5 papers
Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners
Yunhan Wang, Eshika Khandelwal, Edson Araujo +3
Multimodal Large Language Models (MLLMs) have demonstrated remarkable abilities when analyzing images, yet translating these capabilities to few-shot image classification remains c…
AVRT: Audio-Visual Reasoning Transfer through Single-Modality Teachers
Edson Araujo, Saurabhchand Bhati, M. Jehanzeb Mirza +5
Recent advances in reasoning models have shown remarkable progress in text-based domains, but transferring those capabilities to multimodal settings, e.g., to allow reasoning over…
TTA-Vid: Generalized Test-Time Adaptation for Video Reasoning
Soumya Shamarao Jahagirdar, Edson Araujo, Anna Kukleva +7
Recent video reasoning models have shown strong results on temporal and multimodal understanding, yet they depend on large-scale supervised data and multi-stage training pipelines,…
Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
Andrew Rouditchenko, Saurabhchand Bhati, Edson Araujo +4
We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni, on an audio question answering dataset with the reinforcement learning method GRPO. This leads to new St…
CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
Edson Araujo, Andrew Rouditchenko, Yuan Gong +7
Recent advances in audio-visual learning have shown promising results in learning representations across modalities. However, most approaches rely on global audio representations t…