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Original Paper: https://arxiv.org/abs/2309.04379 By: Dongming Wu, Wencheng Han, Tiancai Wang, Yingfei Liu, Xiangyu Zhang, Jianbing Shen Abstract: A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of
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Original Paper: https://arxiv.org/abs/2309.04269 By: Griffin Adams, Alexander Fabbri, Faisal Ladhak, Eric Lehman, Noémie Elhadad Abstract: Selecting the ``right'' amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and
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Original Paper: https://arxiv.org/abs/2306.00526 By: Wenjin Wang, Yunhao Li, Yixin Ou, Yin Zhang Abstract: Layout-aware pre-trained models has achieved significant progress on document image question answering. They introduce extra learnable modules into existing language models to capture layout information within document images from text bounding box
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Original Paper: https://arxiv.org/abs/2309.02848 By: Xuanwen Huang, Kaiqiao Han, Dezheng Bao, Quanjin Tao, Zhisheng Zhang, Yang Yang, Qi Zhu Abstract: Text-attributed Graphs (TAGs) are commonly found in the real world, such as social networks and citation networks, and consist of nodes represented by textual descriptions. Currently,
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Original Paper: https://arxiv.org/abs/2309.01219 By: Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi Abstract: While large language models (LLMs) have demonstrated remarkable capabilities
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Original Paper: https://arxiv.org/abs/2308.09658 By: Pengbo Hu, Ji Qi, Xingyu Li, Hong Li, Xinqi Wang, Bing Quan, Ruiyu Wang, Yi Zhou Abstract: There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This
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Original Paper: https://arxiv.org/abs/2303.15649 By: Senmao Li, Joost van de Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, Jian Yang Abstract: A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images. They either finetune
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Original Paper: https://arxiv.org/abs/2308.10173 By: Zhixiao Qi, Yijiong Yu, Meiqi Tu, Junyi Tan, Yongfeng Huang Abstract: Currently, the construction of large language models in specific domains is done by fine-tuning on a base model. Some models also incorporate knowledge bases without the need for pre-training. This
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Original Paper: https://arxiv.org/abs/2308.09687v2 By: Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler Abstract: We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond
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Original Paper: https://arxiv.org/abs/2308.09718 By: Xiaoyang Wu, Zhuotao Tian, Xin Wen, Bohao Peng, Xihui Liu, Kaicheng Yu, Hengshuang Zhao Abstract: The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited
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Original Paper: https://arxiv.org/abs/2308.08614 By: Bin Lei, pei-Hung Lin, Chunhua Liao, Caiwen Ding Abstract: Recent advancements in large-scale models, such as GPT-4, have showcased remarkable capabilities in addressing standard queries. However, when facing complex problems that require multi-step logical reasoning, their accuracy dramatically decreases. Current research
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Original Paper: https://arxiv.org/abs/2302.12449 By: Yun Zhu, Jianhao Guo, Siliang Tang Abstract: Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and