We present the first systematic study on concealed object detection (COD), which aims to
identify objects that are visually embedded in their background. The high intrinsic similarities …

We present a comprehensive study on a new task named camouflaged object detection
(COD), which aims to identify objects that are" seamlessly" embedded in their surroundings …

Image matting is an inverse fusion process that separates the foreground and background
information by predicting alpha matte for each pixel. Recently, plain vision Transformers …

We introduce a robust, real-time, high-resolution human video matting method that achieves
new state-of-the-art performance. Our method is much lighter than previous approaches and …

We introduce a real-time, high-resolution background replacement technique which
operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU. Our technique is …

We propose a method for creating a matte-the per-pixel foreground color and alpha-of a
person by taking photos or videos in an everyday setting with a handheld camera. Most …

In this paper, we propose a transformer-based image matting model called MatteFormer,
which takes full advantage of trimap information in the transformer block. Our method first …

Existing deep learning based matting algorithms primarily resort to high-level semantic
features to improve the overall structure of alpha mattes. However, we argue that advanced …

Compared to many other dense prediction tasks, eg, semantic segmentation, it is the
arbitrary number of instances that has made instance segmentation much more challenging …

Existing portrait matting methods either require auxiliary inputs that are costly to obtain or
involve multiple stages that are computationally expensive, making them less suitable for …