3 papers
cs.CV2024
TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning
Joshua Feinglass, Yezhou Yang
Zero-shot inference, where pre-trained models perform tasks without specific training data, is an exciting emergent ability of large models like CLIP. Although there has been consi…
cs.CV2024
Grounding Stylistic Domain Generalization with Quantitative Domain Shift Measures and Synthetic Scene Images
Yiran Luo, Joshua Feinglass, Tejas Gokhale +3
Domain Generalization (DG) is a challenging task in machine learning that requires a coherent ability to comprehend shifts across various domains through extraction of domain-invar…
cs.CV2024
`Eyes of a Hawk and Ears of a Fox': Part Prototype Network for Generalized Zero-Shot Learning
Joshua Feinglass, Jayaraman J. Thiagarajan, Rushil Anirudh +2
Current approaches in Generalized Zero-Shot Learning (GZSL) are built upon base models which consider only a single class attribute vector representation over the entire image. Thi…