Author

Paramananda Bhaskar

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#computer vision Preprint Jan 2026

PEST: Parameter Efficient Steering of Blackbox VLMs via Agentic Few-shot Alignment for Hateful Meme Moderation

In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene them before being posted - by applying a range of strategies built on top of generative AI models. To the best of our knowledge, explanation and intervention have typically been...

Naquee Rizwan, Subhankar Swain, Paramananda Bhaskar et al. · 4 citations

#artificial intelligence May 2026

FBHM: Functional Benchmarking and Steering of VLMs for Hateful Meme Detection

Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational - confounding rhetorical hate mechanisms with target community features and preventing causal evaluation of model vulnerabilities. To address this, we introduce FBHM, a systematically...

Paramananda Bhaskar, Naquee Rizwan, Daksh Jogchand et al. · arXiv.org · 0 citations