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I interned at NVIDIA this summer as a performance software engineering intern while pursuing a Master's in Computer Science at UIUC.
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UIUC |
NVIDIA |
Intouch Games |
Logitech |
NCTU CS |
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This paper addresses the problem of fast semantic segmentation on compressed video. Unlike most prior works for video segmentation, which perform feature propagation based on optical flow estimates or sophisticated warping techniques, ours takes advantage of block motion vectors in the com- pressed bitstream to propagate the segmentation of a keyframe to subsequent non-keyframes. This approach, however, needs to respect the inter-frame prediction structure, which often suggests recursive, multi-step prediction with error propagation and accumulation in the temporal dimension. To tackle the issue, we refine the motion-compensated segmentation using inpainting. Our inpainting network incorporates guided non-local attention for long-range reference and pixel-adaptive convolution for ensuring the local coherence of the segmentation. A fusion step then follows to combine both the motion-compensated and inpainted segmentations. Experimental results show that our method outperforms the state-of-the-art baselines in terms of segmentation accuracy. Moreover, it introduces the least amount of network parameters and multiply-add operations for non- keyframe segmentation.
@article{chao18radiotherapy,
title = {Radiotherapy Target Contouring with Convolutional Gated Graph Neural
Network},
author = {Chao, Chun-Hung and Cheng, Yen-Chi and Cheng, Hsien-Tzu and Huang, Chi-Wen and
Ho, Tsung-Ying and Tseng, Chen-Kan
Lu, Le and Sun, Min},
journal = {arXiv preprint arXiv:1904.02912},
year = {2019},
}
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