8 accepted papers
Identifying and localizing objects within images is a fundamental challenge, and numerous efforts have been made to enhance model accuracy by experimenting with diverse architectures and refining training strategies. Nevertheless, a prevalent limitation in existing models is overemphasizing the curr…
"Today’s deep learning methods focus on how to design the objective functions to make the prediction as close as possible to the target. Meanwhile, an appropriate neural network architecture has to be designed. Existing methods ignore a fact that when input data undergoes layer-by-layer feature tran…
Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have spawned when dealing with these latest state-of-the…
Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human mot…
We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also s…
Translating video to language is very challenging due to diversified video contents originated from multiple activities and complicated integration of spatio-temporal information. There are two urgent issues associated with the video-to-language translation problem. First, how to transfer knowledge…
Varying types of shots is a fundamental element in the language of film, commonly used by a visual storytelling director to convey the emotion, ideas, and art. To classify such types of shots from images, we present a new framework that facilitates the intriguing task by addressing two key issues. W…
Player segmentation in team sports videos is challenging but crucial to video semantic understanding, such as player interaction identification and tactic analysis. We leverage the appearance similarity among players of the same team, and cast this task as a co-segmentation problem. In this way, the…