37 citations · 39 across the 12 of their papers we have counts for
14 papers
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching
Bui Van Hung, Dang Thanh Vu, Hoang-Son Vo +3
The inherent variability of fMRI responses across trials encourages a generative view of the stimulus-to-response mapping. Prior generative encoders often operate in compressed lat…
Task-disentangled Low-Rank Adaptation for Versatile Audio-visual Multi-modal Learning Tasks within a Unified Framework
Hanyu Xuan, Mengqi Zhang, Junjun Mao +5
Inspired by human multi-modal perception, Audio-Visual Multi-Modal Learning (AVMML) integrates auditory and visual information to leverage complementary cross-modal cues, enabling…
Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition
Tingyi Liu, Kun Li, Fei Wang +5
In this paper, we present XInsight Lab's solution to the micro-gesture classification track of the 4th MiGA Challenge at IJCAI 2026, in which our solution ranked first and achieved…
3D Smoke Scene Reconstruction Guided by Vision Priors from Multimodal Large Language Models
Xinye Zheng, Fei Wang, Yiqi Nie +5
Reconstructing 3D scenes from smoke-degraded multi-view images is particularly difficult because smoke introduces strong scattering effects, view-dependent appearance changes, and…
MA-Bench: Towards Fine-grained Micro-Action Understanding
Kun Li, Jihao Gu, Fei Wang +3
With the rapid development of Multimodal Large Language Models (MLLMs), their potential in Micro-Action understanding, a vital role in human emotion analysis, remains unexplored du…
OSCAgent: Accelerating the Discovery of Organic Solar Cells with LLM Agents
Zhaolin Hu, Zhiliang Wu, Kun Li +2
Organic solar cells (OSCs) hold great promise for sustainable energy, but discovering high-performance materials is time-consuming and costly. Existing molecular generation methods…
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
Yixiao Zhou, Ziyu Zhao, Dongzhou Cheng +6
Sparse Mixture-of-Experts (SMoE) architectures are widely used in large language models (LLMs) due to their computational efficiency. However, though only a few experts are activat…
MM-Gesture: Towards Precise Micro-Gesture Recognition through Multimodal Fusion
Jihao Gu, Fei Wang, Kun Li +3
In this paper, we present MM-Gesture, the solution developed by our team HFUT-VUT, which ranked 1st in the micro-gesture classification track of the 3rd MiGA Challenge at IJCAI 202…
Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition
Jihao Gu, Kun Li, Fei Wang +4
Micro-Actions (MAs) are an important form of non-verbal communication in social interactions, with potential applications in human emotional analysis. However, existing methods in…
Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition
Fei Wang, Kun Li, Yiqi Nie +5
In this paper, we present our solution to the Cross-View Isolated Sign Language Recognition (CV-ISLR) challenge held at WWW 2025. CV-ISLR addresses a critical issue in traditional…
BVINet: Unlocking Blind Video Inpainting with Zero Annotations
Zhiliang Wu, Kerui Chen, Kun Li +2
Video inpainting aims to fill in corrupted regions of the video with plausible contents. Existing methods generally assume that the locations of corrupted regions are known, focusi…
Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
Kun Li, Dan Guo, Guoliang Chen +5
Micro-Action Recognition (MAR) has gained increasing attention due to its crucial role as a form of non-verbal communication in social interactions, with promising potential for ap…
Prototype Learning for Micro-gesture Classification
Guoliang Chen, Fei Wang, Kun Li +5
In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the track of Micro-gesture Classification in the MiGA challenge at IJCAI 2024. The task of mic…
Micro-gesture Online Recognition using Learnable Query Points
Pengyu Liu, Fei Wang, Kun Li +5
In this paper, we briefly introduce the solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track in the MiGA challenge at IJCAI 2024. The Micro-gest…