Search Results for author:
Found 30 papers, 20 papers with code
Learning Discriminative Representations for Skeleton Based Action Recognition
Furthermore, FR Head could be imposed on different stages of GCNs to build a multi-level refinement for stronger supervision.
BiSVP: Building Footprint Extraction via Bidirectional Serialized Vertex Prediction
Extracting building footprints from remote sensing images has been attracting extensive attention recently.
An Empirical Study on Multi-Domain Robust Semantic Segmentation
How to effectively leverage the plentiful existing datasets to train a robust and high-performance model is of great significance for many practical applications.
Exploring Effective Knowledge Transfer for Few-shot Object Detection
For the high-shot regime, we propose to use the knowledge learned from ImageNet as guidance for the feature learning in the fine-tuning stage, which will implicitly align the distributions of the novel classes.
D$^{\bf{3}}$: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos
In this paper, the duplicate detection is newly and precisely defined as occlusion misreporting on the same athlete by multiple detection boxes in one frame.
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Self-training has shown great potential in semi-supervised learning.
SparseTT: Visual Tracking with Sparse Transformers
In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking.
PanFormer: a Transformer Based Model for Pan-sharpening
Pan-sharpening aims at producing a high-resolution (HR) multi-spectral (MS) image from a low-resolution (LR) multi-spectral (MS) image and its corresponding panchromatic (PAN) image acquired by a same satellite.
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast
Moreover, armed with our method, we increase the segmentation mIoU of EPS from 70. 8% to 73. 6%, achieving new state-of-the-art.
Ranked #3 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening
Most of existing methods fall into the supervised learning framework in which they down-sample the multi-spectral (MS) and panchromatic (PAN) images and regard the original MS images as ground truths to form training samples.
Visual Grounding with Transformers
In this paper, we propose a transformer based approach for visual grounding.
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes.
Ranked #1 on
Visual Object Tracking
on OTB-2015
MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images
Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected.
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks
To further lift the classification performance, in this work we propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node correlations and also effectively reduce graph size.
PGMAN: An Unsupervised Generative Multi-adversarial Network for Pan-sharpening
However, since there are no intended HR MS images as references for learning, almost all of the existing methods down-sample the MS and PAN images and regard the original MS images as targets to form a supervised setting for training.
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images
Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention.
ARM: A Confidence-Based Adversarial Reweighting Module for Coarse Semantic Segmentation
Experiments on standard datasets shows our ARM can bring consistent improvements for both coarse annotations and fine annotations.
Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method
Object counting, whose aim is to estimate the number of objects from a given image, is an important and challenging computation task.
Co-Saliency Detection with Co-Attention Fully Convolutional Network
Co-saliency detection aims to detect common salient objects from a group of relevant images.
CNN-based Density Estimation and Crowd Counting: A Survey
Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
TTDM: A Travel Time Difference Model for Next Location Prediction
In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations.
From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques
Traditional change detection methods usually follow the image differencing, change feature extraction and classification framework, and their performance is limited by such simple image domain differencing and also the hand-crafted features.
Counting dense objects in remote sensing images
Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied.
A Feasible Framework for Arbitrary-Shaped Scene Text Recognition
Deep learning based methods have achieved surprising progress in Scene Text Recognition (STR), one of classic problems in computer vision.
Pyramid Mask Text Detector
Scene text detection, an essential step of scene text recognition system, is to locate text instances in natural scene images automatically.
Ranked #1 on
Scene Text Detection
on ICDAR 2017 MLT
PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening
This paper addresses the problem of remote sensing image pan-sharpening from the perspective of generative adversarial learning.
Road Extraction by Deep Residual U-Net
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images.
Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks
Sentiment analysis is attracting more and more attentions and has become a very hot research topic due to its potential applications in personalized recommendation, opinion mining, etc.
Remote Sensing Image Fusion Based on Two-stream Fusion Network
Unlike previous CNN based methods that consider pan-sharpening as a super resolution problem and perform pan-sharpening in pixel level, the proposed TFNet aims to fuse PAN and MS images in feature level and reconstruct the pan-sharpened image from the fused features.
