Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies.
#22 best model for Image Classification on ImageNet
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
#3 best model for Video Object Detection on ImageNet VID
The micro-batch training setting is hard because small batch sizes are not enough for training networks with Batch Normalization (BN), while other normalization methods that do not rely on batch knowledge still have difficulty matching the performances of BN in large-batch training.
Recent years have seen tremendous progress in still-image segmentation; however the na\"ive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video.
Diffusions effectively interact two aspects of information, i. e., localized and holistic, for more powerful way of representation learning.
HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene.
SOTA for Multi-Task Learning on HVU
In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection.