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Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation
Deepak Sridhar, Yi Li, Kartikeya Bhardwaj +7
Prompt learning is a popular method for adapting foundation models, but learned prompts are typically task-specific and fail to generalize to new classes, domains, or compositions…
MED-DSLC: Multi-Expert-Domain Classification via Domain Supervision and Logit Calibration
Zheng Zeng, Deepak Sridhar, Nuno Vasconcelos
Vision-language models (VLMs) such as CLIP enable zero-shot classification by comparing image features with text prompts in a shared embedding space. A fundamental property underly…
Video Reasoning without Training
Deepak Sridhar, Kartikeya Bhardwaj, Jeya Pradha Jeyaraj +3
Video reasoning using Large Multimodal Models (LMMs) relies on costly reinforcement learning (RL) and verbose chain-of-thought, resulting in substantial computational overhead duri…
Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis
Deepak Sridhar, Abhishek Peri, Rohith Rachala +1
Recent advances in generative modeling with diffusion processes (DPs) enabled breakthroughs in image synthesis. Despite impressive image quality, these models have various prompt c…
Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models
Deepak Sridhar, Nuno Vasconcelos
Diffusion models have recently surpassed GANs in image synthesis and editing, offering superior image quality and diversity. However, achieving precise control over attributes in g…
SCHEME: Scalable Channel Mixer for Vision Transformers
Deepak Sridhar, Yunsheng Li, Nuno Vasconcelos
Vision Transformers have achieved impressive performance in many vision tasks. While the token mixer or attention block has been studied in great detail, much less research has bee…