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Found 12 papers, 4 papers with code
Few-Shot Semantic Segmentation with Democratic Attention Networks
The crux of few-shot segmentation is to extract object information from the support image and then propagate it to guide the segmentation of query images.
Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark
Joint Super-Resolution and Inverse Tone-Mapping (joint SR-ITM) aims to increase the resolution and dynamic range of low-resolution and standard dynamic range images.
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift.
Conditional Variational Image Deraining
In this paper, we propose a Conditional Variational Image Deraining (CVID) network for better deraining performance, leveraging the exclusive generative ability of Conditional Variational Auto-Encoder (CVAE) on providing diverse predictions for the rainy image.
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 Network
In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps.
Two-Stream Multi-Task Network for Fashion Recognition
In this paper, we present a two-stream multi-task network for fashion recognition.
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.
In Defense of Single-column Networks for Crowd Counting
Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban planning, and intelligence gathering.
Direct Shape Regression Networks for End-to-End Face Alignment
In this paper, we propose the direct shape regression network (DSRN) for end-to-end face alignment by jointly handling the aforementioned challenges in a unified framework.
Ranked #16 on
Face Alignment
on AFLW-19
The Structure Transfer Machine Theory and Applications
Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown.
Learning Deep Match Kernels for Image-Set Classification
Image-set classification has recently generated great popularity due to its widespread applications in computer vision.
