5 citations · 12 across the 11 of their papers we have counts for
7 papers · 1 filter
Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training
Junlin Han, Shengbang Tong, David Fan +4
Large Language Models (LLMs), despite being trained on text alone, surprisingly develop rich visual priors. These priors allow latent visual capabilities to be unlocked for vision…
Controlling Multimodal LLMs via Reward-guided Decoding
Oscar Mañas, Pierluca D'Oro, Koustuv Sinha +3
As Multimodal Large Language Models (MLLMs) gain widespread applicability, it is becoming increasingly desirable to adapt them for diverse user needs. In this paper, we study the a…
A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Benno Krojer, Mojtaba Komeili, Candace Ross +4
Existing benchmarks for assessing the spatio-temporal understanding and reasoning abilities of video language models are susceptible to score inflation due to the presence of short…
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan +27
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-s…
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models
Aaron Foss, Chloe Evans, Sasha Mitts +3
We introduce CausalVQA, a benchmark dataset for video question answering (VQA) composed of question-answer pairs that probe models' understanding of causality in the physical world…
Multimodal Language Models as Text-to-Image Model Evaluators
Jiahui Chen, Candace Ross, Reyhane Askari-Hemmat +5
The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on static datasets, motivating researchers to…
Scaling Language-Free Visual Representation Learning
David Fan, Shengbang Tong, Jiachen Zhu +8
Visual Self-Supervised Learning (SSL) currently underperforms Contrastive Language-Image Pretraining (CLIP) in multimodal settings such as Visual Question Answering (VQA). This mul…