133 citations · 510 across the 36 of their papers we have counts for
12 papers · 1 filter
AutoBERT-Zero: Evolving BERT Backbone from Scratch
Jiahui Gao, Hang Xu, Han Shi +5
Transformer-based pre-trained language models like BERT and its variants have recently achieved promising performance in various natural language processing (NLP) tasks. However, t…
One Million Scenes for Autonomous Driving: ONCE Dataset
Jiageng Mao, Minzhe Niu, Chenhan Jiang +10
Current perception models in autonomous driving have become notorious for greatly relying on a mass of annotated data to cover unseen cases and address the long-tail problem. On th…
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
Lewei Yao, Renjie Pi, Hang Xu +3
We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead o…
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search
Yawen Duan, Xin Chen, Hang Xu +4
Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existin…
BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening
Wenqi Shao, Hang Yu, Zhaoyang Zhang +3
This work presents a probabilistic channel pruning method to accelerate Convolutional Neural Networks (CNNs). Previous pruning methods often zero out unimportant channels in traini…
Effective Sparsification of Neural Networks with Global Sparsity Constraint
Xiao Zhou, Weizhong Zhang, Hang Xu +1
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments. However, since magnitudes and relative impo…
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors
Jian Ding, Enze Xie, Hang Xu +4
Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learni…
Segmenting Transparent Object in the Wild with Transformer
Enze Xie, Wenjia Wang, Wenhai Wang +4
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation data…
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
Peidong Liu, Gengwei Zhang, Bochao Wang +4
Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models. For object detection, the well-establishe…
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
Kelly Kostopoulou, Hang Xu, Aritra Dutta +3
Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification proc…
SparseBERT: Rethinking the Importance Analysis in Self-attention
Han Shi, Jiahui Gao, Xiaozhe Ren +4
Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attent…
DetCo: Unsupervised Contrastive Learning for Object Detection
Enze Xie, Jian Ding, Wenhai Wang +5
Unsupervised contrastive learning achieves great success in learning image representations with CNN. Unlike most recent methods that focused on improving accuracy of image classifi…