157 citations · 1.3k across the 97 of their papers we have counts for
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Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification
Kecheng Zheng, Cuiling Lan, Wenjun Zeng +2
Many unsupervised domain adaptive (UDA) person re-identification (ReID) approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of do…
Temporal Attribute-Appearance Learning Network for Video-based Person Re-Identification
Jiawei Liu, Xierong Zhu, Zheng-Jun Zha
Video-based person re-identification aims to match a specific pedestrian in surveillance videos across different time and locations. Human attributes and appearance are complementa…
DeepFacePencil: Creating Face Images from Freehand Sketches
Yuhang Li, Xuejin Chen, Binxin Yang +3
In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches. Existing image-to-image translation methods require a large-scale dataset of…
Nighttime Dehazing with a Synthetic Benchmark
Jing Zhang, Yang Cao, Zheng-Jun Zha +1
Increasing the visibility of nighttime hazy images is challenging because of uneven illumination from active artificial light sources and haze absorbing/scattering. The absence of…
Learning to Discretely Compose Reasoning Module Networks for Video Captioning
Ganchao Tan, Daqing Liu, Meng Wang +1
Generating natural language descriptions for videos, i.e., video captioning, essentially requires step-by-step reasoning along the generation process. For example, to generate the…
On Noise Injection in Generative Adversarial Networks
Ruili Feng, Deli Zhao, Zhengjun Zha
Noise injection has been proved to be one of the key technique advances in generating high-fidelity images. Despite its successful usage in GANs, the mechanism of its validity is s…
Memory-Augmented Relation Network for Few-Shot Learning
Jun He, Richang Hong, Xueliang Liu +3
Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision…
Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos
Jiawei Liu, Zheng-Jun Zha, Xierong Zhu +1
Person re-identification aims at identifying a certain pedestrian across non-overlapping camera networks. Video-based re-identification approaches have gained significant attention…
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Dechao Meng, Liang Li, Xuejing Liu +6
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance dista…
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View
Yizhou Zhou, Xiaoyan Sun, Chong Luo +2
Despite the success in still image recognition, deep neural networks for spatiotemporal signal tasks (such as human action recognition in videos) still suffers from low efficacy an…
Stacked Convolutional Deep Encoding Network for Video-Text Retrieval
Rui Zhao, Kecheng Zheng, Zheng-jun Zha
Existing dominant approaches for cross-modal video-text retrieval task are to learn a joint embedding space to measure the cross-modal similarity. However, these methods rarely exp…
State-Relabeling Adversarial Active Learning
Beichen Zhang, Liang Li, Shijie Yang +3
Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversa…
ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection
Yuxin Wang, Hongtao Xie, Zhengjun Zha +3
Scene text detection has witnessed rapid development in recent years. However, there still exists two main challenges: 1) many methods suffer from false positives in their text rep…
Real-world Person Re-Identification via Degradation Invariance Learning
Yukun Huang, Zheng-Jun Zha, Xueyang Fu +2
Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e.g., low-resolution, weak illumination, blurring and adverse weather. On…
Multi-Objective Matrix Normalization for Fine-grained Visual Recognition
Shaobo Min, Hantao Yao, Hongtao Xie +2
Bilinear pooling achieves great success in fine-grained visual recognition (FGVC). Recent methods have shown that the matrix power normalization can stabilize the second-order info…
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
Shaobo Min, Hantao Yao, Hongtao Xie +3
Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual repre…
Iterative Context-Aware Graph Inference for Visual Dialog
Dan Guo, Hui Wang, Hanwang Zhang +2
Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inf…
Self-Supervised Tuning for Few-Shot Segmentation
Kai Zhu, Wei Zhai, Zheng-Jun Zha +1
Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. It is a challenging task since the dense prediction can only be achieved un…
Object Relational Graph with Teacher-Recommended Learning for Video Captioning
Ziqi Zhang, Yaya Shi, Chunfeng Yuan +4
Taking full advantage of the information from both vision and language is critical for the video captioning task. Existing models lack adequate visual representation due to the neg…
Convolutional Dictionary Pair Learning Network for Image Representation Learning
Zhao Zhang, Yulin Sun, Yang Wang +3
Both the Dictionary Learning (DL) and Convolutional Neural Networks (CNN) are powerful image representation learning systems based on different mechanisms and principles, however w…
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
Zhao Zhang, Zemin Tang, Yang Wang +4
Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Networks (DenseNet), have achieved great success for image representation by discovering deep hierarchical in…