CV计算机视觉每日开源代码Paper with code速览-2023.11.1

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1.【基础网络架构】Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks

论文地址：https://arxiv.org//pdf/2310.19909
开源代码：GitHub - hsouri/Battle-of-the-Backbones

2.【基础网络架构】（NeurIPS2023）Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment

论文地址：https://arxiv.org//pdf/2310.20599
开源代码：https://github.com/toosi/Feedback_Feedforward_Alignment

3.【基础网络架构：Transformer】（WACV2024）Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders

论文地址：https://arxiv.org//pdf/2310.20704
开源代码（即将开源）：https://github.com/dominickrei/Limited-data-vits

4.【目标检测：伪装目标】ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection

论文地址：https://arxiv.org//pdf/2310.20208
开源代码（即将开源）：https://github.com/lartpang/ZoomNeXt

5.【语义分割】（CAC2023）Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation

论文地址：https://arxiv.org//pdf/2310.20305
开源代码（即将开源）：GitHub - LikeLidoA/BiDGANet: $CAC2023$ Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation

6.【点云3D目标检测】（ICCV2023）GACE: Geometry Aware Confidence Enhancement for Black-Box 3D Object Detectors on LiDAR-Data

论文地址：https://arxiv.org//pdf/2310.20319
开源代码：https://github.com/dschinagl/gace

7.【点云3D目标检测】HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

论文地址：https://arxiv.org//pdf/2310.20234
开源代码（即将开源）：https://github.com/zhanggang001/HEDNet

8.【点云语义分割】（NeurIPS2023）Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation

论文地址：https://arxiv.org//pdf/2310.20293
工程主页：Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
开源代码（即将开源）：https://github.com/BIT-DA/Annotator

9.【医学图像分割】From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation

论文地址：https://arxiv.org//pdf/2310.20271
开源代码：https://github.com/WenRuxue/DeTTA

10.【医学图像分割】MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder

论文地址：https://arxiv.org//pdf/2310.19898
开源代码（即将开源）：GitHub - Rahman-Motiur/MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder

11.【多模态】CapsFusion: Rethinking Image-Text Data at Scale

论文地址：https://arxiv.org//pdf/2310.20550
开源代码（即将开源）：https://github.com/baaivision/CapsFusion

12.【数字人】SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark

论文地址：https://arxiv.org//pdf/2310.20436
工程主页：SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark
代码即将开源

13.【自动驾驶：轨迹预测】（ICRA2024）Conditional Unscented Autoencoders for Trajectory Prediction

论文地址：https://arxiv.org//pdf/2310.19944
开源代码（即将开源）：GitHub - boschresearch/cuae-prediction: Accompanying code for the ICRA'24 paper submission titled: "Conditional Unscented Autoencoders for Trajectory Prediction". Coming soon...

14.【Diffusion】SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction

论文地址：https://arxiv.org//pdf/2310.20700
工程主页：SEINE: Short-to-Long Vidoes Diffusion Model for Generative Transition and Prediction
开源代码（即将开源）：https://github.com/Vchitect/SEINE

15.【人体运动生成】SemanticBoost: Elevating Motion Generation with Augmented Textual Cues

论文地址：https://arxiv.org//pdf/2310.20323
工程主页：SemanticBoost
开源代码：https://github.com/blackgold3/SemanticBoost

16.【NeRF】FPO++: Efficient Encoding and Rendering of Dynamic Neural Radiance Fields by Analyzing and Enhancing Fourier PlenOctrees

论文地址：https://arxiv.org//pdf/2310.20710
开源代码（即将开源）：https://github.com/SaskiaRabich/FPOplusplus

17.【NeRF】（NeurIPS2023）NeRF Revisited: Fixing Quadrature Instability in Volume Rendering

18.【类别增量学习】Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

论文地址：https://arxiv.org//pdf/2310.20268
开源代码（即将开源）：https://github.com/DemonJianZ/S2C

19.【Visual Question Answering】Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts

论文地址：https://arxiv.org//pdf/2310.20159
开源代码（即将开源）：https://github.com/declare-lab/LG-VQA

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