Summary of Amu-tuning: Effective Logit Bias For Clip-based Few-shot Learning, by Yuwei Tang et al.
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learningby Yuwei Tang, Zhenyi Lin, Qilong Wang, Pengfei…
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learningby Yuwei Tang, Zhenyi Lin, Qilong Wang, Pengfei…
MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypesby Bor-Shiun Wang, Chien-Yi Wang, Wei-Chen ChiuFirst submitted…
Augmenting Knowledge Graph Hierarchies Using Neural Transformersby Sanat Sharma, Mayank Poddar, Jayant Kumar, Kosta Blank,…
Flatness Improves Backbone Generalisation in Few-shot Classificationby Rui Li, Martin Trapp, Marcus Klasson, Arno SolinFirst…
Interactive Prompt Debugging with Sequence Salienceby Ian Tenney, Ryan Mullins, Bin Du, Shree Pandya, Minsuk…
MoReVQA: Exploring Modular Reasoning Models for Video Question Answeringby Juhong Min, Shyamal Buch, Arsha Nagrani,…
Heuristic-enhanced Candidates Selection strategy for GPTs tackle Few-Shot Aspect-Based Sentiment Analysisby Baoxing Jiang, Yujie Wan,…
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Modelsby Sebastian Bordt,…
Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Datasetby Chih-Chung Hsu,…
Hypothesis Generation with Large Language Modelsby Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava, Hongyuan Mei, Chenhao…