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Original Paper: https://arxiv.org/abs/2312.06185 By: Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha, Zailiang Yu, Xiao Huang Abstract: Generative Large Language Models (LLMs), such as ChatGPT, offer interactive APIs that can answer common questions at a human-expert level. However, these models often give inaccurate or incorrect
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Original Paper: https://arxiv.org/abs/2312.06660 By: Chong Zhou, Xiangtai Li, Chen Change Loy, Bo Dai Abstract: This paper presents EdgeSAM, an accelerated variant of the Segment Anything Model (SAM), optimized for efficient execution on edge devices with minimal compromise in performance. Our approach involves distilling the original
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Original Paper: https://arxiv.org/abs/2311.15317 By: Xingtong Yu, Zhenghao Liu, Yuan Fang, Zemin Liu, Sihong Chen, Xinming Zhang Abstract: Graph neural networks have emerged as a powerful tool for graph representation learning, but their performance heavily relies on abundant task-specific supervision. To reduce labeling requirement, the "
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Original Paper: https://arxiv.org/abs/2312.05038 By: Zilong Li, Yiming Lei, Chenglong Ma, Junping Zhang, Hongming Shan Abstract: Image restoration, which aims to retrieve and enhance degraded images, is fundamental across a wide range of applications. While conventional deep learning approaches have notably improved the image quality across
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Original Paper: https://arxiv.org/abs/2310.00085 By: Haechan Mark Bong, Rongge Zhang, Ricardo de Azambuja, Giovanni Beltrame Abstract: From industrial to space robotics, safe landing is an essential component for flight operations. With the growing interest in artificial intelligence, we direct our attention to learning based safe landing
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Original Paper: https://arxiv.org/abs/2309.02833 By: In-Ug Yoon, Tae-Min Choi, Sun-Kyung Lee, Young-Min Kim, Jong-Hwan Kim Abstract: While many FSCIL studies have been undertaken, achieving satisfactory performance, especially during incremental sessions, has remained challenging. One prominent challenge is that the encoder, trained with an ample base session
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Original Paper: https://arxiv.org/abs/2310.16427 By: Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu Abstract: Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep
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Original Paper: https://arxiv.org/abs/2312.01801 By: Yihan Liu, Zhen Wen, Luoxuan Weng, Ollie Woodman, Yi Yang, Wei Chen Abstract: The rapid development of large language models (LLMs), such as ChatGPT, has revolutionized the efficiency of creating programming tutorials. LLMs can be instructed with text prompts to generate
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Original Paper: https://arxiv.org/abs/2303.13534 By: Jonas Oppenlaender, Rhema Linder, Johanna Silvennoinen Abstract: We are witnessing a novel era of creativity where anyone can create digital content via prompt-based learning (known as prompt engineering). This paper delves into prompt engineering as a novel creative skill for creating
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Original Paper: https://arxiv.org/abs/2312.02201 By: Peng Wang, Yichun Shi Abstract: We introduce "ImageDream," an innovative image-prompt, multi-view diffusion model for 3D object generation. ImageDream stands out for its ability to produce 3D models of higher quality compared to existing state-of-the-art, image-conditioned methods. Our approach
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Original Paper: https://arxiv.org/abs/2305.03047 By: Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, Chuang Gan Abstract: Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback (RLHF) to align
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We’ve spoken to over 50 developers building RAG-based applications last month. Here are 5 Common Mistakes an LLM might make: 1. Bad Retrievals: This normally happens when the context provided is irrelevant to the query. 2. Unfaithful Responses: Often, the response doesn't stay true to the context
