406 citations · 1.8k across the 130 of their papers we have counts for
7 papers · 1 filter
VITON: An Image-based Virtual Try-on Network
Xintong Han, Zuxuan Wu, Zhe Wu +2
We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region…
BlockDrop: Dynamic Inference Paths in Residual Networks
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar +4
Very deep convolutional neural networks offer excellent recognition results, yet their computational expense limits their impact for many real-world applications. We introduce Bloc…
Automatic Spatially-aware Fashion Concept Discovery
Xintong Han, Zuxuan Wu, Phoenix X. Huang +5
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly mod…
Learning Fashion Compatibility with Bidirectional LSTMs
Xintong Han, Zuxuan Wu, Yu-Gang Jiang +1
The ubiquity of online fashion shopping demands effective recommendation services for customers. In this paper, we study two types of fashion recommendation: (i) suggesting an item…
Aggregating Frame-level Features for Large-Scale Video Classification
Shaoxiang Chen, Xi Wang, Yongyi Tang +3
This paper introduces the system we developed for the Google Cloud & YouTube-8M Video Understanding Challenge, which can be considered as a multi-label classification problem defin…
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification
Yu-Gang Jiang, Zuxuan Wu, Jinhui Tang +3
Videos are inherently multimodal. This paper studies the problem of how to fully exploit the abundant multimodal clues for improved video categorization. We introduce a hybrid deep…
Weakly-Supervised Spatial Context Networks
Zuxuan Wu, Larry S. Davis, Leonid Sigal
We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a…