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Yuchen Guo
Researcher at Tsinghua University
Publications - 114
Citations - 3801
Yuchen Guo is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 23, co-authored 81 publications receiving 2489 citations.
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Proceedings ArticleDOI
Collective Matrix Factorization Hashing for Multimodal Data
TL;DR: This paper puts forward a novel hashing method, which is referred to Collective Matrix Factorization Hashing (CMFH), which learns unified hash codes by collective matrix factorization with latent factor model from different modalities of one instance, which can not only supports cross-view search but also increases the search accuracy by merging multiple view information sources.
Proceedings ArticleDOI
Latent semantic sparse hashing for cross-modal similarity search
TL;DR: A novel Latent Semantic Sparse Hashing (LSSH) is proposed to perform cross-modal similarity search by employing Sparse Coding and Matrix Factorization to capture the salient structures of images and learn the latent concepts from text.
Proceedings ArticleDOI
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
TL;DR: Asymmetric convolution block (ACB) as mentioned in this paper uses 1D asymmetric convolutions to strengthen the square convolution kernels, which can be trained to reach a higher level of accuracy.
Proceedings ArticleDOI
Transfer Sparse Coding for Robust Image Representation
TL;DR: This paper aims to minimize the distribution divergence between the labeled and unlabeled images, and incorporates this criterion into the objective function of sparse coding to make the new representations robust to the distribution difference.
Posted Content
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
TL;DR: Asymmetric Convolution Block (ACB), an architecture-neutral structure as a CNN building block, which uses 1D asymmetric convolutions to strengthen the square convolution kernels, which can improve the performance of various models on CIFAR and ImageNet by a clear margin.