no code implementations • 26 Mar 2024 • Guikun Chen, Xia Li, Yi Yang, Wenguan Wang
In this work, we propose feature extraction with clustering (FEC), a conceptually elegant yet surprisingly ad-hoc interpretable neural clustering framework, which views feature extraction as a process of selecting representatives from data and thus automatically captures the underlying data distribution.
1 code implementation • 16 Jan 2024 • Zongxin Yang, Guikun Chen, Xiaodi Li, Wenguan Wang, Yi Yang
Recent LLM-driven visual agents mainly focus on solving image-based tasks, which limits their ability to understand dynamic scenes, making it far from real-life applications like guiding students in laboratory experiments and identifying their mistakes.
no code implementations • 8 Jan 2024 • Guikun Chen, Wenguan Wang
The survey concludes by identifying current challenges and suggesting potential avenues for future research in this domain.
no code implementations • 23 Nov 2023 • Lin Li, Guikun Chen, Jun Xiao, Long Chen
Compositional zero-shot learning aims to recognize unseen state-object compositions by leveraging known primitives (state and object) during training.
1 code implementation • ICCV 2023 • Lin Li, Guikun Chen, Jun Xiao, Yi Yang, Chunping Wang, Long Chen
Specifically, we first decompose each relation triplet feature into two components: intrinsic feature and extrinsic feature, which correspond to the intrinsic characteristics and extrinsic contexts of a relation triplet, respectively.
1 code implementation • NeurIPS 2023 • Lin Li, Jun Xiao, Guikun Chen, Jian Shao, Yueting Zhuang, Long Chen
To dynamically fuse different cues, we further introduce a chain-of-thought method that prompts LLMs to generate reasonable weights for different visual cues.
no code implementations • 20 Mar 2023 • Xingchen Li, Long Chen, Guikun Chen, Yinfu Feng, Yi Yang, Jun Xiao
To this end, we propose a novel Decomposed Prototype Learning (DPL).