AI That Thinks Differently: Seeing, Understanding, and Trusting Visual Data in the Age of Foundation Models

Visual information is central to healthcare, agriculture, autonomous systems and communication, but remains expensive to encode, difficult to interpret in detail and vulnerable to manipulation. Manoranjan Paul presents research that combines domain knowledge, structural constraints and careful treatment of noisy observations to make visual AI dependable in practical settings.

Four connected directions organize the talk. Compact representations of video and three-dimensional data can support several downstream tasks without separate encodings. Vision-language methods connect image components with semantic descriptions under limited supervision. Efficient task-specific video representations support reasoning across space and time and video question answering. Structural evidence in generated images helps distinguish synthetic content from authentic images. The presentation draws on digital health, environmental sensing and intelligent systems, and closes with research challenges in integrated multimodal pipelines and emerging approaches to computing for imaging.