1.6k citations · 4.8k across the 90 of their papers we have counts for
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Scale-wise Convolution for Image Restoration
Yuchen Fan, Jiahui Yu, Ding Liu +1
While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Na…
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Bowen Cheng, Maxwell D. Collins, Yukun Zhu +4
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achiev…
Any-Precision Deep Neural Networks
Haichao Yu, Haoxiang Li, Honghui Shi +2
We present any-precision deep neural networks (DNNs), which are trained with a new method that allows the learned DNNs to be flexible in numerical precision during inference. The s…
Panoptic-DeepLab
Bowen Cheng, Maxwell D. Collins, Yukun Zhu +4
We present Panoptic-DeepLab, a bottom-up and single-shot approach for panoptic segmentation. Our Panoptic-DeepLab is conceptually simple and delivers state-of-the-art results. In p…
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
Zengming Shen, Yifan Chen, S. Kevin Zhou +3
The one-to-one mapping is necessary for many bidirectional image-to-image translation applications, such as MRI image synthesis as MRI images are unique to the patient. State-of-th…
One-to-one Mapping for Unpaired Image-to-image Translation
Zengming Shen, S. Kevin Zhou, Yifan Chen +3
Recently image-to-image translation has attracted significant interests in the literature, starting from the successful use of the generative adversarial network (GAN), to the intr…