Why Vision Language Models Ignore What They See with Munawar Hayat - #758
About this episode
Munawar Hayat discusses challenges and innovations in Vision-Language Models and generative AI.
In this episode, we’re joined by Munawar Hayat, researcher at Qualcomm AI Research, to discuss a series of papers presented at NeurIPS 2025 focusing on multimodal and generative AI. We dive into the persistent challenge of object hallucination in Vision-Language Models (VLMs), why models often discard visual information in favor of pre-trained language priors, and how his team used attention-guided alignment to enforce better visual grounding. We also explore a novel approach to generalized contrastive learning designed to solve complex, composed retrieval tasks—such as searching via combined text and image queries—without increasing inference costs. Finally, we cover the difficulties generative models face when rendering multiple human subjects, and the new "MultiHuman Testbench" his team created to measure and mitigate issues like identity leakage and attribute blending. Throughout the discussion, we examine how these innovations align with the need for efficient, on-device AI deployment. The complete show notes for this episode can be found at https://twimlai.com/go/758.
People in this episode
Host: Sam Charrington
Guest: Munawar Hayat
Topics covered
- Vision-Language Models
- multimodal AI
- generative AI
- object hallucination
- contrastive learning
- AI deployment
Keywords
- Vision-Language Models
- object hallucination
- multimodal AI
- generative models
- contrastive learning
- AI deployment
Mentioned in this episode
Organizations: Qualcomm AI Research
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