Human action recognition based on still images is one of the most challenging computer
vision tasks. In the past decade, convolutional neural networks (CNNs) have developed …

Human activity recognition is one of the most difficult tasks in computer vision. Due to the
lack of time information, detecting human activities from still photos is more difficult than …

Infrared photothermal heterodyne imaging (IR-PHI) is an all-optical table top approach that
enables super-resolution mid-infrared microscopy and spectroscopy. The underlying …

The assessment of students' classroom behavior is an important part of classroom teaching
evaluation. However, teachers cannot timely, objectively and accurately evaluate the …

In still image human action recognition, existing studies have mainly leveraged extra
bounding box information along with class labels to mitigate the lack of temporal information …

Human action recognition in visual data is one of the most fundamental challenges in
computer vision. Existing approaches for this primary goal have been based on video data …

Human-object interaction (HOI) detection is the task of predicting the visual relationships
between humans and their surroundings in images and videos by locating and inferring the …

Still image-based action recognition is a challenging area in which recognition is performed
based on only a single input image. Utilizing auxiliary information such as pose, object, or …

Deep neural networks have demonstrated outstanding performance in various image
classification tasks, yet action recognition from still images is challenging due to lack of …

Establishing fine-grained human behavior recognition is crucial in human-centric
applications. Although modeling human behaviors and activity patterns for recognition or …