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20182024
most citedImage Inpainting by End-to-End Cascaded Refinement with Mask Awareness

140 citations · 436 across the 46 of their papers we have counts for

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Showing 2022Show all

13 papers · 1 filter

cs.CV2022

AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer

Tianwei Lin, Honglin Lin, Fu Li +5

Photo-realistic style transfer aims at migrating the artistic style from an exemplar style image to a content image, producing a result image without spatial distortions or unreali…

cs.CV2022★ 17 cited

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

Jiaxiang Tang, Kaisiyuan Wang, Hang Zhou +6

While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their pote…

cs.CV2022★ 1 cited

RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection

Lin Zhang, Xin Li, Dongliang He +3

Reference-based image super-resolution (RefSR) is a promising SR branch and has shown great potential in overcoming the limitations of single image super-resolution. While previous…

cs.CV2022★ 5 cited

It Takes Two: Masked Appearance-Motion Modeling for Self-supervised Video Transformer Pre-training

Yuxin Song, Min Yang, Wenhao Wu +3

Self-supervised video transformer pre-training has recently benefited from the mask-and-predict pipeline. They have demonstrated outstanding effectiveness on downstream video tasks…

eess.IV2022

Effective Invertible Arbitrary Image Rescaling

Zhihong Pan, Baopu Li, Dongliang He +2

Great successes have been achieved using deep learning techniques for image super-resolution (SR) with fixed scales. To increase its real world applicability, numerous models have…

eess.IV2022★ 16 cited

AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results

Ren Yang, Radu Timofte, Xin Li +49

This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compress…

cs.CV2022

Boosting Video-Text Retrieval with Explicit High-Level Semantics

Haoran Wang, Di Xu, Dongliang He +4

Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video). Existing metho…

cs.CV2022★ 3 cited

CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval

Haoran Wang, Dongliang He, Wenhao Wu +7

Image-Text Retrieval (ITR) is challenging in bridging visual and lingual modalities. Contrastive learning has been adopted by most prior arts. Except for limited amount of negative…

cs.CV2022★ 1 cited

NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition

Boyang Xia, Wenhao Wu, Haoran Wang +5

It is challenging for artificial intelligence systems to achieve accurate video recognition under the scenario of low computation costs. Adaptive inference based efficient video re…

cs.CV2022★ 14 cited

Neural Color Operators for Sequential Image Retouching

Yili Wang, Xin Li, Kun Xu +4

We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operat…

cs.CV2022★ 7 cited

DALG: Deep Attentive Local and Global Modeling for Image Retrieval

Yuxin Song, Ruolin Zhu, Min Yang +1

Deeply learned representations have achieved superior image retrieval performance in a retrieve-then-rerank manner. Recent state-of-the-art single stage model, which heuristically…

eess.IV2022

Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence

Zhihong Pan, Baopu Li, Dongliang He +5

Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and dow…

cs.CV2022

Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation

Cong Cao, Tianwei Lin, Dongliang He +4

A common challenge posed to robust semantic segmentation is the expensive data annotation cost. Existing semi-supervised solutions show great potential for solving this problem. Th…