63 citations · 248 across the 35 of their papers we have counts for
8 papers · 2 filters
GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs
Zhenfang Chen, Rui Sun, Wenjun Liu +2
Recent works have shown that Large Language Models (LLMs) could empower traditional neuro-symbolic models via programming capabilities to translate language into module description…
CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding
Junyan Li, Delin Chen, Yining Hong +4
A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make "infinite use of finite means". However, current large vision-language foundatio…
TextPSG: Panoptic Scene Graph Generation from Textual Descriptions
Chengyang Zhao, Yikang Shen, Zhenfang Chen +2
Panoptic Scene Graph has recently been proposed for comprehensive scene understanding. However, previous works adopt a fully-supervised learning manner, requiring large amounts of…
Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen +6
General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g.,…
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following
Mingyu Ding, Yan Xu, Zhenfang Chen +4
Humans, even at a very early age, can learn visual concepts and understand geometry and layout through active interaction with the environment, and generalize their compositions to…
Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention
Mingyu Ding, Yikang Shen, Lijie Fan +5
Humans possess a versatile mechanism for extracting structured representations of our visual world. When looking at an image, we can decompose the scene into entities and their par…
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…