Training data for video segmentation are expensive to annotate. This imp...
Learning computer vision models from (and for) movies has a long-standin...
Despite advancements in user-guided video segmentation, extracting compl...
We present Interactive Neural Video Editing (INVE), a real-time video ed...
The tracking-by-detection paradigm today has become the dominant method ...
Scaling object taxonomies is one of the important steps toward a robust
...
This paper investigates the challenge of extracting highlight moments fr...
Video has become a dominant form of media. However, video editing interf...
Recently, handling long videos of complex and occluded sequences has eme...
Recently, memory-based approaches show promising results on semi-supervi...
Recent studies made great progress in video matting by extending the suc...
Machine learning is transforming the video editing industry. Recent adva...
We introduce a novel paradigm for offline Video Instance Segmentation (V...
We propose an information-theoretic bias measurement technique through a...
We present Hierarchical Memory Matching Network (HMMN) for semi-supervis...
When the trained physician interprets medical images, they understand th...
Dataset bias is a critical challenge in machine learning, and its negati...
Temporal correspondence - linking pixels or objects across frames - is a...
Humans are arguably one of the most important subjects in video streams,...
In this paper, we propose a novel learning-based polygonal point set tra...
We extend panoptic segmentation to the open-world and introduce an open-...
Panoptic segmentation has become a new standard of visual recognition ta...
Current methods for active speak er detection focus on modeling short-te...
Visual Dialog involves "understanding" the dialog history (what has been...
We propose a novel memory-based tracker via part-level dense memory and
...
We propose the onion-peel networks for video completion. Given a set of
...
We propose a novel feed-forward network for video inpainting. We use a s...
Correspondences between frames encode rich information about dynamic con...
Blind video decaptioning is a problem of automatically removing text ove...
Video inpainting aims to fill spatio-temporal holes with plausible conte...
We present a deep learning method for the interactive video object
segme...
We propose a novel solution for semi-supervised video object segmentatio...
We study the problem of learning a generalizable action policy for an
in...
While machine learning approaches to visual emotion recognition offer gr...
We propose Convolutional Block Attention Module (CBAM), a simple yet
eff...
Recent advances in deep neural networks have been developed via architec...
Learning-based color enhancement approaches typically learn to map from ...
Deep reinforcement learning (DRL) demonstrates its potential in learning...
Videos captured by consumer cameras often exhibit temporal variations in...
We introduce a novel method to automatically adjust camera exposure for ...
Indoor scene understanding is central to applications such as robot
navi...
A dominant paradigm for deep learning based object detection relies on a...
We present surface normal estimation using a single near infrared (NIR)
...
We introduce a new technique that automatically generates diverse, visua...
We present a novel detection method using a deep convolutional neural ne...
Compared to image representation based on low-level local descriptors, d...