15 citations · 49 across the 12 of their papers we have counts for
12 papers
3D Concept Learning and Reasoning from Multi-View Images
Yining Hong, Chunru Lin, Yilun Du +3
Humans are able to accurately reason in 3D by gathering multi-view observations of the surrounding world. Inspired by this insight, we introduce a new large-scale benchmark for 3D…
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning
Zhenfang Chen, Qinhong Zhou, Yikang Shen +3
Large pre-trained vision and language models have demonstrated remarkable capacities for various tasks. However, solving the knowledge-based visual reasoning tasks remains challeng…
3D Concept Grounding on Neural Fields
Yining Hong, Yilun Du, Chunru Lin +2
In this paper, we address the challenging problem of 3D concept grounding (i.e. segmenting and learning visual concepts) by looking at RGBD images and reasoning about paired questi…
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
Yining Hong, Kaichun Mo, Li Yi +4
This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D object…
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
Yining Hong, Li Yi, Joshua B. Tenenbaum +2
A critical aspect of human visual perception is the ability to parse visual scenes into individual objects and further into object parts, forming part-whole hierarchies. Such compo…
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks
Jinghui Qin, Xiaodan Liang, Yining Hong +2
Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasona…
VLGrammar: Grounded Grammar Induction of Vision and Language
Yining Hong, Qing Li, Song-Chun Zhu +1
Cognitive grammar suggests that the acquisition of language grammar is grounded within visual structures. While grammar is an essential representation of natural language, it also…
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
Qing Li, Siyuan Huang, Yining Hong +3
Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machi…
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Yining Hong, Qing Li, Ran Gong +3
Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical rea…
Learning by Fixing: Solving Math Word Problems with Weak Supervision
Yining Hong, Qing Li, Daniel Ciao +2
Previous neural solvers of math word problems (MWPs) are learned with full supervision and fail to generate diverse solutions. In this paper, we address this issue by introducing a…
A Competence-aware Curriculum for Visual Concepts Learning via Question Answering
Qing Li, Siyuan Huang, Yining Hong +1
Humans can progressively learn visual concepts from easy to hard questions. To mimic this efficient learning ability, we propose a competence-aware curriculum for visual concept le…
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Qing Li, Siyuan Huang, Yining Hong +3
The goal of neural-symbolic computation is to integrate the connectionist and symbolist paradigms. Prior methods learn the neural-symbolic models using reinforcement learning (RL)…