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1.【目标检测】Rank-DETR for High Quality Object Detection
-  论文地址:https://arxiv.org//pdf/2310.08854 
-  开源代码(即将开源):https://github.com/LeapLabTHU/Rank-DETR 

2.【语义分割】SSG2: A new modelling paradigm for semantic segmentation
-  论文地址:https://arxiv.org//pdf/2310.08671 
-  开源代码(即将开源):GitHub - feevos/ssg2: Official code repository for the publication "SSG2: A New Modelling Paradigm for Semantic Segmentation" 

3.【域自适应】SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation
-  论文地址:https://arxiv.org//pdf/2310.08928 
-  开源代码:GitHub - se111/SIDE 

4.【多模态】Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet Hierarchy
-  论文地址:https://arxiv.org//pdf/2310.09247 
-  开源代码:GitHub - yandex-research/text-to-img-hypernymy: Official code for "Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet Hierarchy" 

5.【多模态】Extending Multi-modal Contrastive Representations
-  论文地址:https://arxiv.org//pdf/2310.08884 
-  开源代码:GitHub - MCR-PEFT/Ex-MCR 

6.【多模态】From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
-  论文地址:https://arxiv.org//pdf/2310.08825 
-  开源代码(即将开源):GitHub - YuchenLiu98/COMM: Pytorch code for paper From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models 

7.【多模态】Making Multimodal Generation Easier: When Diffusion Models Meet LLMs
-  论文地址:https://arxiv.org//pdf/2310.08949 
-  开源代码:GitHub - zxy556677/EasyGen: The official code for paper "Making Multimodal Generation Easier: When Diffusion Models Meet LLMs" 

8.【GAN】Feature Proliferation -- the "Cancer" in StyleGAN and its Treatments
-  论文地址:https://arxiv.org//pdf/2310.08921 
-  开源代码:GitHub - songc42/Feature-proliferation 

9.【深度补全】LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
-  论文地址:https://arxiv.org//pdf/2310.08956 
-  工程主页:LRRU: Long-short Range Recurrent Updating Networks for Depth Completion 
-  开源代码(即将开源):GitHub - YufeiWang777/LRRU: Official implementation of ``LRRU: Long-short Range Recurrent Updating Networks for Depth Completion'', ICCV 2023. 

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