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Original Paper: https://arxiv.org/abs/2311.16194 By: Jiawang Bai, Kuofeng Gao, Shaobo Min, Shu-Tao Xia, Zhifeng Li, Wei Liu Abstract: Contrastive Vision-Language Pre-training, known as CLIP, has shown promising effectiveness in addressing downstream image recognition tasks. However, recent works revealed that the CLIP model can be implanted with
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Original Paper: https://arxiv.org/abs/2312.02918 By: Yuang Ai, Huaibo Huang, Xiaoqiang Zhou, Jiexiang Wang, Ran He Abstract: Despite substantial progress, all-in-one image restoration (IR) grapples with persistent challenges in handling intricate real-world degradations. This paper introduces MPerceiver: a novel multimodal prompt learning approach that harnesses Stable Diffusion
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Original Paper: https://arxiv.org/abs/2307.01187 By: Haixing Dai, Chong Ma, Zhiling Yan, Zhengliang Liu, Enze Shi, Yiwei Li, Peng Shu, Xiaozheng Wei, Lin Zhao, Zihao Wu, Fang Zeng, Dajiang Zhu, Wei Liu, Quanzheng Li, Lichao Sun, Shu Zhang Tianming Liu, Xiang Li Abstract: This paper introduces SAMAug,
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Original Paper: https://arxiv.org/abs/2403.11446 By: Clint Morris, Michael Jurado, Jason Zutty Abstract: In the realm of machine learning, traditional model development and automated approaches like AutoML typically rely on layers of abstraction, such as tree-based or Cartesian genetic programming. Our study introduces "Guided Evolution"
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Original Paper: https://arxiv.org/abs/2312.14091 By: Hayk Manukyan, Andranik Sargsyan, Barsegh Atanyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi Abstract: Recent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results. However, there is still
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Original Paper: https://arxiv.org/abs/2312.03724 By: Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng Li, Bo Li, Zhangyang Wang Abstract: Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding data privacy
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Original Paper: https://arxiv.org/abs/2310.18961 By: Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen Abstract: Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data
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Original Paper: https://arxiv.org/abs/2403.10131 By: Tianjun Zhang, Shishir G. Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, Joseph E. Gonzalez Abstract: Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications,
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Original Paper: https://arxiv.org/html/2403.20127v1 AI has come a long way, with tools like GPT-3 making big waves. They're super useful, but there's a downside: they can create fake news or copy someone else's work. To stop this, experts have come
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Original Paper: https://arxiv.org/abs/2311.08364 By: Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, Kashun Shum, Renjie Pi, Jipeng Zhang, Tong Zhang Abstract: Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts,
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Original Paper: https://arxiv.org/abs/2403.09611 By: Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, Anton Belyi, Haotian Zhang, Karanjeet Singh, Doug Kang, Ankur Jain, Hongyu Hè, Max Schwarzer, Tom Gunter, Xiang Kong, Aonan Zhang, Jianyu
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Original Paper: https://arxiv.org/abs/2403.08950 By: Michael Desmond, Michelle Brachman Abstract: Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. In theory, natural language prompts enable non-experts
