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The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness.
Towards fully autonomous driving: Systems and algorithms
Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, et al · 2011
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Machine learning: Trends, perspectives, and prospects
Michael I Jordan and Tom M Mitchell · 2015
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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An analysis of visual question answering algorithms
Kushal Kafle and Christopher Kanan · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
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Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John F. Canny, and Zeynep Akata · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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Aads: Augmented autonomous driving simulation using data-driven algorithms
Wei Li, CW Pan, Rong Zhang, JP Ren, YX Ma, Jin Fang, FL Yan, QC Geng, XY Huang, HJ Gong, et al · 2019
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Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
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A survey of end-to-end driving: Architectures and training methods
Ardi Tampuu, Tambet Matiisen, Maksym Semikin, Dmytro Fishman, and Naveed Muhammad · 2020
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Computing systems for autonomous driving: State of the art and challenges
Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyang Zhang, and Weisong Shi · 2020
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Mapping for autonomous driving: Opportunities and challenges
Kelvin Wong, Yanlei Gu, and Shunsuke Kamijo · 2020
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Evaluating the limits of a lidar for an autonomous driving localization
Lucas de Paula Veronese, Fernando Auat-Cheein, Filipe Mutz, Thiago Oliveira-Santos, José E Guivant, Edilson De Aguiar, Claudine Badue, and Alberto Ferreira De Souza · 2020
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A survey of autonomous driving: Common practices and emerging technologies
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and Kazuya Takeda · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
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Autonomy 2.0: Why is self-driving always 5 years away?
Ashesh Jain, Luca Del Pero, Hugo Grimmett, and Peter Ondruska · 2021
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Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2021
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant · 2021
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Meta-learning via language model in-context tuning
Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, and He He · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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A review of end-to-end autonomous driving in urban environments
Daniel Coelho and Miguel Oliveira · 2022
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan · 2022
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Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Srinivas Iyer, Xiaojuan Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O’Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, and Veselin Stoyanov · 2022
Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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VideoMAE: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
Cited alongside, same era.
Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Mohsen Soori, Behrooz Arezoo, and Roza Dastres · 2023
Cited alongside, same era.
Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
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Valley: Video assistant with large language model enhanced ability
Ruipu Luo, Ziwang Zhao, Min Yang, Junwei Dong, Minghui Qiu, Pengcheng Lu, Tao Wang, and Zhongyu Wei · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al · 2023
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Embodiedgpt: Vision-language pre-training via embodied chain of thought
Yao Mu, Qinglong Zhang, Mengkang Hu, Wenhai Wang, Mingyu Ding, Jun Jin, Bin Wang, Jifeng Dai, Yu Qiao, and Ping Luo · 2023
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The evolution of automotive technology: a handbook
Gijs Mom · 2023
Cited alongside, same era.
End-to-end autonomous driving: Challenges and frontiers
Li Chen, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li · 2023
Cited alongside, same era.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Cited alongside, same era.
Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao · 2023
Cited alongside, same era.
Openflamingo: An open-source framework for training large autoregressive vision-language models
Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt · 2023
Cited alongside, same era.
Svit: Scaling up visual instruction tuning
Bo Zhao, Boya Wu, and Tiejun Huang · 2023
Cited alongside, same era.
Drive like a human: Rethinking autonomous driving with large language models
Daocheng Fu, Xin Li, Licheng Wen, Min Dou, Pinlong Cai, Botian Shi, and Yu Qiao · 2023
Cited alongside, same era.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
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Octopus: Embodied vision-language programmer from environmental feedback
Jingkang Yang, Yuhao Dong, Shuai Liu, Bo Li, Ziyue Wang, Chencheng Jiang, Haoran Tan, Jiamu Kang, Yuanhan Zhang, Kaiyang Zhou, et al · 2023
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3d-llm: Injecting the 3d world into large language models
Yining Hong, Haoyu Zhen, Peihao Chen, Shuhong Zheng, Yilun Du, Zhenfang Chen, and Chuang Gan · 2023
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Pointllm: Empowering large language models to understand point clouds
Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, and Dahua Lin · 2023
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Medalpaca–an open-source collection of medical conversational ai models and training data
Tianyu Han, Lisa C Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K Bressem · 2023
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Med-flamingo: a multimodal medical few-shot learner
Michael Moor, Qian Huang, Shirley Wu, Michihiro Yasunaga, Cyril Zakka, Yash Dalmia, Eduardo Pontes Reis, Pranav Rajpurkar, and Jure Leskovec · 2023
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Marinegpt: Unlocking secrets of ocean to the public
Ziqiang Zheng, Jipeng Zhang, Tuan-Anh Vu, Shizhe Diao, Yue Him Wong Tim, and Sai-Kit Yeung · 2023
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Xinpeng Ding, Jianhua Han, Hang Xu, Wei Zhang, and Xiaomeng Li · 2023
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Drivegpt4: Interpretable end-to-end autonomous driving via large language model
Zhenhua Xu, Yujia Zhang, Enze Xie, Zhen Zhao, Yong Guo, Kenneth K.Y. Wong, Zhenguo Li, and Hengshuang Zhao · 2023
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Mimic-it: Multi-modal in-context instruction tuning
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Fanyi Pu, Jingkang Yang, C. Li, and Ziwei Liu · 2023
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Gpt4roi: Instruction tuning large language model on region-of-interest
Shilong Zhang, Peize Sun, Shoufa Chen, Min Xiao, Wenqi Shao, Wenwei Zhang, Kai Chen, and Ping Luo · 2023
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Shikra: Unleashing multimodal llm’s referential dialogue magic
Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao · 2023
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https://chat.openai.com
Openai chat · 2023
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The flan collection: Designing data and methods for effective instruction tuning
S. Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, and Adam Roberts · 2023
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Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matthew M. Botvinick, Jane X. Wang, and Eric Schulz · 2023
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Multimodal c4: An open, billion-scale corpus of images interleaved with text
Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre, Jesse Dodge, Alex Fang, Youngjae Yu, Ludwig Schmidt, William Yang Wang, and Yejin Choi · 2023
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Understanding multimodal instruction format for in-context learning
Anonymous · 2023
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Video-llama: An instruction-tuned audio-visual language model for video understanding
Hang Zhang, Xin Li, and Lidong Bing · 2023
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Videochat: Chat-centric video understanding
Kunchang Li, Yinan He, Yi Wang, Yizhuo Li, Wen Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao · 2023
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
Closest in time.
Valley: Video assistant with large language model enhanced ability
Ruipu Luo, Ziwang Zhao, Min Yang, Junwei Dong, Ming-Hui Qiu, Pengcheng Lu, Tao Wang, and Zhongyu Wei · 2023
Closest in time.
https://hanlab.mit.edu/blog/tinychat
Tinychat: Large language model on the edge · 2023
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