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20102023
most citedBeyond Product Quantization: Deep Progressive Quantization for Image Retrieval

538 citations · 2k across the 47 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.CV2019★ 4 cited

Cooperative Cross-Stream Network for Discriminative Action Representation

Jingran Zhang, Fumin Shen, Xing Xu +1

Spatial and temporal stream model has gained great success in video action recognition. Most existing works pay more attention to designing effective features fusion methods, which…

cs.CV2019★ 10 cited

Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking

Tan Wang, Xing Xu, Yang Yang +3

A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on…

cs.CV2019★ 538 cited

Deep Recurrent Quantization for Generating Sequential Binary Codes

Jingkuan Song, Xiaosu Zhu, Lianli Gao +3

Quantization has been an effective technology in ANN (approximate nearest neighbour) search due to its high accuracy and fast search speed. To meet the requirement of different app…

cs.CV2019★ 538 cited

Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval

Lianli Gao, Xiaosu Zhu, Jingkuan Song +2

Product Quantization (PQ) has long been a mainstream for generating an exponentially large codebook at very low memory/time cost. Despite its success, PQ is still tricky for the de…

cs.CV2019★ 1 cited

One-Shot Image-to-Image Translation via Part-Global Learning with a Multi-adversarial Framework

Ziqiang Zheng, Zhibin Yu, Haiyong Zheng +2

It is well known that humans can learn and recognize objects effectively from several limited image samples. However, learning from just a few images is still a tremendous challeng…

cs.CV2019★ 7 cited

Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables

Yan Xu, Baoyuan Wu, Fumin Shen +4

In this work, we study the robustness of a CNN+RNN based image captioning system being subjected to adversarial noises. We propose to fool an image captioning system to generate so…