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Found 4 papers, 3 papers with code
Transformer-based Selective Super-Resolution for Efficient Image Refinement
Conventional super-resolution methods suffer from two drawbacks: substantial computational cost in upscaling an entire large image, and the introduction of extraneous or potentially detrimental information for downstream computer vision tasks during the refinement of the background.
Patch-based Selection and Refinement for Early Object Detection
Early object detection (OD) is a crucial task for the safety of many dynamic systems.
FedNS: Improving Federated Learning for collaborative image classification on mobile clients
Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model collaboratively with the help of a centralized server.
Weakly Supervised Deep Image Hashing through Tag Embeddings
We utilize the information contained in the user-generated tags associated with the images to learn the hash codes.
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