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Deep Attention-guided Adaptive Subsampling
Sharath M Shankaranarayana, Soumava Kumar Roy, Prasad Sudhakar +1
Although deep neural networks have provided impressive gains in performance, these improvements often come at the cost of increased computational complexity and expense. In many ca…
Label Sharing Incremental Learning Framework for Independent Multi-Label Segmentation Tasks
Deepa Anand, Bipul Das, Vyshnav Dangeti +5
In a setting where segmentation models have to be built for multiple datasets, each with its own corresponding label set, a straightforward way is to learn one model for every data…
Task-driven Prompt Evolution for Foundation Models
Rachana Sathish, Rahul Venkataramani, K S Shriram +1
Promptable foundation models, particularly Segment Anything Model (SAM), have emerged as a promising alternative to the traditional task-specific supervised learning for image segm…