43 citations · 74 across the 16 of their papers we have counts for
18 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…
Harmonizing Visual Representations for Unified Multimodal Understanding and Generation
Size Wu, Wenwei Zhang, Lumin Xu +6
Unifying visual understanding and generation within a single multimodal framework remains a significant challenge, as the two inherently heterogeneous tasks require representations…
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…
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…
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…
F-LMM: Grounding Frozen Large Multimodal Models
Size Wu, Sheng Jin, Wenwei Zhang +4
Endowing Large Multimodal Models (LMMs) with visual grounding capability can significantly enhance AIs' understanding of the visual world and their interaction with humans. However…
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…
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…
CLIM: Contrastive Language-Image Mosaic for Region Representation
Size Wu, Wenwei Zhang, Lumin Xu +3
Detecting objects accurately from a large or open vocabulary necessitates the vision-language alignment on region representations. However, learning such a region-text alignment by…
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…
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
Size Wu, Wenwei Zhang, Lumin Xu +4
Open-vocabulary dense prediction tasks including object detection and image segmentation have been advanced by the success of Contrastive Language-Image Pre-training (CLIP). CLIP m…
ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild
Lumin Xu, Sheng Jin, Wentao Liu +4
This paper investigates the task of 2D whole-body human pose estimation, which aims to localize dense landmarks on the entire human body including body, feet, face, and hands. We p…
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
Lumin Xu, Yingda Guan, Sheng Jin +5
Human pose estimation has achieved significant progress in recent years. However, most of the recent methods focus on improving accuracy using complicated models and ignoring real-…
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks
Jiahang Wang, Sheng Jin, Wentao Liu +3
Human pose estimation is a fundamental yet challenging task in computer vision, which aims at localizing human anatomical keypoints. However, unlike human vision that is robust to…
TRB: A Novel Triplet Representation for Understanding 2D Human Body
Haodong Duan, KwanYee Lin, Sheng Jin +3
Human pose and shape are two important components of 2D human body. However, how to efficiently represent both of them in images is still an open question. In this paper, we propos…