167 citations · 236 across the 18 of their papers we have counts for
8 papers · 1 filter
Planning Paths through Occlusions in Urban Environments
Yutao Han, Youya Xia, Guo-Jun Qi +1
This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navi…
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
Wenjie Li, Juncheng Li, Guangwei Gao +4
Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss…
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future
Guo-Jun Qi, Mubarak Shah
In this paper, we review adversarial pretraining of self-supervised deep networks including both convolutional neural networks and vision transformers. Unlike the adversarial train…
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
Ziteng Cui, Yingying Zhu, Lin Gu +5
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the…
Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
Wenjie Li, Juncheng Li, Guangwei Gao +3
Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the ca…
FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER
Ce Zheng, Matias Mendieta, Taojiannan Yang +2
Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D human pose estimation (2D HPE), 3D human pose estimation (3D HPE), and huma…
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Guangwei Gao, Zixiang Xu, Juncheng Li +3
Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by jointly training with f…
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning
Xiao Wang, Yuhang Huang, Dan Zeng +1
As a representative self-supervised method, contrastive learning has achieved great successes in unsupervised training of representations. It trains an encoder by distinguishing po…