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20212023
most citedMeta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation

162 citations · 230 across the 10 of their papers we have counts for

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cs.CV2023

Face Transformer: Towards High Fidelity and Accurate Face Swapping

Kaiwen Cui, Rongliang Wu, Fangneng Zhan +1

Face swapping aims to generate swapped images that fuse the identity of source faces and the attributes of target faces. Most existing works address this challenging task through 3…

cs.CV20231 cited

KD-DLGAN: Data Limited Image Generation via Knowledge Distillation

Kaiwen Cui, Yingchen Yu, Fangneng Zhan +3

Generative Adversarial Networks (GANs) rely heavily on large-scale training data for training high-quality image generation models. With limited training data, the GAN discriminato…

cs.CV202238 cited

PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds

Aoran Xiao, Jiaxing Huang, Dayan Guan +3

LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous det…

cs.CV2022162 cited

Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation

Gongjie Zhang, Zhipeng Luo, Kaiwen Cui +2

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is still c…

cs.CV2022

Auto-regressive Image Synthesis with Integrated Quantization

Fangneng Zhan, Yingchen Yu, Rongliang Wu +4

Deep generative models have achieved conspicuous progress in realistic image synthesis with multifarious conditional inputs, while generating diverse yet high-fidelity images remai…

cs.CV20221 cited

Accelerating DETR Convergence via Semantic-Aligned Matching

Gongjie Zhang, Zhipeng Luo, Yingchen Yu +2

The recently developed DEtection TRansformer (DETR) establishes a new object detection paradigm by eliminating a series of hand-crafted components. However, DETR suffers from extre…