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20152023
most citedDeep Multimodal Speaker Naming

38 citations · 125 across the 18 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

eess.IV2020★ 31 cited

Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric

Jinjin Gu, Haoming Cai, Haoyu Chen +3

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent perceptual IR algorithms based on generative adversa…

cs.CV2020★ 2 cited

EfficientFCN: Holistically-guided Decoding for Semantic Segmentation

Jianbo Liu, Junjun He, Jiawei Zhang +2

Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (…

eess.IV2020★ 10 cited

PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration

Jinjin Gu, Haoming Cai, Haoyu Chen +3

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks…

cs.CV2020★ 1 cited

Learning a Reinforced Agent for Flexible Exposure Bracketing Selection

Zhouxia Wang, Jiawei Zhang, Mude Lin +3

Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion. Unlike previous methods t…

cs.CV2020★ 2 cited

Learning Event-Based Motion Deblurring

Zhe Jiang, Yu Zhang, Dongqing Zou +3

Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the significant loss of motion information in the blurring process. For event-based cameras,…

cs.CV2020

Learning to Predict Context-adaptive Convolution for Semantic Segmentation

Jianbo Liu, Junjun He, Jimmy S. Ren +2

Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for…