79 citations · 440 across the 39 of their papers we have counts for
40 papers
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…
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…
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training
Sumanth Gurram, Andy Fang, David Chan +1
Generating representations of video data is of key importance in advancing the field of machine perception. Most current techniques rely on hand-annotated data, which can be diffic…
An Embedding-Dynamic Approach to Self-supervised Learning
Suhong Moon, Domas Buracas, Seunghyun Park +2
A number of recent self-supervised learning methods have shown impressive performance on image classification and other tasks. A somewhat bewildering variety of techniques have bee…
Towards Understanding How Machines Can Learn Causal Overhypotheses
Eliza Kosoy, David M. Chan, Adrian Liu +7
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature…
What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics
David M. Chan, Austin Myers, Sudheendra Vijayanarasimhan +3
While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major…