22 citations · 39 across the 14 of their papers we have counts for
14 papers · 1 filter
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…
UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD
Jingyuan Chen, Sheng Jin, Haopeng Sun +2
Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models. However, CAD research typically studies tasks in…
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…
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…
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…
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…
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…