
As a foundational task in human-centric cross-modal intelligence, motion-language retrieval aims to bridge the semantic gap between natural language and human motion, enabling intu…
Towards Interpretable Hallucination Analysis and Mitigation in LVLMs via Contrastive Neuron Steering
Guangtao Lyu, Xinyi Cheng, Qi Liu +5
LVLMs achieve remarkable multimodal understanding and generation but remain susceptible to hallucinations. Existing mitigation methods predominantly focus on output-level adjustmen…
Towards Arbitrary Motion Completing via Hierarchical Continuous Representation
Chenghao Xu, Guangtao Lyu, Qi Liu +3
Physical motions are inherently continuous, and higher camera frame rates typically contribute to improved smoothness and temporal coherence. For the first time, we explore continu…
A Turn Toward Better Alignment: Few-Shot Generative Adaptation with Equivariant Feature Rotation
Chenghao Xu, Qi Liu, Jiexi Yan +2
Few-shot image generation aims to effectively adapt a source generative model to a target domain using very few training images. Most existing approaches introduce consistency cons…
Revealing Perception and Generation Dynamics in LVLMs: Mitigating Hallucinations via Validated Dominance Correction
Guangtao Lyu, Xinyi Cheng, Chenghao Xu +7
Large Vision-Language Models (LVLMs) have shown remarkable capabilities, yet hallucinations remain a persistent challenge. This work presents a systematic analysis of the internal…
Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation
Guangtao Lyu, Chenghao Xu, Qi Liu +4
Music to 3D dance generation aims to synthesize realistic and rhythmically synchronized human dance from music. While existing methods often rely on additional genre labels to furt…
AStF: Motion Style Transfer via Adaptive Statistics Fusor
Hanmo Chen, Chenghao Xu, Jiexi Yan +1
Human motion style transfer allows characters to appear less rigidity and more realism with specific style. Traditional arbitrary image style transfer typically process mean and va…
A Tale of Two Experts: Cooperative Learning for Source-Free Unsupervised Domain Adaptation
Jiaping Yu, Muli Yang, Jiapeng Ji +2
Source-Free Unsupervised Domain Adaptation (SFUDA) addresses the realistic challenge of adapting a source-trained model to a target domain without access to the source data, driven…
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels
Jiexi Yan, Lei Luo, Cheng Deng +1
Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to nois…
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Zhipeng Wang, Hao Wang, Jiexi Yan +2
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scena…