471 citations · 3.1k across the 59 of their papers we have counts for
16 papers · 1 filter
LVreID: Person Re-Identification with Long Sequence Videos
Jianing Li, Shiliang Zhang, Jingdong Wang +2
This paper mainly establishes a large-scale Long sequence Video database for person re-IDentification (LVreID).
Pseudo-positive regularization for deep person re-identification
Fuqing Zhu, Xiangwei Kong, Haiyan Fu +1
An intrinsic challenge of person re-identification (re-ID) is the annotation difficulty. This typically means 1) few training samples per identity, and 2) thus the lack of diversit…
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
Yifan Sun, Liang Zheng, Yi Yang +2
Employing part-level features for pedestrian image description offers fine-grained information and has been verified as beneficial for person retrieval in very recent literature. A…
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Longhui Wei, Shiliang Zhang, Wen Gao +1
Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the co…
Learning to Learn Image Classifiers with Visual Analogy
Linjun Zhou, Peng Cui, Shiqiang Yang +2
Humans are far better learners who can learn a new concept very fast with only a few samples compared with machines. The plausible mystery making the difference is two fundamental…
Pose-driven Deep Convolutional Model for Person Re-identification
Chi Su, Jianing Li, Shiliang Zhang +3
Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured…
EBoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network
Xiaobin Liu, Shiliang Zhang, Tiejun Huang +1
Traditional Bag-of-visual Words (BoWs) model is commonly generated with many steps including local feature extraction, codebook generation, and feature quantization, etc. Those ste…
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
Longhui Wei, Shiliang Zhang, Hantao Yao +2
The huge variance of human pose and the misalignment of detected human images significantly increase the difficulty of person Re-Identification (Re-ID). Moreover, efficient Re-ID s…
One-Shot Fine-Grained Instance Retrieval
Hantao Yao, Shiliang Zhang, Yongdong Zhang +2
Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real s…
Deep Representation Learning with Part Loss for Person Re-Identification
Hantao Yao, Shiliang Zhang, Yongdong Zhang +2
Learning discriminative representations for unseen person images is critical for person Re-Identification (ReID). Most of current approaches learn deep representations in classific…
Recent Advance in Content-based Image Retrieval: A Literature Survey
Wengang Zhou, Houqiang Li, Qi Tian
The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of vi…
Part-based Deep Hashing for Large-scale Person Re-identification
Fuqing Zhu, Xiangwei Kong, Liang Zheng +2
Large-scale is a trend in person re-identification (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discrimina…
Enhancing Person Re-identification in a Self-trained Subspace
Xun Yang, Meng Wang, Richang Hong +2
Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different c…
Scalable Person Re-identification on Supervised Smoothed Manifold
Song Bai, Xiang Bai, Qi Tian
Most existing person re-identification algorithms either extract robust visual features or learn discriminative metrics for person images. However, the underlying manifold which th…
SORT: Second-Order Response Transform for Visual Recognition
Yan Wang, Lingxi Xie, Chenxi Liu +3
In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks. We propose a novel approach named Second-Order Response Trans…
DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing
Hantao Yao, Feng Dai, Dongming Zhang +4
Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been re…