57 citations · 226 across the 36 of their papers we have counts for
12 papers · 1 filter
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism
Zhen Wang, Xinyun Jiang, Jun Xiao +2
Explicit Caption Editing (ECE) -- refining reference image captions through a sequence of explicit edit operations (e.g., KEEP, DETELE) -- has raised significant attention due to i…
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…
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation
Wenqing Wang, Kaifeng Gao, Yawei Luo +5
Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships. Due to th…
Improving Reference-based Distinctive Image Captioning with Contrastive Rewards
Yangjun Mao, Jun Xiao, Dong Zhang +4
Distinctive Image Captioning (DIC) -- generating distinctive captions that describe the unique details of a target image -- has received considerable attention over the last few ye…
TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding
Chenchi Zhang, Jun Xiao, Lei Chen +2
Prompt tuning has achieved great success in transferring the knowledge from large pretrained vision-language models into downstream tasks, and has dominated the performance on visu…
Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization
Feifei Shao, Yawei Luo, Lei Chen +4
Contemporary weakly-supervised object localization (WSOL) methods have primarily focused on addressing the challenge of localizing the most discriminative region while largely over…
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…
Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning
Wenqing Wang, Yawei Luo, Zhiqing Chen +4
Current video-based scene graph generation (VidSGG) methods have been found to perform poorly on predicting predicates that are less represented due to the inherent biased distribu…
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…
Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection
Kaifeng Gao, Long Chen, Hanwang Zhang +2
Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary predictions trained on limited base categories, e.g., object classification and detection.…
Knowledge-guided Causal Intervention for Weakly-supervised Object Localization
Feifei Shao, Yawei Luo, Fei Gao +2
Previous weakly-supervised object localization (WSOL) methods aim to expand activation map discriminative areas to cover the whole objects, yet neglect two inherent challenges when…