Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection

1 code implementation • ICCV 2023 • Boyang Li, Yingqian Wang, Longguang Wang, Fei Zhang, Ting Liu, Zaiping Lin, Wei An, Yulan Guo

The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.

Clustering

Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision

1 code implementation • CVPR 2023 • Xinyi Ying, Li Liu, Yingqian Wang, Ruojing Li, Nuo Chen, Zaiping Lin, Weidong Sheng, Shilin Zhou

Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels.

You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection

1 code implementation • 31 Mar 2023 • Zhaoxu Li, Yingqian Wang, Chao Xiao, Qiang Ling, Zaiping Lin, Wei An

Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way.

Anomaly Detection Model Selection

Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution

1 code implementation • 4 Jan 2022 • Xinyi Ying, Yingqian Wang, Longguang Wang, Weidong Sheng, Li Liu, Zaiping Lin, Shilin Zhou

Specifically, motivated by the local motion prior in the spatio-temporal dimension, we propose a local spatio-temporal attention module to perform implicit frame alignment and incorporate the local spatio-temporal information to enhance the local features (especially for small targets).

Super-Resolution

Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

1 code implementation • 25 Nov 2021 • Qian Yin, Qingyong Hu, Hao liu, Feng Zhang, Yingqian Wang, Zaiping Lin, Wei An, Yulan Guo

Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.

Matrix Completion Moving Object Detection +3

Dense Nested Attention Network for Infrared Small Target Detection

1 code implementation • 1 Jun 2021 • Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao Li, Wei An, Yulan Guo

With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained.

object-detection Object Detection

Parallax Attention for Unsupervised Stereo Correspondence Learning

1 code implementation • 16 Sep 2020 • Longguang Wang, Yulan Guo, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An

Based on our PAM, we propose a parallax-attention stereo matching network (PASMnet) and a parallax-attention stereo image super-resolution network (PASSRnet) for stereo matching and stereo image super-resolution tasks.

Stereo Image Super-Resolution Stereo Matching

Exploring Sparsity in Image Super-Resolution for Efficient Inference

1 code implementation • CVPR 2021 • Longguang Wang, Xiaoyu Dong, Yingqian Wang, Xinyi Ying, Zaiping Lin, Wei An, Yulan Guo

Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.

Image Super-Resolution

Learning A Single Network for Scale-Arbitrary Super-Resolution

2 code implementations • ICCV 2021 • Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo

In this paper, we propose to learn a scale-arbitrary image SR network from scale-specific networks.

Image Super-Resolution Transfer Learning

Deep Video Super-Resolution using HR Optical Flow Estimation

2 code implementations • 6 Jan 2020 • Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, Wei An

The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames.

Motion Compensation Optical Flow Estimation +1

Learning Parallax Attention for Stereo Image Super-Resolution

1 code implementation • CVPR 2019 • Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint.

Stereo Image Super-Resolution

Learning for Video Super-Resolution through HR Optical Flow Estimation

2 code implementations • 23 Sep 2018 • Longguang Wang, Yulan Guo, Zaiping Lin, Xinpu Deng, Wei An

Extensive experiments demonstrate that HR optical flows provide more accurate correspondences than their LR counterparts and improve both accuracy and consistency performance.

Motion Compensation Optical Flow Estimation +1

Fast single image super-resolution based on sigmoid transformation

no code implementations • 23 Aug 2017 • Longguang Wang, Zaiping Lin, Jinyan Gao, Xinpu Deng, Wei An

Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications.

Image Super-Resolution

Multi-frame image super-resolution with fast upscaling technique

no code implementations • 20 Jun 2017 • Longguang Wang, Zaiping Lin, Xinpu Deng, Wei An

In this paper, we propose an end-to-end fast upscaling technique to replace the interpolation operator, design upscaling filters in LR space for periodic sub-locations respectively and shuffle the filter results to derive the final reconstruction errors in HR space.

Image Super-Resolution