3 papers
cs.CV2025
CoMAD: A Multiple-Teacher Self-Supervised Distillation Framework
Sriram Mandalika, Lalitha V
Numerous self-supervised learning paradigms, such as contrastive learning and masked image modeling, learn powerful representations from unlabeled data but are typically pretrained…
cs.CV2025
Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay
Sriram Mandalika, Harsha Vardhan, Athira Nambiar
Continual Learning entails progressively acquiring knowledge from new data while retaining previously acquired knowledge, thereby mitigating ``Catastrophic Forgetting'' in neural n…
cs.CV2025
PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario
Sriram Mandalika, Lalitha V, Athira Nambiar
Driving scene understanding is a critical real-world problem that involves interpreting and associating various elements of a driving environment, such as vehicles, pedestrians, an…