CV计算机视觉每日开源代码Paper with code速览-2023.11.15

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1.【基础网络架构：CNN】PadChannel: Improving CNN Performance through Explicit Padding Encoding

论文地址：https://arxiv.org//pdf/2311.07623
开源代码：GitHub - AussieSeaweed/pad-channel: Encoding padding statuses as an input channel for enhanced performance in convolutional neural networks

2.【语义分割】（ICLR2024）Test-Time Training for Semantic Segmentation with Output Contrastive Loss

论文地址：https://arxiv.org//pdf/2311.07877
开源代码：GitHub - dazhangyu123/OCL: Test-Time Training for Semantic Segmentation with Output Contrastive Loss

3.【医学图像分割：3D】Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts

论文地址：https://arxiv.org//pdf/2311.07806
开源代码（即将开源）：GitHub - MedICL-VU/variability: Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts

4.【多模态】Instant3D: Instant Text-to-3D Generation

论文地址：https://arxiv.org//pdf/2311.08403
工程主页：Instant3D: Instant Text-to-3D Generation
代码即将开源

5.【多模态】Towards Open-Ended Visual Recognition with Large Language Model

论文地址：https://arxiv.org//pdf/2311.08400
开源代码：GitHub - bytedance/OmniScient-Model: This repo contains the code for our paper Towards Open-Ended Visual Recognition with Large Language Model

6.【多模态】Improving Image Captioning via Predicting Structured Concepts

论文地址：https://arxiv.org//pdf/2311.08223
开源代码（即将开源）：GitHub - wangting0/SCP-WGCN: official code of "Improving Image Captioning via Predicting Structured Concepts"

7.【多模态】Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

论文地址：https://arxiv.org//pdf/2311.08046
开源代码：GitHub - PKU-YuanGroup/Chat-UniVi: Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

8.【多模态】One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

论文地址：https://arxiv.org//pdf/2311.07885
工程主页：One-2-3-45++
开源代码（即将开源）：GitHub - SUDO-AI-3D/One2345plus

9.【多模态】Vision-Language Instruction Tuning: A Review and Analysis

论文地址：https://arxiv.org//pdf/2311.08172
开源代码（即将开源）：GitHub - palchenli/VL-Instruction-Tuning

10.【多模态】Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification

论文地址：https://arxiv.org//pdf/2311.07593
开源代码：GitHub - BatsResearch/fudd: Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification

11.【三维重建】DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid

论文地址：https://arxiv.org//pdf/2311.08159
工程主页：DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction with an Optimizable Feature Grid.
代码即将开源

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