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Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” 2016
2016
Earlier work this paper cites.
A. B. Vasudevan, D. Dai, and L. V. Gool, “Object referring in videos with language and human gaze,” 2018
2018
Earlier work this paper cites.
E. Wijmans, S. Datta, O. Maksymets, A. Das, G. Gkioxari, S. Lee, I. Essa, D. Parikh, and D. Batra, “Embodied question answering in photorealistic environments with point cloud perception,” 2019
2019
Earlier work this paper cites.
S. N N, T. Maniar, J. Kalyanasundaram, V. Gandhi, B. Bhowmick, and M. Krishna, “Talk to the vehicle: Language conditioned autonomous navigation of self driving cars,” 11 2019, pp. 5284–5290
2019
Earlier work this paper cites.
T. Deruyttere, S. Vandenhende, D. Grujicic, L. V. Gool, and M.-F. Moens, “Talk2car: Taking control of your self-driving car,” in Conference on Empirical Methods in Natural Language Processing , 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:202734592
2019
Earlier work this paper cites.
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” 2019
2019
Earlier work this paper cites.
J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” 2020
2020
Earlier work this paper cites.
P. Achlioptas, A. Abdelreheem, F. Xia, M. Elhoseiny, and L. J. Guibas, “Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes,” in European Conference on Computer Vision , 2020. [Online]. Available: https://api.semanticscholar.org/CorpusID:221378802
2020
Earlier work this paper cites.
D. Z. Chen, A. X. Chang, and M. Nießner, “Scanrefer: 3d object localization in rgb-d scans using natural language,” 2020
2020
Earlier work this paper cites.
D. Z. Chen, A. Gholami, M. Nießner, and A. X. Chang, “Scan2cap: Context-aware dense captioning in rgb-d scans,” 2020
2020
Earlier work this paper cites.
S.-H. Chou, W.-L. Chao, W.-S. Lai, M. Sun, and M.-H. Yang, “Visual question answering on 360-degree images,” 2020
2020
Earlier work this paper cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” 2020
2020
Earlier work this paper cites.
K. Mani, S. Daga, S. Garg, S. Shankar, K. Jatavallabhula, and M. K, “Monolayout: Amodal scene layout from a single image,” in WACV , 2020
2020
Earlier work this paper cites.
K. Mani, S. Shankar, K. Jatavallabhula, and M. K, “Autolay: Benchmarking monocular layout estimation,” in IROS , 2020
2020
Earlier work this paper cites.
Y. Du, C. Li, R. Guo, X. Yin, W. Liu, J. Zhou, Y. Bai, Z. Yu, Y. Yang, Q. Dang, and H. Wang, “Pp-ocr: A practical ultra lightweight ocr system,” 2020
2020
Earlier work this paper cites.
OpenAI. (2021) Chatgpt. Accessed: yyyy-mm-dd. [Online]. Available: https://www.openai.com/
2021
Earlier work this paper cites.
A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V. Badrinarayanan, R. Cipolla, and A. Kendall, “Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,” 2021
2021
Cited alongside, same era.
P.-H. Huang, H.-H. Lee, H.-T. Chen, and T.-L. Liu, “Text-guided graph neural networks for referring 3d instance segmentation,” in AAAI Conference on Artificial Intelligence , 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:235306096
2021
Cited alongside, same era.
M. Feng, Z. Li, Q. Li, L. Zhang, X. Zhang, G. Zhu, H. Zhang, Y. Wang, and A. Mian, “Free-form description guided 3d visual graph network for object grounding in point cloud,” 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
OpenAI, “Gpt-4 technical report,” 2023
2023
Closest in time.
W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, and S. Hoi, “Instructblip: Towards general-purpose vision-language models with instruction tuning,” 2023
2023
Closest in time.
C. Fu, P. Chen, Y. Shen, Y. Qin, M. Zhang, X. Lin, Z. Qiu, W. Lin, J. Yang, X. Zheng, K. Li, X. Sun, and R. Ji, “Mme: A comprehensive evaluation benchmark for multimodal large language models,” 2023
2023
Closest in time.
