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20182022
most citedIoU-Enhanced Attention for End-to-End Task Specific Object Detection

3 citations · 8 across the 7 of their papers we have counts for

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9 papers · 1 filter

cs.CV20223 cited

IoU-Enhanced Attention for End-to-End Task Specific Object Detection

Jing Zhao, Shengjian Wu, Li Sun +1

Without densely tiled anchor boxes or grid points in the image, sparse R-CNN achieves promising results through a set of object queries and proposal boxes updated in the cascaded t…

cs.CV20222 cited

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation

Xueqi Hu, Xinyue Zhou, Qiusheng Huang +3

Unpaired image-to-image (I2I) translation often requires to maximize the mutual information between the source and the translated images across different domains, which is critical…

cs.CV20222 cited

Style Transformer for Image Inversion and Editing

Xueqi Hu, Qiusheng Huang, Zhengyi Shi +4

Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously. This paper presents a transformer-based image inversion…

cs.CV2021

Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation

Qiusheng Huang, Zhilin Zheng, Xueqi Hu +2

The image-to-image translation (I2IT) model takes a target label or a reference image as the input, and changes a source into the specified target domain. The two types of synthesi…

cs.CV2021

LSC-GAN: Latent Style Code Modeling for Continuous Image-to-image Translation

Qiusheng Huang, Xueqi Hu, Li Sun +1

Image-to-image (I2I) translation is usually carried out among discrete domains. However, image domains, often corresponding to a physical value, are usually continuous. In other wo…

cs.CV2021

ID-Unet: Iterative Soft and Hard Deformation for View Synthesis

Mingyu Yin, Li Sun, Qingli Li

View synthesis is usually done by an autoencoder, in which the encoder maps a source view image into a latent content code, and the decoder transforms it into a target view image a…