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1.【基础网络架构：Transformer】Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer
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论文地址：https://arxiv.org//pdf/2311.06749
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开源代码（即将开源）：https://github.com/Dongping-Chen/EFFT-EFfective-Factor-Tuning
2.【缺陷检测】Self-supervised Context Learning for Visual Inspection of Industrial Defects
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论文地址：https://arxiv.org//pdf/2311.06504
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开源代码（即将开源）：https://github.com/wangpeng000/VisualInspection
3.【目标检测、分割】CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision
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论文地址：https://arxiv.org//pdf/2311.06976
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开源代码：https://github.com/Aymanbegh/CD-COCO
4.【视频分割】Sketch-based Video Object Segmentation: Benchmark and Analysis
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论文地址：https://arxiv.org//pdf/2311.07261
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开源代码（即将开源）：https://github.com/YRlin-12/Sketch-VOS-datasets
5.【多模态】SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
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论文地址：https://arxiv.org//pdf/2311.07575
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开源代码：https://github.com/Alpha-VLLM/LLaMA2-Accessory
6.【多模态】To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
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论文地址：https://arxiv.org//pdf/2311.07574
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开源代码（即将开源）：https://github.com/X2FD/LVIS-INSTRUCT4V
7.【多模态】GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
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论文地址：https://arxiv.org//pdf/2311.07562
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开源代码（即将开源）：https://github.com/zzxslp/MM-Navigator
8.【多模态】GPT-4V(ision) as A Social Media Analysis Engine
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论文地址：https://arxiv.org//pdf/2311.07547
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开源代码（即将开源）：https://github.com/VIStA-H/GPT-4V_Social_Media
9.【多模态】InfMLLM: A Unified Framework for Visual-Language Tasks
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论文地址：https://arxiv.org//pdf/2311.06791
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开源代码：https://github.com/mightyzau/InfMLLM
10.【多模态】Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
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论文地址：https://arxiv.org//pdf/2311.06783
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工程主页：Q-Instruct | [IQA, Low-level Vision, MLLM] Low-level visual instruction tuning, with a 200K dataset and a model zoo for fine-tuned checkpoints.
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开源代码：https://github.com/Q-Future/Q-Instruct/
11.【多模态】ChatAnything: Facetime Chat with LLM-Enhanced Personas
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论文地址：https://arxiv.org//pdf/2311.06772
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工程主页：ChatAnything
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开源代码：https://github.com/zhoudaquan/ChatAnything
12.【多模态】Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models
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论文地址：https://arxiv.org//pdf/2311.06607
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开源代码（即将开源）：https://github.com/Yuliang-Liu/Monkey
13.【多模态】An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
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论文地址：https://arxiv.org//pdf/2311.07397
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开源代码（即将开源）：https://github.com/junyangwang0410/AMBER
14.【多模态】Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
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论文地址：https://arxiv.org//pdf/2311.07362
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开源代码（即将开源）：https://github.com/kaistAI/Volcano
15.【多模态】ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
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论文地址：https://arxiv.org//pdf/2311.07022
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工程主页：ViLMA - Video Language Model Assessment
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开源代码：https://github.com/ilkerkesen/ViLMA
16.【数字人】（WACV2024）CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer
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论文地址：https://arxiv.org//pdf/2311.06443
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开源代码（即将开源）：https://github.com/HowieMa/CVTHead
17.【深度估计】MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
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论文地址：https://arxiv.org//pdf/2311.07198
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开源代码（即将开源）：https://github.com/ShuweiShao/MonoDiffusion
18.【深度估计】（ICCV2023）NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
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论文地址：https://arxiv.org//pdf/2311.07166
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开源代码（即将开源）：https://github.com/ShuweiShao/NDDepth
19.【自动驾驶：BEV】Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection
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论文地址：https://arxiv.org//pdf/2311.07152
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开源代码：https://github.com/HuangJunJie2017/BEVDet
20.【自动驾驶：BEV】Deep Perspective Transformation Based Vehicle Localization on Bird's Eye View
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论文地址：https://arxiv.org//pdf/2311.06796
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开源代码（即将开源）：https://github.com/IPM-HPC/Perspective-BEV-Transformer
21.【Diffusion】Sampler Scheduler for Diffusion Models
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论文地址：https://arxiv.org//pdf/2311.06845
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开源代码：https://github.com/Carzit/sd-webui-samplers-scheduler
22.【NeRF】-Sampler: An Model Guided Volume Sampling for NeRF
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论文地址：https://arxiv.org//pdf/2311.07044
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工程主页：L0-Sampler: An L0 Model Guided Volume Sampling for NeRF
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开源代码：https://github.com/USTC3DV/L0-Sampler-code
23.【Visual Question Answering】Analyzing Modular Approaches for Visual Question Decomposition
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论文地址：https://arxiv.org//pdf/2311.06411
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开源代码：https://github.com/brown-palm/visual-question-decomposition
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