Y. Liu, H. Duan, Y. Zhang, B. Li, S. Zhang, W. Zhao, Y. Yuan, J. Wang, C. He, Z. Liu, K. Chen, and D. Lin, “Mmbench: Is your multi-modal model an all-around player?” 2023
2023
Closest in time.
P. Xu, W. Shao, K. Zhang, P. Gao, S. Liu, M. Lei, F. Meng, S. Huang, Y. Qiao, and P. Luo, “Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models,” 2023
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H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani, S. Brahma, A. Webson, S. S. Gu, Z. Dai, M. Suzgun, X. Chen, A. Chowdhery, A. Castro-Ros, M. Pellat, K. Robinson, D. Valter, S. Narang, G. Mishra, A. Yu, V. Zhao, Y. Huang, A. Dai, H. Yu, S. Petrov, E. H. Chi, J. Dean, J. Devlin, A. Roberts, D. Zhou, Q. V. Le, and J. Wei, “Scaling instruction-finetuned language models,” 2022
2022
Cited alongside, same era.
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Q. Yu, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” 2022
2022
Cited alongside, same era.
S. Hu, L. Chen, P. Wu, H. Li, J. Yan, and D. Tao, “St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning,” 2022
2022
Cited alongside, same era.
D. Azuma, T. Miyanishi, S. Kurita, and M. Kawanabe, “Scanqa: 3d question answering for spatial scene understanding,” 2022
2022
Cited alongside, same era.
J. Wu, J. Wang, Z. Yang, Z. Gan, Z. Liu, J. Yuan, and L. Wang, “Grit: A generative region-to-text transformer for object understanding,” 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” 2023
2023
Cited alongside, same era.
D. Zhu, J. Chen, X. Shen, X. Li, and M. Elhoseiny, “Minigpt-4: Enhancing vision-language understanding with advanced large language models,” 2023
2023
Cited alongside, same era.
2023
Closest in time.
B. Li, R. Wang, G. Wang, Y. Ge, Y. Ge, and Y. Shan, “Seed-bench: Benchmarking multimodal llms with generative comprehension,” 2023
2023
Closest in time.
Y. Hong, H. Zhen, P. Chen, S. Zheng, Y. Du, Z. Chen, and C. Gan, “3d-llm: Injecting the 3d world into large language models,” 2023
2023
Closest in time.
R. Xu, X. Wang, T. Wang, Y. Chen, J. Pang, and D. Lin, “Pointllm: Empowering large language models to understand point clouds,” 2023
2023
Closest in time.
D. Wu, W. Han, T. Wang, Y. Liu, X. Zhang, and J. Shen, “Language prompt for autonomous driving,” 2023
2023
Closest in time.
D. Wu, W. Han, T. Wang, X. Dong, X. Zhang, and J. Shen, “Referring multi-object tracking,” 2023
2023
Closest in time.
T. Qian, J. Chen, L. Zhuo, Y. Jiao, and Y.-G. Jiang, “Nuscenes-qa: A multi-modal visual question answering benchmark for autonomous driving scenario,” 2023
2023
Closest in time.
Wayve, “Lingo-1: Exploring natural language for autonomous driving,” https://wayve.ai/thinking/lingo-natural-language-autonomous-driving/ , Year, accessed: 2 October 2023
2023
Closest in time.
X. Zhao, W. Ding, Y. An, Y. Du, T. Yu, M. Li, M. Tang, and J. Wang, “Fast segment anything,” 2023
2023
Closest in time.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
Closest in time.
K. M. Jatavallabhula, A. Kuwajerwala, Q. Gu, M. Omama, T. Chen, S. Li, G. Iyer, S. Saryazdi, N. Keetha, A. Tewari, J. B. Tenenbaum, C. M. de Melo, M. Krishna, L. Paull, F. Shkurti, and A. Torralba, “Conceptfusion: Open-set multimodal 3d mapping,” 2023
2023
Closest in time.
2023
Closest in time.