CV计算机视觉每日开源代码Paper with code速览-2023.10.13

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1.【基础网络架构】CHIP: Contrastive Hierarchical Image Pretraining

• 论文地址：https://arxiv.org//pdf/2310.08304

• 开源代码：GitHub - harshiljhaveri/CHIP

2.【基础网络架构：Transformer】AutoVP: An Automated Visual Prompting Framework and Benchmark

• 论文地址：https://arxiv.org//pdf/2310.08381

• 开源代码：GitHub - IBM/AutoVP: Code and Benchmark for the paper "AutoVP: An Automated Visual Prompting Framework and Benchmark"

3.【关键点检测】UniPose: Detecting Any Keypoints

• 论文地址：https://arxiv.org//pdf/2310.08530

• 工程主页：UniPose: Detecting Any Keypoints

• 开源代码（即将开源）：GitHub - IDEA-Research/UniPose: Official implementation of the paper "UniPose : Detecting Any Keypoints"

4.【点云】PonderV2: Pave the Way for 3D Foundataion Model with A Universal Pre-training Paradigm

• 论文地址：https://arxiv.org//pdf/2310.08586

• 开源代码：GitHub - Pointcept/Pointcept: Pointcept: a codebase for point cloud perception research. Latest works: PPT, MSC (CVPR'23), PTv2 (NeurIPS'22)

5.【点云分割】PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation

• 论文地址：https://arxiv.org//pdf/2310.07743

• 开源代码：GitHub - haibo-qiu/PointHR: PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation

6.【医学图像分割】Volumetric Medical Image Segmentation via Scribble Annotations and Shape Priors

• 论文地址：https://arxiv.org//pdf/2310.08084

• 开源代码：GitHub - Qybc/Scribble2D5: Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations

7.【医学图像分割：3D】3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

• 论文地址：https://arxiv.org//pdf/2310.07781

• 开源代码：GitHub - Beckschen/3D-TransUNet: This is the official repository for the paper "3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers"

8.【多模态】Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models

• 论文地址：https://arxiv.org//pdf/2310.08577

• 开源代码（即将开源）：GitHub - bethgelab/DataTypeIdentification: Code for the paper: "Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models"

9.【多模态】Multimodal Variational Auto-encoder based Audio-Visual Segmentation

• 论文地址：https://arxiv.org//pdf/2310.08303

• 工程主页：Multimodal Variational Auto-encoder based Audio-Visual Segmentation

• 开源代码（即将开源）：GitHub - OpenNLPLab/MMVAE-AVS: Multimodal Variational Auto-encoder based Audio-Visual Segmentation [ICCV2023].

10.【多模态】Distilling from Vision-Language Models for Improved OOD Generalization in Vision Tasks

• 论文地址：https://arxiv.org//pdf/2310.08255

• 开源代码：GitHub - val-iisc/VL2V-ADiP: Distilling from Vision-Language Models for Improved OOD Generalization in Image Classification

11.【多模态】Lifelong Audio-video Masked Autoencoder with Forget-robust Localized Alignments

• 论文地址：https://arxiv.org//pdf/2310.08204

• 工程主页：FLAVA

• 代码即将开源

12.【多模态】Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing Label Bias in Foundation Models

• 论文地址：https://arxiv.org//pdf/2310.08106

• 开源代码（即将开源）：GitHub - BeierZhu/GLA: [NeurIPS 2023] Generalized Logit Adjustment (Coming Soon)

13.【多模态】SingleInsert: Inserting New Concepts from a Single Image into Text-to-Image Models for Flexible Editing

• 论文地址：https://arxiv.org//pdf/2310.08094

• 工程主页：SingleInsert

• 开源代码（即将开源）：GitHub - JarrentWu1031/SingleInsert: Official pytorch implementation for SingleInsert

14.【多模态】Can We Edit Multimodal Large Language Models?

• 论文地址：https://arxiv.org//pdf/2310.08475

• 开源代码：GitHub - zjunlp/EasyEdit: An Easy-to-use Knowledge Editing Framework for LLMs.

15.【自动驾驶：多模态感知】UniPAD: A Universal Pre-training Paradigm for Autonomous Driving

• 论文地址：https://arxiv.org//pdf/2310.08370

• 开源代码（即将开源）：GitHub - Nightmare-n/UniPAD: UniPAD: A Universal Pre-training Paradigm for Autonomous Driving

16.【自动驾驶：协同感知】DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception

• 论文地址：https://arxiv.org//pdf/2310.08117

• 开源代码（即将开源）：GitHub - refkxh/DUSA: [ACM MM 2023] Official implementation of DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception

17.【自动驾驶：仿真】DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model

• 论文地址：https://arxiv.org//pdf/2310.07771

• 工程主页：DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model

• 开源代码（即将开源）：GitHub - shalfun/DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model

18.【Diffusion】HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion

• 论文地址：https://arxiv.org//pdf/2310.08579

• 工程主页：HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion

• 开源代码（即将开源）：GitHub - snap-research/HyperHuman: Github Repo for "HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion"

19.【Diffusion】MotionDirector: Motion Customization of Text-to-Video Diffusion Models

• 论文地址：https://arxiv.org//pdf/2310.08465

• 代码即将开源

20.【人体姿态估计】X-HRNet: Towards Lightweight Human Pose Estimation with Spatially Unidimensional Self-Attention

• 论文地址：https://arxiv.org//pdf/2310.08042

• 开源代码：GitHub - cool-xuan/x-hrnet: Official code for "X-HRNet: Towards Lightweight Human Pose Estimation with Spatially Unidimensional Self-Attention"

21.【人体运动生成】OmniControl: Control Any Joint at Any Time for Human Motion Generation

• 论文地址：https://arxiv.org//pdf/2310.08580

• 工程主页：OmniControl

• 开源代码（即将开源）：GitHub - neu-vi/OmniControl

22.【生成模型】Explorable Mesh Deformation Subspaces from Unstructured Generative Models

• 论文地址：https://arxiv.org//pdf/2310.07814

• 开源代码（即将开源）：ArmanMaesumi/generative-mesh-subspaces · GitHub

23.【三维重建】Consistent123: Improve Consistency for One Image to 3D Object Synthesis

• 论文地址：https://arxiv.org//pdf/2310.08092

• 工程主页：Consistent123: Improve Consistency for One Image to 3D Object Synthesis

• 代码即将开源

24.【图像分类：长尾分布】Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss

• 论文地址：https://arxiv.org//pdf/2310.08206

• 开源代码（即将开源）：GitHub - jinyery/Cognisance: Long-tail Classification Based on Invariant Feature Learning from A Multi-granularity Perspective

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