Device-free activity recognition plays a crucial role in smart building, security, and human–
computer interaction, which shows its strength in its convenience and cost-efficiency …

Deep learning has been proved effective in multiple object tracking, which confronts the
difficulties of frequent occlusions, confusing appearance, in‐and‐out objects, and lack of …

Real-world data often exhibits long tail distributions with heavy class imbalance, where the
majority classes can dominate the training process and alter the decision boundaries of the …

Abstract We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network
architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D …

This paper presents X3D, a family of efficient video networks that progressively expand a
tiny 2D image classification architecture along multiple network axes, in space, time, width …

Most state-of-the-art action localization systems process each action proposal individually,
without explicitly exploiting their relations during learning. However, the relations between …

Deep multimodal fusion by using multiple sources of data for classification or regression has
exhibited a clear advantage over the unimodal counterpart on various applications. Yet …

Spatiotemporal and motion features are two complementary and crucial information for
video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

Quality assessment of in-the-wild videos is a challenging problem because of the absence
of reference videos and shooting distortions. Knowledge of the human visual system can …