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SOTA2 Research · papers
Find papers, implementations, and the benchmark evidence behind state-of-the-art AI systems.
| Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation | Jinming Su, Ruihong Yin, Xingyue Chen | 2023 | arxiv 2304.08826 |
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| MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation | Sanghyun Jo, In-Jae Yu, Kyungsu Kim | 2023 | arxiv 2304.09913 |
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| Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability | Svetlana Pavlitska, Christian Hubschneider, Lukas Struppek | 2022 | arxiv 2204.10598 |
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| Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness | Liangliang Cao, Bowen Zhang, Chen Chen | 2023 | arxiv 2305.05095 |
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| CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision | Aman Shrivastava, Ramprasaath R. Selvaraju, Nikhil Naik | 2021 | arxiv 2112.07133 |
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| ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images | Cuong Ly, Grayson Jorgenson, Dan Rosa de Jesus | 2023 | arxiv 2305.13509 |
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| HAAV: Hierarchical Aggregation of Augmented Views for Image Captioning | Chia-Wen Kuo, Zsolt Kira | 2023 | arxiv 2305.16295 |
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| Human-imperceptible, Machine-recognizable Images | Fusheng Hao, Fengxiang He, Yikai Wang | 2023 | arxiv 2306.03679 |
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| SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation | Huaishao Luo, Junwei Bao, Youzheng Wu | 2022 | arxiv 2211.14813 |
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| Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning | Shaofeng Zhang, Feng Zhu, Rui Zhao | 2023 | arxiv 2306.13337 |
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