Prompt-optimized suffixes plus synthetic fine-tuning recover ~82% of knowledge that multimodal unlearning methods claim to erase from MLLMs.
Multimodal conversational ai: A survey of datasets and approaches
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
As humans, we experience the world with all our senses or modalities (sound, sight, touch, smell, and taste). We use these modalities, particularly sight and touch, to convey and interpret specific meanings. Multimodal expressions are central to conversations; a rich set of modalities amplify and often compensate for each other. A multimodal conversational AI system answers questions, fulfills tasks, and emulates human conversations by understanding and expressing itself via multiple modalities. This paper motivates, defines, and mathematically formulates the multimodal conversational research objective. We provide a taxonomy of research required to solve the objective: multimodal representation, fusion, alignment, translation, and co-learning. We survey state-of-the-art datasets and approaches for each research area and highlight their limiting assumptions. Finally, we identify multimodal co-learning as a promising direction for multimodal conversational AI research.
representative citing papers
ReVisiT refines LVLM output distributions during decoding by projecting selected vision tokens into text space via context-aware constrained divergence minimization.
citing papers explorer
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POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking
Prompt-optimized suffixes plus synthetic fine-tuning recover ~82% of knowledge that multimodal unlearning methods claim to erase from MLLMs.
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Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding
ReVisiT refines LVLM output distributions during decoding by projecting selected vision tokens into text space via context-aware constrained divergence minimization.