913 citations · 2.7k across the 51 of their papers we have counts for
14 papers · 1 filter
AdaViT: Adaptive Tokens for Efficient Vision Transformer
Hongxu Yin, Arash Vahdat, Jose Alvarez +3
We introduce A-ViT, a method that adaptively adjusts the inference cost of vision transformer (ViT) for images of different complexity. A-ViT achieves this by automatically reducin…
Boosting Supervised Learning Performance with Co-training
Xinnan Du, William Zhang, Jose M. Alvarez
Deep learning perception models require a massive amount of labeled training data to achieve good performance. While unlabeled data is easy to acquire, the cost of labeling is proh…
When to Prune? A Policy towards Early Structural Pruning
Maying Shen, Pavlo Molchanov, Hongxu Yin +1
Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooki…
HALP: Hardware-Aware Latency Pruning
Maying Shen, Hongxu Yin, Pavlo Molchanov +3
Structural pruning can simplify network architecture and improve inference speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global re…
Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers
Zhiqi Li, Wenhai Wang, Enze Xie +5
Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present…
Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks
Xin Dong, Hongxu Yin, Jose M. Alvarez +3
Mobile edge devices see increased demands in deep neural networks (DNNs) inference while suffering from stringent constraints in computing resources. Split computing (SC) emerges a…