20 citations · 39 across the 20 of their papers we have counts for
20 papers
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
Xiaoshuang Chen, Zhongyi Sun, Ke Yan +2
Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classe…
Prompt Guided Transformer for Multi-Task Dense Prediction
Yuxiang Lu, Shalayiding Sirejiding, Yue Ding +2
Task-conditional architecture offers advantage in parameter efficiency but falls short in performance compared to state-of-the-art multi-decoder methods. How to trade off performan…
Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation
Deyi Ji, Feng Zhao, Hongtao Lu
Most existing ultra-high resolution (UHR) segmentation methods always struggle in the dilemma of balancing memory cost and local characterization accuracy, which are both taken int…
Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation
Tianyu Li, Subhankar Roy, Huayi Zhou +2
To overcome the domain gap between synthetic and real-world datasets, unsupervised domain adaptation methods have been proposed for semantic segmentation. Majority of the previous…
Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark
Deyi Ji, Feng Zhao, Hongtao Lu +2
With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-g…
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation
Deyi Ji, Haoran Wang, Mingyuan Tao +3
Existing knowledge distillation works for semantic segmentation mainly focus on transferring high-level contextual knowledge from teacher to student. However, low-level texture kno…
BPJDet: Extended Object Representation for Generic Body-Part Joint Detection
Huayi Zhou, Fei Jiang, Jiaxin Si +2
Detection of human body and its parts has been intensively studied. However, most of CNNs-based detectors are trained independently, making it difficult to associate detected parts…
DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles
Huayi Zhou, Fei Jiang, Hongtao Lu
Existing head pose estimation (HPE) mainly focuses on single person with pre-detected frontal heads, which limits their applications in real complex scenarios with multi-persons. W…
T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
Jianrong Zhang, Yangsong Zhang, Xiaodong Cun +5
In this work, we investigate a simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transfor…
An Intuitive and Unconstrained 2D Cube Representation for Simultaneous Head Detection and Pose Estimation
Huayi Zhou, Fei Jiang, Lili Xiong +1
Most recent head pose estimation (HPE) methods are dominated by the Euler angle representation. To avoid its inherent ambiguity problem of rotation labels, alternative quaternion-b…
Body-Part Joint Detection and Association via Extended Object Representation
Huayi Zhou, Fei Jiang, Hongtao Lu
The detection of human body and its related parts (e.g., face, head or hands) have been intensively studied and greatly improved since the breakthrough of deep CNNs. However, most…
SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection
Huayi Zhou, Fei Jiang, Hongtao Lu
Domain adaptive object detection (DAOD) aims to alleviate transfer performance degradation caused by the cross-domain discrepancy. However, most existing DAOD methods are dominated…
StuArt: Individualized Classroom Observation of Students with Automatic Behavior Recognition and Tracking
Huayi Zhou, Fei Jiang, Jiaxin Si +2
Each student matters, but it is hardly for instructors to observe all the students during the courses and provide helps to the needed ones immediately. In this paper, we present St…
Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation
Yangsong Zhang, Subhankar Roy, Hongtao Lu +2
In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unann…
Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding
Huayi Zhou, Fei Jiang, Jiaxin Si +1
Human body orientation estimation (HBOE) is widely applied into various applications, including robotics, surveillance, pedestrian analysis and autonomous driving. Although many ap…
Degradation-Guided Meta-Restoration Network for Blind Super-Resolution
Fuzhi Yang, Huan Yang, Yanhong Zeng +2
Blind super-resolution (SR) aims to recover high-quality visual textures from a low-resolution (LR) image, which is usually degraded by down-sampling blur kernels and additive nois…
RAFT-MSF: Self-Supervised Monocular Scene Flow using Recurrent Optimizer
Bayram Bayramli, Junhwa Hur, Hongtao Lu
Learning scene flow from a monocular camera still remains a challenging task due to its ill-posedness as well as lack of annotated data. Self-supervised methods demonstrate learnin…
Trimap-guided Feature Mining and Fusion Network for Natural Image Matting
Weihao Jiang, Dongdong Yu, Zhaozhi Xie +3
Utilizing trimap guidance and fusing multi-level features are two important issues for trimap-based matting with pixel-level prediction. To utilize trimap guidance, most existing a…
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform
Runyuan Cai, Yue Ding, Hongtao Lu
Single image super-resolution(SISR) is an ill-posed problem that aims to obtain high-resolution (HR) output from low-resolution (LR) input, during which extra high-frequency inform…
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
Zhe Xie, Chengxuan Liu, Yichi Zhang +3
Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism…