123 citations · 443 across the 24 of their papers we have counts for
24 papers
Zero-shot HOI Detection with MLLM-based Detector-agnostic Interaction Recognition
Shiyu Xuan, Dongkai Wang, Zechao Li +1
Zero-shot Human-object interaction (HOI) detection aims to locate humans and objects in images and recognize their interactions. While advances in open-vocabulary object detection…
Diff-MM: Exploring Pre-trained Text-to-Image Generation Model for Unified Multi-modal Object Tracking
Shiyu Xuan, Zechao Li, Jinhui Tang
Multi-modal object tracking integrates auxiliary modalities such as depth, thermal infrared, event flow, and language to provide additional information beyond RGB images, showing g…
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond
Meiqi Cao, Xiangbo Shu, Jiachao Zhang +3
Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current l…
Spatial Structure Constraints for Weakly Supervised Semantic Segmentation
Tao Chen, Yazhou Yao, Xingguo Huang +3
The image-level label has prevailed in weakly supervised semantic segmentation tasks due to its easy availability. Since image-level labels can only indicate the existence or absen…
MNet: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition
Hao Tang, Jun Liu, Shuanglin Yan +3
Due to the scarcity of manually annotated data required for fine-grained video understanding, few-shot fine-grained (FS-FG) action recognition has gained significant attention, wit…
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Jian Zhao, Jianan Li, Lei Jin +20
The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UA…
Exploring Effective Factors for Improving Visual In-Context Learning
Yanpeng Sun, Qiang Chen, Xiaofan Li +3
The In-Context Learning (ICL) is to understand a new task via a few demonstrations (aka. prompt) and predict new inputs without tuning the models. While it has been widely studied…
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection
Peng Xing, Hao Tang, Jinhui Tang +1
Knowledge Distillation-based Anomaly Detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions by contrasting the unique features extrac…
Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation
Peng Xing, Zechao Li
Reconstruction method based on the memory module for visual anomaly detection attempts to narrow the reconstruction error for normal samples while enlarging it for anomalous sample…
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection
Peng Xing, Yanpeng Sun, Zechao Li
Unsupervised anomaly detection is a challenging task in industrial applications since it is impracticable to collect sufficient anomalous samples. In this paper, a novel Self-Super…
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
Yanpeng Sun, Qiang Chen, Xiangyu He +7
Freezing the pre-trained backbone has become a standard paradigm to avoid overfitting in few-shot segmentation. In this paper, we rethink the paradigm and explore a new regime: {\e…
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost
Yanpeng Sun, Zechao Li
The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps (CAM) to generate pseudo masks as ground-truth. However, the existing methods…
CTNet: Context-based Tandem Network for Semantic Segmentation
Zechao Li, Yanpeng Sun, Jinhui Tang
Contextual information has been shown to be powerful for semantic segmentation. This work proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the spati…
NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal
Chongyi Li, Huazhu Fu, Runmin Cong +2
Retinal images have been widely used by clinicians for early diagnosis of ocular diseases. However, the quality of retinal images is often clinically unsatisfactory due to eye lesi…
Data-driven Meta-set Based Fine-Grained Visual Classification
Chuanyi Zhang, Yazhou Yao, Xiangbo Shu +3
Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, lea…
Face Super-Resolution Guided by 3D Facial Priors
Xiaobin Hu, Wenqi Ren, John LaMaster +5
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local app…
Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals
Lu Jin, Zechao Li, Jinhui Tang
Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep se…
Learning Dual Convolutional Neural Networks for Low-Level Vision
Jinshan Pan, Sifei Liu, Deqing Sun +8
In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and deha…
Deep Ordinal Hashing with Spatial Attention
Lu Jin, Xiangbo Shu, Kai Li +3
Hashing has attracted increasing research attentions in recent years due to its high efficiency of computation and storage in image retrieval. Recent works have demonstrated the su…
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Jinhui Tang, Xiangbo Shu, Zechao Li +2
Image retagging aims to improve tag quality of social images by refining their original tags or assigning new high-quality tags. Recent approaches simultaneously explore visual, us…
Hardware-Efficient Guided Image Filtering For Multi-Label Problem
Longquan Dai, Mengke Yuan, Zechao Li +2
The Guided Filter (GF) is well-known for its linear complexity. However, when filtering an image with an n-channel guidance, GF needs to invert an n x n matrix for each pixel. To t…
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification
Yu-Gang Jiang, Zuxuan Wu, Jinhui Tang +3
Videos are inherently multimodal. This paper studies the problem of how to fully exploit the abundant multimodal clues for improved video categorization. We introduce a hybrid deep…
Personalized Age Progression with Bi-level Aging Dictionary Learning
Xiangbo Shu, Jinhui Tang, Zechao Li +3
Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a pers…
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Xiangbo Shu
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition due to its ability of modeling the temporal in…