20 citations · 79 across the 25 of their papers we have counts for
11 papers · 1 filter
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation
Weimin Wu, Jiayuan Fan, Tao Chen +3
The linear ensemble based strategy, i.e., averaging ensemble, has been proposed to improve the performance in unsupervised domain adaptation tasks. However, a typical UDA task is u…
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing
Peng Ye, Shengji Tang, Baopu Li +2
Residual networks have shown great success and become indispensable in today's deep models. In this work, we aim to re-investigate the training process of residual networks from a…
ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
Haoran You, Zhanyi Sun, Huihong Shi +6
Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting t…
Effective Invertible Arbitrary Image Rescaling
Zhihong Pan, Baopu Li, Dongliang He +2
Great successes have been achieved using deep learning techniques for image super-resolution (SR) with fixed scales. To increase its real world applicability, numerous models have…
Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation
Peng Ye, Baopu Li, Tao Chen +6
Semantic segmentation is a popular research topic in computer vision, and many efforts have been made on it with impressive results. In this paper, we intend to search an optimal n…
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification
Bo Zhang, Jiakang Yuan, Baopu Li +3
Few-shot fine-grained learning aims to classify a query image into one of a set of support categories with fine-grained differences. Although learning different objects' local diff…
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning
Haoran You, Baopu Li, Zhanyi Sun +2
Neural architecture search (NAS) has demonstrated amazing success in searching for efficient deep neural networks (DNNs) from a given supernet. In parallel, the lottery ticket hypo…
ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
Haoran You, Baopu Li, Huihong Shi +2
Neural networks (NNs) with intensive multiplications (e.g., convolutions and transformers) are capable yet power hungry, impeding their more extensive deployment into resource-cons…
Towards Robust Adaptive Object Detection under Noisy Annotations
Xinyu Liu, Wuyang Li, Qiushi Yang +2
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate th…
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence
Zhihong Pan, Baopu Li, Dongliang He +5
Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and dow…
-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
Peng Ye, Baopu Li, Yikang Li +3
Neural Architecture Search~(NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural networks automatically. Among them, diffe…