1.1k citations · 1.2k across the 24 of their papers we have counts for
24 papers
See, Say, and Segment: Teaching LMMs to Overcome False Premises
Tsung-Han Wu, Giscard Biamby, David Chan +5
Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply…
CLAIR: Evaluating Image Captions with Large Language Models
David Chan, Suzanne Petryk, Joseph E. Gonzalez +2
The evaluation of machine-generated image captions poses an interesting yet persistent challenge. Effective evaluation measures must consider numerous dimensions of similarity, inc…
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data
Vladislav Lialin, Stephen Rawls, David Chan +3
Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multi…
IC3: Image Captioning by Committee Consensus
David M. Chan, Austin Myers, Sudheendra Vijayanarasimhan +2
If you ask a human to describe an image, they might do so in a thousand different ways. Traditionally, image captioning models are trained to generate a single "best" (most like a…
Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition
David M. Chan, Shalini Ghosh, Ariya Rastrow +1
Despite improvements to the generalization performance of automated speech recognition (ASR) models, specializing ASR models for downstream tasks remains a challenging task, primar…
Distribution Aware Metrics for Conditional Natural Language Generation
David M Chan, Yiming Ni, David A Ross +3
Traditional automated metrics for evaluating conditional natural language generation use pairwise comparisons between a single generated text and the best-matching gold-standard gr…