We present an effective and efficient method that explores the properties of Transformers in
the frequency domain for high-quality image deblurring. Our method is motivated by the …

We propose to explore a new problem called audio-visual segmentation (AVS), in which the
goal is to output a pixel-level map of the object (s) that produce sound at the time of the …

The High-Resolution Transformer (HRFormer) can maintain high-resolution representation
and share global receptive fields. It is friendly towards salient object detection (SOD) in …

How to identify and segment camouflaged objects from the background is challenging.
Inspired by the multi-head self-attention in Transformers, we present a simple masked …

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often
provided by multiple annotators. Existing methods fuse multiple annotations using a simple …

Abstract We propose an Explicit Conditional Multimodal Variational Auto-Encoder
(ECMVAE) for audio-visual segmentation (AVS), aiming to segment sound sources in the …

Audio-visual video segmentation (AVVS) aims to generate pixel-level maps of sound-
producing objects within image frames and ensure the maps faithfully adheres to the given …

Audio-visual segmentation (AVS) aims to locate and segment the sounding objects in a
given video, which demands audio-driven pixel-level scene understanding. The existing …

Abstract The objective of Audio-Visual Segmentation (AVS) is to localise the sounding
objects within visual scenes by accurately predicting pixel-wise segmentation masks. To …

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this
way, camouflage acts as a key defence mechanism across species that is critical to survival …