The automatic analysis of chemical literature has immense potential to
a...
Camouflaged object detection (COD) is the challenging task of identifyin...
Object localization in general environments is a fundamental part of vis...
While originally designed for image generation, diffusion models have
re...
The Segment Anything Model (SAM) has established itself as a powerful
ze...
We propose a discrete latent distribution for Generative Adversarial Net...
The recent advancement in Video Instance Segmentation (VIS) has largely ...
Pose-conditioned convolutional generative models struggle with high-qual...
Many important computer vision applications are naturally formulated as
...
Transfer learning based approaches have recently achieved promising resu...
Normalizing Flows (NFs) are flexible explicit generative models that hav...
One of the key factors behind the recent success in visual tracking is t...
While Video Instance Segmentation (VIS) has seen rapid progress, current...
Current multi-category Multiple Object Tracking (MOT) metrics use class
...
Positional encodings have enabled recent works to train a single adversa...
Optimization based tracking methods have been widely successful by
integ...
Estimating the target extent poses a fundamental challenge in visual obj...
We propose a trainable Image Signal Processing (ISP) framework that prod...
We propose Probabilistic Warp Consistency, a weakly-supervised learning
...
Multi-Object Tracking (MOT) is most often approached in the
tracking-by-...
Video super-resolution (VSR) has many applications that pose strict caus...
Free-form inpainting is the task of adding new content to an image in th...
Class-conditioning offers a direct means of controlling a Generative
Adv...
The design of more complex and powerful neural network models has
signif...
Accurate and robust visual object tracking is one of the most challengin...
Two-stage and query-based instance segmentation methods have achieved
re...
Super-resolution is an ill-posed problem, where a ground-truth
high-reso...
Energy-based models (EBMs) have experienced a resurgence within machine
...
Few-shot segmentation is a challenging dense prediction task, which enta...
Establishing robust and accurate correspondences between a pair of image...
Traditional domain adaptation addresses the task of adapting a model to ...
We propose a deep reparametrization of the maximum a posteriori formulat...
Normalizing flows have recently demonstrated promising results for low-l...
Multiple object tracking and segmentation requires detecting, tracking, ...
This paper reviews the NTIRE2021 challenge on burst super-resolution. Gi...
Tracking of objects in 3D is a fundamental task in computer vision that ...
The key challenge in learning dense correspondences lies in the lack of
...
The presence of objects that are confusingly similar to the tracked targ...
Few-shot segmentation is a challenging task, requiring the extraction of...
While single-image super-resolution (SISR) has attracted substantial int...
The difficulty of obtaining paired data remains a major bottleneck for
l...
Segmenting objects in videos is a fundamental computer vision task. The
...
We propose a novel neural network module that transforms an existing
sin...
Establishing dense correspondences between a pair of images is an import...
The state-of-the-art object detection and image classification methods c...
Accurate 3D object detection (3DOD) is crucial for safe navigation of co...
Most existing approaches to video instance segmentation comprise multipl...
Learning in a low-data regime from only a few labeled examples is an
imp...
The feature correlation layer serves as a key neural network module in
n...
This paper reviews the AIM 2020 challenge on efficient single image
supe...