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Mixed-precision Supernet Training from Vision Foundation Models using Low Rank Adapter
To tackle these challenges, first, we study the effective search space design for fine-tuning a VFM by comparing different operators (such as resolution, feature size, width, depth, and bit-widths) in terms of performance and BitOPs reduction.
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion
We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D.
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter
We address the challenge of training a large supernet for the object detection task, using a relatively small amount of training data.
n-hot: Efficient bit-level sparsity for powers-of-two neural network quantization
Moreover, we use a two-stage fine-tuning algorithm to recover the accuracy drop that is triggered by introducing the bit-level sparsity.
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