欢迎分享ICCV 2023 论文和代码 / Welcome to share the paper and code of ICCV 2023

[amusi]

[The format of the issue] Paper name/title: Paper link: Code link: keywords:

[yichen928]

Paper name: SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection Paper link: https://arxiv.org/abs/2304.14340 Code link: https://github.com/yichen928/SparseFusion Thank you very much!

[yh-pengtu]

Paper name/title: Femtodet: an object detection baseline for energy versus performance tradeoffs Paper link: https://arxiv.org/pdf/2301.06719.pdf Code link: https://github.com/yh-pengtu/FemtoDet Thank you very very very very very very much!!!!!

[simon3dv]

Paper name/title: Simulating Fluids in Real-World Still Images Paper link: https://arxiv.org/abs/2204.11335 Code link: https://github.com/simon3dv/SLR-SFS Project Page: https://slr-sfs.github.io/ Related areas: Video Generation Thank you very much!

[yingfei1016]

Related areas: 3D Object Detection

Paper name/title: StreamPETR: Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection Paper link: https://arxiv.org/pdf/2303.11926.pdf Code link: https://github.com/exiawsh/StreamPETR.git

Paper name/title: Cross Modal Transformer: Towards Fast and Robust 3D Object Detection Paper link: https://arxiv.org/pdf/2301.01283.pdf Code link: https://github.com/junjie18/CMT.git

Thank you very much!

[ChongjianGE]

Related areas: BEV Perception/3D Perception/Autonomy Perception

Paper name/title: MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation Paper link: https://arxiv.org/abs/2304.09801 Project link: https://chongjiange.github.io/metabev.html Code link: https://github.com/ChongjianGE/MetaBEV

Thank you very much!

[TY-Shi]

Paper name/title: FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation Paper link: https://arxiv.org/abs/2307.07245 Code link: https://github.com/TY-Shi/FreeCOS keywords: self-supervised learning, segmentation

[Sierkinhane]

Related areas: Diffusion Models(扩散模型) Paper name/title: BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion Paper link: https://arxiv.org/abs/2307.10816 Code link: https://github.com/Sierkinhane/BoxDiff keywords: text-to-image synthesis, diffusion models, layout-to-image synthesis

[zhuoxiao-chen]

Related Area: 3D Object Detection, Domain Adaptation Paper title: Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling Paper link: https://arxiv.org/abs/2307.07944 Code link: https://github.com/zhuoxiao-chen/ReDB-DA-3Ddet Thank you very much!

[MrGiovanni]

Paper name: CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection Rank First in Medical Segmentation Decathlon (MSD) Competition paper | code | slides | poster | talk

Thank you very much!

[yyliu01]

Related Area: Anomaly Segmentation / Out-of-Distribution Segmentation Paper name: Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation Paper link: https://arxiv.org/pdf/2211.14512.pdf Code link: https://github.com/yyliu01

Related Area: Multi-Label Noisy Label Learning Paper name: BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification Paper link: https://arxiv.org/pdf/2203.01937.pdf Code link: https://github.com/cyh-0/BoMD

@amusi Thanks so much for your help.

[PromptStyler]

Related Area: CLIP, Prompt, Vision-Language, Domain Generalization Paper name/title: PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization Paper link: https://arxiv.org/abs/2307.15199 Project Page: https://PromptStyler.github.io/

@amusi Thanks for your help!!

[FutureXiang]

Paper name: Denoising Diffusion Autoencoders are Unified Self-supervised Learners Paper link: https://arxiv.org/abs/2303.09769 Code link: https://github.com/FutureXiang/ddae Related area: Diffusion, Self-supervised representation learning

Selected for Oral presentation at ICCV 23. @amusi Thank you!

[Yushu-Li]

Paper name: On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion Paper link: https://arxiv.org/abs/2308.09942 Code link: https://github.com/Yushu-Li/OWTTT Project Page: https://yushu-li.github.io/2023_LiEtAl_OWTTT/ Related area: Transfer Learning, Test-Time Adaptation

Selected for Oral presentation at ICCV 23. @amusi Thank you!