157 citations · 1.3k across the 97 of their papers we have counts for
24 papers · 1 filter
Weakly Supervised High-Fidelity Clothing Model Generation
Ruili Feng, Cheng Ma, Chengji Shen +5
The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprieta…
Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation
Zhipeng Huang, Zhizheng Zhang, Cuiling Lan +6
Unsupervised domain adaptive person re-identification (ReID) has been extensively investigated to mitigate the adverse effects of domain gaps. Those works assume the target domain…
Calibrated Feature Decomposition for Generalizable Person Re-Identification
Kecheng Zheng, Jiawei Liu, Wei Wu +2
Existing disentangled-based methods for generalizable person re-identification aim at directly disentangling person representations into domain-relevant interference and identity-r…
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
Yaya Shi, Xu Yang, Haiyang Xu +4
Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. However, they have some insuperable drawbacks, e.g., th…
Edge-featured Graph Neural Architecture Search
Shaofei Cai, Liang Li, Xinzhe Han +2
Graph neural networks (GNNs) have been successfully applied to learning representation on graphs in many relational tasks. Recently, researchers study neural architecture search (N…
Multi-Modulation Network for Audio-Visual Event Localization
Hao Wang, Zheng-Jun Zha, Liang Li +2
We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the…
Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +3
Occluded person re-identification (ReID) aims to match person images with occlusion. It is fundamentally challenging because of the serious occlusion which aggravates the misalignm…
Self-Supervised Visual Representations Learning by Contrastive Mask Prediction
Yucheng Zhao, Guangting Wang, Chong Luo +2
Advanced self-supervised visual representation learning methods rely on the instance discrimination (ID) pretext task. We point out that the ID task has an implicit semantic consis…
Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment
Heliang Zheng, Huan Yang, Jianlong Fu +2
An important scenario for image quality assessment (IQA) is to evaluate image restoration (IR) algorithms. The state-of-the-art approaches adopt a full-reference paradigm that comp…
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
Yucheng Zhao, Guangting Wang, Chuanxin Tang +3
Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision. Recently, Transformer and multi-layer perceptron (MLP)-based models…
Disentangle Your Dense Object Detector
Zehui Chen, Chenhongyi Yang, Qiaofei Li +3
Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding.…
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
Kai Zhu, Yang Cao, Wei Zhai +2
Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and…
MVT: Mask Vision Transformer for Facial Expression Recognition in the wild
Hanting Li, Mingzhe Sui, Feng Zhao +2
Facial Expression Recognition (FER) in the wild is an extremely challenging task in computer vision due to variant backgrounds, low-quality facial images, and the subjectiveness of…
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
Wen Wang, Yang Cao, Jing Zhang +4
Detection transformers have recently shown promising object detection results and attracted increasing attention. However, how to develop effective domain adaptation techniques to…
Low-Rank Subspaces in GANs
Jiapeng Zhu, Ruili Feng, Yujun Shen +4
The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich semantics within some subspaces. To identify these subspaces, researchers typically analyze…
Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification
Jiawei Liu, Zhipeng Huang, Kecheng Zheng +3
Although existing person re-identification (Re-ID) methods have shown impressive accuracy, most of them usually suffer from poor generalization on unseen target domain. Thus, gener…
BoundarySqueeze: Image Segmentation as Boundary Squeezing
Hao He, Xiangtai Li, Yibo Yang +5
This paper proposes a novel method for high-quality image segmentation of both objects and scenes. Inspired by the dilation and erosion operations in morphological image processing…
Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos
Jiawei Liu, Zheng-Jun Zha, Wei Wu +2
Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re-identification is to eff…
Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval
Rui Zhao, Kecheng Zheng, Zheng-Jun Zha +2
Cross-modal video-text retrieval, a challenging task in the field of vision and language, aims at retrieving corresponding instance giving sample from either modality. Existing app…
Group-aware Label Transfer for Domain Adaptive Person Re-identification
Kecheng Zheng, Wu Liu, Lingxiao He +3
Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any furt…
Rethinking Graph Neural Architecture Search from Message-passing
Shaofei Cai, Liang Li, Jincan Deng +4
Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore diffe…
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting
Shunxin Xu, Ke Sun, Dong Liu +2
The capability of image semantic segmentation may be deteriorated due to noisy input image, where image denoising prior to segmentation helps. Both image denoising and semantic seg…
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework
Yucheng Zhao, Dacheng Yin, Chong Luo +4
This paper presents a self-supervised learning framework, named MGF, for general-purpose speech representation learning. In the design of MGF, speech hierarchy is taken into consid…
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
Yizhou Zhou, Chong Luo, Xiaoyan Sun +2
Predicting future frames of video sequences is challenging due to the complex and stochastic nature of the problem. Video prediction methods based on variational auto-encoders (VAE…