1 citations · 3 across the 22 of their papers we have counts for
5 papers · 1 filter
Analyzing The Language of Visual Tokens
David M. Chan, Rodolfo Corona, Joonyong Park +3
With the introduction of transformer-based models for vision and language tasks, such as LLaVA and Chameleon, there has been renewed interest in the discrete tokenized representati…
CLAIR-A: Leveraging Large Language Models to Judge Audio Captions
Tsung-Han Wu, Joseph E. Gonzalez, Trevor Darrell +1
The Automated Audio Captioning (AAC) task asks models to generate natural language descriptions of an audio input. Evaluating these machine-generated audio captions is a complex ta…
Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
Tsung-Han Wu, Giscard Biamby, Jerome Quenum +4
Large Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest…
ALOHa: A New Measure for Hallucination in Captioning Models
Suzanne Petryk, David M. Chan, Anish Kachinthaya +4
Despite recent advances in multimodal pre-training for visual description, state-of-the-art models still produce captions containing errors, such as hallucinating objects not prese…
ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video
Kevin Cai, Chonghua Liu, David M. Chan
The Internet's wealth of content, with up to 60% published in English, starkly contrasts the global population, where only 18.8% are English speakers, and just 5.1% consider it the…