Papers by Vishnu Prabhakaran
VADE: Visual Attention Guided Hallucination Detection and Elimination (2025.findings-acl)
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| Challenge: | Vision Language Models (VLMs) are prone to hallucinations, generating outputs that lack grounding in the actual visual data. |
| Approach: | They propose a sequence modelling approach to learn complex sequential patterns from transformer attention maps. |
| Outcome: | The proposed approach achieves an average PR-AUC of 80% in hallucination detection on M-HalDetect and an 5% improvement in hallucinosis mitigation on MSCOCO. |
MTIVE: Multi-Task Image Verification Engine Using Vision-Language Models for E-commerce (2026.acl-industry)
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| Challenge: | Vision-language models struggle with noisy real-world images and multi-task requirements. |
| Approach: | They propose a curriculum learning framework that adapts vision-language models through three stages . MTIVE uses frozen base weights with stacked LoRA adapters for shared domain knowledge . |
| Outcome: | MTIVE outperforms open-source and proprietary baselines in standard and continual learning settings. |
VIT-Pro: Visual Instruction Tuning for Product Images (2025.naacl-industry)
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Vishnu Prabhakaran, Purav Aggarwal, Vishruit Kulshreshtha, Arunita Das, Sahini Venkata Sitaram Sruti, Anoop Saladi
| Challenge: | general-purpose vision-language models struggle to understand and converse about real-world e-commerce product images. |
| Approach: | a new approach is proposed to use large-scale image-text pairs to train a generative VLM for e-commerce product images. |
| Outcome: | The proposed model outperforms general-purpose VLMs on multiple vision tasks in the e-commerce domain. |