activity
20132026
most citedAligning where to see and what to tell: image caption with region-based attention and scene factorization

107 citations · 274 across the 28 of their papers we have counts for

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Showing 2018Show all

7 papers · 1 filter

cs.CV2018

Classifier and Exemplar Synthesis for Zero-Shot Learning

Soravit Changpinyo, Wei-Lun Chao, Boqing Gong +1

Zero-shot learning (ZSL) enables solving a task without the need to see its examples. In this paper, we propose two ZSL frameworks that learn to synthesize parameters for novel uns…

cs.CV2018

Cross-Modal and Hierarchical Modeling of Video and Text

Bowen Zhang, Hexiang Hu, Fei Sha

Visual data and text data are composed of information at multiple granularities. A video can describe a complex scene that is composed of multiple clips or shots, where each depict…

cs.LG2018

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Shariq Iqbal, Fei Sha

Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-cri…

cs.AI2018

Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner

Yury Zemlyanskiy, Fei Sha

There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction w…

cs.CL2018

Multi-Task Learning for Sequence Tagging: An Empirical Study

Soravit Changpinyo, Hexiang Hu, Fei Sha

We study three general multi-task learning (MTL) approaches on 11 sequence tagging tasks. Our extensive empirical results show that in about 50% of the cases, jointly learning all…

cs.CV2018

Cross-Dataset Adaptation for Visual Question Answering

Wei-Lun Chao, Hexiang Hu, Fei Sha

We investigate the problem of cross-dataset adaptation for visual question answering (Visual QA). Our goal is to train a Visual QA model on a source dataset but apply it to another…