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1.【基础网络架构:Transformer】(NeurIPS2023)MCUFormer: Deploying Vision Tranformers on Microcontrollers with Limited Memory
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论文地址:https://arxiv.org//pdf/2310.16898
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开源代码(即将开源):https://github.com/liangyn22/MCUFormer
2.【Open-Vocabulary Object Detection】LP-OVOD: Open-Vocabulary Object Detection by Linear Probing
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论文地址:https://arxiv.org//pdf/2310.17109
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开源代码(即将开源):https://github.com/VinAIResearch/LP-OVOD
3.【视频异常检测】A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection
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论文地址:https://arxiv.org//pdf/2310.17650
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开源代码(即将开源):https://github.com/AnasEmad11/C2FPL
4.【视频超分辨率重建】(WACV2024)Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution
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论文地址:https://arxiv.org//pdf/2310.17294
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开源代码:https://github.com/megvii-research/WACV2024-SAFA
5.【图像增强】(NeurIPS2023)Global Structure-Aware Diffusion Process for Low-Light Image Enhancement
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论文地址:https://arxiv.org//pdf/2310.17577
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开源代码(即将开源):GitHub - jinnh/GSAD: Global Structure-Aware Diffusion Process for Low-Light Image Enhancement
6.【领域泛化】SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
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论文地址:https://arxiv.org//pdf/2310.17594
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开源代码(即将开源):GitHub - CrownX/SPA
7.【多模态】(EMNLP2023)Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models
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论文地址:https://arxiv.org//pdf/2310.17530
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开源代码:GitHub - coastalcph/gender-neutral-vl: [EMNLP 2023] Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models
8.【多模态】(NeurIPS2023)Cross-modal Active Complementary Learning with Self-refining Correspondence
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论文地址:https://arxiv.org//pdf/2310.17468
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开源代码(即将开源):GitHub - QinYang79/CRCL: Cross-modal Active Complementary Learning with Self-refining Correspondence (NeurIPS 2023, Pytorch Code)
9.【自动驾驶】Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
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论文地址:https://arxiv.org//pdf/2310.17642
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工程主页:Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models
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开源代码(即将开源):https://github.com/zswang666/drive-anywhere
10.【自动驾驶:BEV】BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
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论文地址:https://arxiv.org//pdf/2310.17281
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开源代码:https://github.com/valeoai/BEVContrast
11.【自动驾驶:协同感知】(WACV2024)MACP: Efficient Model Adaptation for Cooperative Perception
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论文地址:https://arxiv.org//pdf/2310.16870
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开源代码:https://github.com/PurdueDigitalTwin/MACP
12.【NeRF】4D-Editor: Interactive Object-level Editing in Dynamic Neural Radiance Fields via 4D Semantic Segmentation
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论文地址:https://arxiv.org//pdf/2310.16858
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工程主页:4D-Editor: Interactive Object-level Editing in Dynamic Neural Radiance Fields via 4D Semantic Segmentation
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代码即将开源
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