Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Plastic pollution in water bodies is an unresolved environmental issue that damages all
aquatic environments, and causes economic and health problems. Accurate detection of …

In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-
Decoder predicts the existence of class labels via queries, and enables better utilization of …

While deep convolutional neural networks (CNNs) have shown a great success in single-
label image classification, it is important to note that most real world images contain multiple …

Current action recognition methods heavily rely on trimmed videos for model training.
However, it is expensive and time-consuming to acquire a large-scale trimmed video …

In this work we investigate the effect of the convolutional network depth on its accuracy in the
large-scale image recognition setting. Our main contribution is a thorough evaluation of …

Image and sentence matching has made great progress recently, but it remains challenging
due to the large visual semantic discrepancy. This mainly arises from that the representation …

Convolutional neural networks (ConvNets) have achieved excellent recognition
performance in various visual recognition tasks. A large labeled training set is one of the …

The latest generation of Convolutional Neural Networks (CNN) have achieved impressive
results in challenging benchmarks on image recognition and object detection, significantly …

Visual attention has become a popular and widely used component for image recognition.
Although various attention-based methods have been proposed and achieved relatively …