Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

Abstract The recently proposed Detection Transformer (DETR) model successfully applies
Transformer to objects detection and achieves comparable performance with two-stage …

Vision-and-language reasoning requires an understanding of visual concepts, language
semantics, and, most importantly, the alignment and relationships between these two …

End-to-end Object Detection with Transformer (DETR) proposes to perform object detection
with Transformer and achieve comparable performance with two-stage object detection like …

Self-attention (SA) network has shown profound value in image captioning. In this paper, we
improve SA from two aspects to promote the performance of image captioning. First, we …

Abstract Language bias is a critical issue in Visual Question Answering (VQA), where
models often exploit dataset biases for the final decision without considering the image …

Convolutional neural networks (CNNs) are ubiquitous in computer vision, with a myriad of
effective and efficient variations. Recently, Transformers--originally introduced in natural …

To fluently collaborate with people, robots need the ability to recognize human activities
accurately. Although modern robots are equipped with various sensors, robust human …

Generalization beyond the training distribution is a core challenge in machine learning. The
common practice of mixing and shuffling examples when training neural networks may not …

A simultaneous understanding of questions and images is crucial in Visual Question
Answering (VQA). While the existing models have achieved satisfactory performance by …