63 citations · 248 across the 35 of their papers we have counts for
13 papers · 1 filter
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…
Sparse Universal Transformer
Shawn Tan, Yikang Shen, Zhenfang Chen +2
The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers. Empirical evidence shows that UTs have better compositional generalization…
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…
SALMON: Self-Alignment with Instructable Reward Models
Zhiqing Sun, Yikang Shen, Hongxin Zhang +5
Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI ag…
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.,…
ModuleFormer: Modularity Emerges from Mixture-of-Experts
Yikang Shen, Zheyu Zhang, Tianyou Cao +3
Large Language Models (LLMs) have achieved remarkable results. However, existing models are expensive to train and deploy, and it is also difficult to expand their knowledge beyond…
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
Zhiqing Sun, Yikang Shen, Qinhong Zhou +5
Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to alig…
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…
Planning with Large Language Models for Code Generation
Shun Zhang, Zhenfang Chen, Yikang Shen +3
Existing large language model-based code generation pipelines typically use beam search or sampling algorithms during the decoding process. Although the programs they generate achi…
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…