9 papers
FIGMA: Towards FIne-Grained Music retrievAl
Nishit Anand, Ashish Seth, Sreyan Ghosh +2
Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries. Wh…
Exploring Audio Hallucination in Egocentric Video Understanding
Ashish Seth, Xinhao Mei, Changsheng Zhao +9
Egocentric videos provide a distinctive setting in which sound serves as crucial cues to understand user activities and surroundings, particularly when visual information is unstab…
Audio Hallucination Attacks: Probing the Reliability of Large Audio Language Models
Ashish Seth, Sonal Kumar, Ramaneswaran Selvakumar +5
Large Audio Language Models (LALMs) achieve strong performance on audio-language tasks; however, their reliability in real-world settings remains underexplored. We introduce Audio…
EgoAVU: Egocentric Audio-Visual Understanding
Ashish Seth, Xinhao Mei, Changsheng Zhao +9
Understanding egocentric videos plays a vital role for embodied intelligence. Recent multi-modal large language models (MLLMs) can accept both visual and audio inputs. However, due…
MultiVox: A Benchmark for Evaluating Voice Assistants for Multimodal Interactions
Ramaneswaran Selvakumar, Ashish Seth, Nishit Anand +4
The rapid progress of Large Language Models (LLMs) has empowered omni models to act as voice assistants capable of understanding spoken dialogues. These models can process multimod…
EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding
Ashish Seth, Utkarsh Tyagi, Ramaneswaran Selvakumar +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in complex multimodal tasks. While MLLMs excel at visual perception and reasoning in third-person…