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Found 5 papers, 1 papers with code
TinySAM: Pushing the Envelope for Efficient Segment Anything Model
Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods.
Data-Free Knowledge Distillation for Image Super-Resolution
Experiments on various datasets and architectures demonstrate that the proposed method is able to be utilized for effectively learning portable student networks without the original data, e. g., with 0. 16dB PSNR drop on Set5 for x2 super resolution.
A Survey on Visual Transformer
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism.
MTP: Multi-Task Pruning for Efficient Semantic Segmentation Networks
To identify the redundancy in segmentation networks, we present a multi-task channel pruning approach.
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm.
