9 citations · 16 across the 5 of their papers we have counts for
5 papers
A Survey on Multimodal Benchmarks: In the Era of Large AI Models
Lin Li, Guikun Chen, Hanrong Shi +2
The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understan…
Compositional Zero-shot Learning via Progressive Language-based Observations
Lin Li, Guikun Chen, Zhen Wang +2
Compositional zero-shot learning aims to recognize unseen state-object compositions by leveraging known primitives (state and object) during training. However, effectively modeling…
Compositional Feature Augmentation for Unbiased Scene Graph Generation
Lin Li, Guikun Chen, Jun Xiao +3
Scene Graph Generation (SGG) aims to detect all the visual relation triplets \texttt{sub}, \texttt{pred}, \texttt{obj} in a given image. With the emergence of various advance…
Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models
Lin Li, Jun Xiao, Guikun Chen +3
Pretrained vision-language models, such as CLIP, have demonstrated strong generalization capabilities, making them promising tools in the realm of zero-shot visual recognition. Vis…
Decomposed Prototype Learning for Few-Shot Scene Graph Generation
Xingchen Li, Jun Xiao, Guikun Chen +4
Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Therefore, it is difficult to apply them to real-world appli…