T
Ting Zhang
Researcher at Microsoft
Publications - 44
Citations - 3223
Ting Zhang is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & Nearest neighbor search. The author has an hindex of 15, co-authored 29 publications receiving 1850 citations. Previous affiliations of Ting Zhang include University of Science and Technology of China.
Papers
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Journal ArticleDOI
A Survey on Learning to Hash
TL;DR: In this paper, a comprehensive survey of the learning to hash algorithms is presented, categorizing them according to the manners of preserving the similarities into: pairwise similarity preserving, multi-wise similarity preservation, implicit similarity preserving and quantization, and discuss their relations.
Proceedings ArticleDOI
Face X-Ray for More General Face Forgery Detection
TL;DR: A novel image representation called face X-ray is proposed, which only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique, and can be trained without fake images generated by any of the state-of-the-art face manipulation methods.
Proceedings ArticleDOI
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
TL;DR: ProDA as mentioned in this paper aligns the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space and distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance.
Proceedings ArticleDOI
Interleaved Group Convolutions
TL;DR: This paper presents a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets), and discusses one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved.
Proceedings Article
Composite Quantization for Approximate Nearest Neighbor Search
Ting Zhang,Chao Du,Jingdong Wang +2 more
TL;DR: This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search, to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the indices of the selected elements.