activity
20152019
most citedSupervised Discrete Hashing

158 citations · 323 across the 10 of their papers we have counts for

collaborators

15 papers

cs.CV20191 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.CV20197 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…

cs.IR20193 cited

Exploring Auxiliary Context: Discrete Semantic Transfer Hashing for Scalable Image Retrieval

Lei Zhu, Zi Huang, Zhihui Li +2

Unsupervised hashing can desirably support scalable content-based image retrieval (SCBIR) for its appealing advantages of semantic label independence, memory and search efficiency.…

cs.CV201918 cited

A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition

Yanli Ji, Feixiang Xu, Yang Yang +3

Current researches of action recognition mainly focus on single-view and multi-view recognition, which can hardly satisfies the requirements of human-robot interaction (HRI) applic…

cs.CV201716 cited

From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning

Jingkuan Song, Yuyu Guo, Lianli Gao +3

Video captioning in essential is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, etc. In this paper we build…

cs.CV201736 cited

Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning

Jingkuan Song, Zhao Guo, Lianli Gao +3

Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every genera…