1 citations · 1 across the 6 of their papers we have counts for
6 papers
CMAP: Cross-Modal Adaptive Prompting for Multi-Domain Task-Incremental Learning
Sriram Mandalika
Multi-domain task-incremental learning requires a model to sequentially acquire knowledge across visually diverse domains without forgetting prior tasks, and without access to task…
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…
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…
Generalizable Vision-Language Few-Shot Adaptation with Predictive Prompts and Negative Learning
Sriram Mandalika
Few-shot adaptation of vision-language models remains fundamentally limited by how negative class signals are handled at inference. Existing methods apply uniform negative suppress…
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…
SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios
Sriram Mandalika, Athira Nambiar
Most of the sophisticated AI models utilize huge amounts of annotated data and heavy training to achieve high-end performance. However, there are certain challenges that hinder the…