activity
20162023
most citedThe Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

143 citations · 759 across the 44 of their papers we have counts for

collaborators
Showing 2020Show all

18 papers · 1 filter

cs.CV2020★ 10 cited

Object-Centric Diagnosis of Visual Reasoning

Jianwei Yang, Jiayuan Mao, Jiajun Wu +4

When answering questions about an image, it not only needs knowing what -- understanding the fine-grained contents (e.g., objects, relationships) in the image, but also telling why…

cs.LG2020

Augmenting Policy Learning with Routines Discovered from a Single Demonstration

Zelin Zhao, Chuang Gan, Jiajun Wu +2

Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers ro…

cs.CV2020★ 16 cited

RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning

Peihao Chen, Deng Huang, Dongliang He +5

We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such…

cs.CV2020★ 1 cited

Synthetic Training for Monocular Human Mesh Recovery

Yu Sun, Qian Bao, Wu Liu +4

Recovering 3D human mesh from monocular images is a popular topic in computer vision and has a wide range of applications. This paper aims to estimate 3D mesh of multiple body part…

cs.CL2020★ 1 cited

Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning

Xiaoxiao Guo, Mo Yu, Yupeng Gao +3

Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text g…

cs.CV2020★ 4 cited

Location-aware Graph Convolutional Networks for Video Question Answering

Deng Huang, Peihao Chen, Runhao Zeng +3

We addressed the challenging task of video question answering, which requires machines to answer questions about videos in a natural language form. Previous state-of-the-art method…