34 citations · 53 across the 7 of their papers we have counts for
8 papers
Reading Recognition in the Wild
Charig Yang, Samiul Alam, Shakhrul Iman Siam +12
To enable egocentric contextual AI in always-on smart glasses, it is crucial to be able to keep a record of the user's interactions with the world, including during reading. In thi…
Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
Wei Bao, Mi Zhang, Tao Zhang +1
Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions.Today's…
Multiview Transformers for Video Recognition
Shen Yan, Xuehan Xiong, Anurag Arnab +4
Video understanding requires reasoning at multiple spatiotemporal resolutions -- from short fine-grained motions to events taking place over longer durations. Although transformer…
Multi-Scale Feature and Metric Learning for Relation Extraction
Mi Zhang, Tieyun Qian
Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical feature…
CATE: Computation-aware Neural Architecture Encoding with Transformers
Shen Yan, Kaiqiang Song, Fei Liu +1
Recent works (White et al., 2020a; Yan et al., 2020) demonstrate the importance of architecture encodings in Neural Architecture Search (NAS). These encodings encode either structu…
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
Mi Zhang, Faen Zhang, Nicholas D. Lane +5
The era of edge computing has arrived. Although the Internet is the backbone of edge computing, its true value lies at the intersection of gathering data from sensors and extractin…
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Shen Yan, Yu Zheng, Wei Ao +2
Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to…
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution
Taojiannan Yang, Sijie Zhu, Chen Chen +3
We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency tra…