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Found 10 papers, 1 papers with code
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization
Finally, our method can identify an optimal SubNet through few-step gradient optimization and a simple post-processing procedure.
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism
Next, we reconstruct a dense model featuring a pruning-friendly weight distribution by reactivating pruned connections with sparse regularization.
Towards Scale-Aware Full Surround Monodepth with Transformers
Full surround monodepth (FSM) methods can learn from multiple camera views simultaneously in a self-supervised manner to predict the scale-aware depth, which is more practical for real-world applications in contrast to scale-ambiguous depth from a standalone monocular camera.
Sparse Laneformer
We analyze that dense anchors are not necessary for lane detection, and propose a transformer-based lane detection framework based on a sparse anchor mechanism.
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Our method also achieves state-of-the-art pruning performance on the vision transformer model.
Separated RoadTopoFormer
Previous works such as map learning and BEV lane detection neglect the connection relationship between lane instances, and traffic elements detection tasks usually neglect the relationship with lane lines.
Ranked #3 on
3D Lane Detection
on OpenLane-V2 val
FDViT: Improve the Hierarchical Architecture of Vision Transformer
In this paper, we propose FDViT to improve the hierarchical architecture of the vision transformer by using a flexible downsampling layer that is not limited to integer stride to smoothly reduce the sizes of the middle feature maps.
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
We hope that our work will stimulate research and lead to the development of a new generation of ML systems for XR use cases.
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy.
