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Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as human and object, the improvements of IoU are even higher than 18 percentage points.
Deep Neural Nets have hit quite a crest, But physical networks are where they must rest, And here we put them all to the test, To see which network optimization is best.
In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimat
Energy-Based Models (EBMs), also known as non-normalized probabilistic models, specify probability density or mass functions up to an unknown normalizing constant. Unlike most other probabilistic models, EBMs do not place a restriction on the tractab
Video interpolation aims to generate a non-existent intermediate frame given the past and future frames. Many state-of-the-art methods achieve promising results by estimating the optical flow between the known frames and then generating the backward
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation. In comparison to convolutional neural networks