Papers by Nishit Anand
EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding (2025.emnlp-main)
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Ashish Seth, Utkarsh Tyagi, Ramaneswaran Selvakumar, Nishit Anand, Sonal Kumar, Sreyan Ghosh, Ramani Duraiswami, Chirag Agarwal, Dinesh Manocha
| Challenge: | Multimodal Large Language Models excel at visual perception and reasoning in third-person and egocentric videos, but are prone to hallucinations, generating coherent yet inaccurate responses. |
| Approach: | They propose to use a benchmark to evaluate MLLM hallucinations in egocentric videos. |
| Outcome: | EGOILLUSION comprises 1,400 videos paired with 8,000 human-annotated open and closed-ended questions designed to trigger hallucinations in both visual and auditory cues in egocentric videos. |
MULTIVOX: A Benchmark for Evaluating Voice Assistants for Multimodal Interactions (2025.emnlp-main)
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Ramaneswaran Selvakumar, Ashish Seth, Nishit Anand, Utkarsh Tyagi, Sonal Kumar, Sreyan Ghosh, Dinesh Manocha
| Challenge: | omni models lack spoken dialogues, which is essential for assessing conversational and auditory capabilities of voice assistants. |
| Approach: | They propose a benchmark to evaluate the ability of voice assistants to integrate paralinguistic speech features into their models. |
| Outcome: | The multivox voice assistant benchmark evaluates the ability of models to integrate spoken and visual cues including paralinguistic speech features for truly multimodal understanding. |
FIGMA: Towards FIne-Grained Music retrievAl (2026.acl-long)
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| Challenge: | Existing music retrieval models fail to retrieve fine-grained musical attributes when using coarse semantic queries. |
| Approach: | They propose a multi-view contrastive architecture that captures high-level semantic context and fine-grained musical attributes within a unified representation space. |
| Outcome: | The proposed method outperforms existing CLAP-based music retrieval models on multiple benchmarks. |
Do Audio-Language Models Understand Linguistic Variations? (2025.naacl-short)
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Ramaneswaran Selvakumar, Sonal Kumar, Hemant Kumar Giri, Nishit Anand, Ashish Seth, Sreyan Ghosh, Dinesh Manocha
| Challenge: | Existing open-vocabulary audio language models struggle to generalize to linguistic variations in textual queries. |
| Approach: | They propose a novel technique to learn audio-language representations agnostic to linguistic variations by reformulating contrastive loss used in CLAP architectures. |
| Outcome: | The proposed approach improves the performance of the open-vocabulary audio language models by 0.8%-13% across benchmarks and enhances robustness to linguistic variation. |