6.2k citations · 7k across the 18 of their papers we have counts for
5 papers · 1 filter
BloomVQA: Assessing Hierarchical Multi-modal Comprehension
Yunye Gong, Robik Shrestha, Jared Claypoole +4
We propose a novel VQA dataset, BloomVQA, to facilitate comprehensive evaluation of large vision-language models on comprehension tasks. Unlike current benchmarks that often focus…
A Video is Worth 10,000 Words: Training and Benchmarking with Diverse Captions for Better Long Video Retrieval
Matthew Gwilliam, Michael Cogswell, Meng Ye +3
Existing long video retrieval systems are trained and tested in the paragraph-to-video retrieval regime, where every long video is described by a single long paragraph. This neglec…
DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback
Yangyi Chen, Karan Sikka, Michael Cogswell +2
We present DRESS, a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models to enhance its alignment and interactio…
Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models
Yangyi Chen, Karan Sikka, Michael Cogswell +2
Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can parse natural queries about the visual content and generate human-like output…
Probing Conceptual Understanding of Large Visual-Language Models
Madeline Schiappa, Raiyaan Abdullah, Shehreen Azad +4
In recent years large visual-language (V+L) models have achieved great success in various downstream tasks. However, it is not well studied whether these models have a conceptual g…