22 citations · 27 across the 12 of their papers we have counts for
13 papers
Rethinking Visual Token Compression for Video Large Language Models: A Simple Yet Strong Baseline
Xiao Zhang, Wang Zeng, Sheng Jin +3
Video Large Language Models (Video LLMs) have achieved remarkable progress in video understanding, but their inference efficiency is constrained by the large number of visual token…
Thinking with Cameras: Active Visual Reasoning via Dynamic Viewpoint Control for Surveillance Video Understanding
Xiao Zhang, Wang Zeng, Sheng Jin +3
Large vision-language models (LVLMs) have recently achieved remarkable progress in general-purpose video understanding. However, their application to real-world surveillance remain…
KptLLM++: Towards Generic Keypoint Comprehension with Large Language Model
Jie Yang, Wang Zeng, Sheng Jin +5
The emergence of Multimodal Large Language Models (MLLMs) has revolutionized image understanding by bridging textual and visual modalities. However, these models often struggle wit…
NADER: Neural Architecture Design via Multi-Agent Collaboration
Zekang Yang, Wang Zeng, Sheng Jin +3
Designing effective neural architectures poses a significant challenge in deep learning. While Neural Architecture Search (NAS) automates the search for optimal architectures, exis…
ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries
Wangyu Xue, Chen Qian, Jiayi Wu +5
Existing works on human-centric video understanding typically focus on analyzing specific moment or entire videos. However, many applications require higher precision at the frame…
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension
Jie Yang, Wang Zeng, Sheng Jin +4
Recent advancements in Multimodal Large Language Models (MLLMs) have greatly improved their abilities in image understanding. However, these models often struggle with grasping pix…
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
Tianfang Zhang, Lei Li, Yang Zhou +4
Vision Transformers (ViTs) mark a revolutionary advance in neural networks with their token mixer's powerful global context capability. However, the pairwise token affinity and com…
TCFormer: Visual Recognition via Token Clustering Transformer
Wang Zeng, Sheng Jin, Lumin Xu +5
Transformers are widely used in computer vision areas and have achieved remarkable success. Most state-of-the-art approaches split images into regular grids and represent each grid…
When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset
Yi Zhang, Wang Zeng, Sheng Jin +3
Recent years have witnessed increasing research attention towards pedestrian detection by taking the advantages of different sensor modalities (e.g. RGB, IR, Depth, LiDAR and Event…
UniFS: Universal Few-shot Instance Perception with Point Representations
Sheng Jin, Ruijie Yao, Lumin Xu +4
Instance perception tasks (object detection, instance segmentation, pose estimation, counting) play a key role in industrial applications of visual models. As supervised learning m…
DEMOS: Dynamic Environment Motion Synthesis in 3D Scenes via Local Spherical-BEV Perception
Jingyu Gong, Min Wang, Wentao Liu +4
Motion synthesis in real-world 3D scenes has recently attracted much attention. However, the static environment assumption made by most current methods usually cannot be satisfied…
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks
Zekang Yang, Wang Zeng, Sheng Jin +3
Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process. While traditional AutoML approaches have been succe…
You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception
Sheng Jin, Shuhuai Li, Tong Li +3
Human-centric perception (e.g. detection, segmentation, pose estimation, and attribute analysis) is a long-standing problem for computer vision. This paper introduces a unified and…