Search Results for author:
Found 8 papers, 5 papers with code
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space
Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models.
OvarNet: Towards Open-vocabulary Object Attribute Recognition
In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario.
Ranked #1 on
Open Vocabulary Attribute Detection
on OVAD benchmark
(using extra training data)
SwiftNet: Real-time Video Object Segmentation
In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77. 8% J &F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Horizontal-to-Vertical Video Conversion
Alongside the prevalence of mobile videos, the general public leans towards consuming vertical videos on hand-held devices.
Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking
In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT).
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks
In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps.
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network
In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps.
Model-free Tracking with Deep Appearance and Motion Features Integration
To overcome the object-centric information scarcity, both appearance and motion features are deeply integrated by the proposed AMNet, which is an end-to-end offline trained two-stream network.
