6 papers
Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification
Zhiyuan Tao, Srikumar Sastry, Matthew J Thompson +9
Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories. However, in hierarchically structured label spaces, existing…
ActWorld: From Explorable to Interactive World Model via Action-Aware Memory
Zhexiao Xiong, Yizhi Song, Hao Kang +11
Interactive world models aim to simulate environment dynamics under real-time user actions. However, their action vocabulary is largely confined to navigation: most actions corresp…
Global and Local Entailment Learning for Natural World Imagery
Srikumar Sastry, Aayush Dhakal, Eric Xing +2
Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment le…
QuARI: Query Adaptive Retrieval Improvement
Eric Xing, Abby Stylianou, Robert Pless +1
Massive-scale pretraining has made vision-language models increasingly popular for image-to-image and text-to-image retrieval across a broad collection of domains. However, these m…
ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
Eric Xing, Pranavi Kolouju, Robert Pless +2
Composed image retrieval (CIR) is the task of retrieving a target image specified by a query image and a relative text that describes a semantic modification to the query image. Ex…
good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval
Pranavi Kolouju, Eric Xing, Robert Pless +2
Composed image retrieval (CIR) enables users to search images using a reference image combined with textual modifications. Recent advances in vision-language models have improved C…