4 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
PANICL: Mitigating Over-Reliance on Single Prompt in Visual In-Context Learning
Jiahao Zhang, Bowen Wang, Hong Liu +2
Visual In-Context Learning (VICL) uses input-output image pairs, referred to as in-context pairs (or examples), as prompts alongside query images to guide models in performing dive…
E-InMeMo: Enhanced Prompting for Visual In-Context Learning
Jiahao Zhang, Bowen Wang, Hong Liu +3
Large-scale models trained on extensive datasets have become the standard due to their strong generalizability across diverse tasks. In-context learning (ICL), widely used in natur…
MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognition
Ryosuke Kawamura, Hideaki Hayashi, Noriko Takemura +1
Dynamic facial expression recognition (DFER) is an important task in the field of computer vision. To apply automatic DFER in practice, it is necessary to accurately recognize ambi…
Explainable Image Recognition via Enhanced Slot-attention Based Classifier
Bowen Wang, Liangzhi Li, Jiahao Zhang +2
The imperative to comprehend the behaviors of deep learning models is of utmost importance. In this realm, Explainable Artificial Intelligence (XAI) has emerged as a promising aven…
Instruct Me More! Random Prompting for Visual In-Context Learning
Jiahao Zhang, Bowen Wang, Liangzhi Li +2
Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across various tasks. In-context learning (ICL), a popul…
Learning Bottleneck Concepts in Image Classification
Bowen Wang, Liangzhi Li, Yuta Nakashima +1
Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel…