161 citations · 1k across the 45 of their papers we have counts for
45 papers
Hierarchical Matching and Reasoning for Multi-Query Image Retrieval
Zhong Ji, Zhihao Li, Yan Zhang +3
As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly fo…
Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks
Xiaohan Liu, Yanwei Pang, Xuebin Sun +4
Partial scan is a common approach to accelerate Magnetic Resonance Imaging (MRI) data acquisition in both 2D and 3D settings. However, accurately reconstructing images from partial…
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search
Jiabei Wang, Yanwei Pang, Jiale Cao +3
Weakly supervised person search aims to perform joint pedestrian detection and re-identification (re-id) with only person bounding-box annotations. Recently, the idea of contrastiv…
USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval
Yan Zhang, Zhong Ji, Di Wang +2
As a fundamental and challenging task in bridging language and vision domains, Image-Text Retrieval (ITR) aims at searching for the target instances that are semantically relevant…
Memorizing Complementation Network for Few-Shot Class-Incremental Learning
Zhong Ji, Zhishen Hou, Xiyao Liu +2
Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts continually with only a few samples, which is prone to suffer the catastrophic forgetting and overfitting…
Multi-scale Feature Aggregation for Crowd Counting
Xiaoheng Jiang, Xinyi Wu, Hisham Cholakkal +5
Convolutional Neural Network (CNN) based crowd counting methods have achieved promising results in the past few years. However, the scale variation problem is still a huge challeng…
Reinforced Pedestrian Attribute Recognition with Group Optimization Reward
Zhong Ji, Zhenfei Hu, Yaodong Wang +1
Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and att…
PSTR: End-to-End One-Step Person Search With Transformers
Jiale Cao, Yanwei Pang, Rao Muhammad Anwer +4
We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR com…
Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI
Xiaohan Liu, Yanwei Pang, Ruiqi Jin +2
Purpose: To introduce a dual-domain reconstruction network with V-Net and K-Net for accurate MR image reconstruction from undersampled k-space data. Methods: Most state-of-the-art…
Active Phase-Encode Selection for Slice-Specific Fast MR Scanning Using a Transformer-Based Deep Reinforcement Learning Framework
Yiming Liu, Yanwei Pang, Ruiqi Jin +1
Purpose: Long scan time in phase encoding for forming complete K-space matrices is a critical drawback of MRI, making patients uncomfortable and wasting important time for diagnosi…
Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition
Xiyao Liu, Zhong Ji, Yanwei Pang +1
The domain shift between the source and target domain is the main challenge in Cross-Domain Few-Shot Learning (CD-FSL). However, the target domain is absolutely unknown during the…
Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning
Zhong Ji, Zhishen Hou, Xiyao Liu +2
Semantic information provides intra-class consistency and inter-class discriminability beyond visual concepts, which has been employed in Few-Shot Learning (FSL) to achieve further…
Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection
Aqi Gao, Jiale Cao, Yanwei Pang
Pseudo-LiDAR based 3D object detectors have gained popularity due to their high accuracy. However, these methods need dense depth supervision and suffer from inferior speed. To sol…
TJU-DHD: A Diverse High-Resolution Dataset for Object Detection
Yanwei Pang, Jiale Cao, Yazhao Li +3
Vehicles, pedestrians, and riders are the most important and interesting objects for the perception modules of self-driving vehicles and video surveillance. However, the state-of-t…
From Handcrafted to Deep Features for Pedestrian Detection: A Survey
Jiale Cao, Yanwei Pang, Jin Xie +2
Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed…
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation
Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal +3
Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage met…
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
Haoran Wang, Ying Zhang, Zhong Ji +2
Image-text matching plays a central role in bridging vision and language. Most existing approaches only rely on the image-text instance pair to learn their representations, thereby…
Hierarchical Human Parsing with Typed Part-Relation Reasoning
Wenguan Wang, Hailong Zhu, Jifeng Dai +3
Human parsing is for pixel-wise human semantic understanding. As human bodies are underlying hierarchically structured, how to model human structures is the central theme in this t…
Learning Compositional Neural Information Fusion for Human Parsing
Wenguan Wang, Zhijie Zhang, Siyuan Qi +3
This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural infor…
NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection
Yazhao Li, Yanwei Pang, Jianbing Shen +2
Due to the advantages of real-time detection and improved performance, single-shot detectors have gained great attention recently. To solve the complex scale variations, single-sho…
PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection
Jin Xie, Yanwei Pang, Hisham Cholakkal +3
Detecting pedestrians, especially under heavy occlusions, is a challenging computer vision problem with numerous real-world applications. This paper introduces a novel approach, te…
Deep Contextual Attention for Human-Object Interaction Detection
Tiancai Wang, Rao Muhammad Anwer, Muhammad Haris Khan +4
Human-object interaction detection is an important and relatively new class of visual relationship detection tasks, essential for deeper scene understanding. Most existing approach…
Mask-Guided Attention Network for Occluded Pedestrian Detection
Yanwei Pang, Jin Xie, Muhammad Haris Khan +3
Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance…
A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification
Zhong Ji, Xuejie Yu, Yunlong Yu +2
Zero-Shot Classification (ZSC) equips the learned model with the ability to recognize the visual instances from the novel classes via constructing the interactions between the visu…