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Semantic future prediction is important for autonomous systems navigating dynamic environments.
Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Deep multi-scale video prediction beyond mean square error
Michaël Mathieu, Camille Couprie, and Yann LeCun · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Learning to act by predicting the future
Alexey Dosovitskiy and Vladlen Koltun · 2017
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Unsupervised monocular depth estimation with left-right consistency
Clement Godard, Oisin Mac Aodha, and Gabriel J. Brostow · 2017
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Predicting scene parsing and motion dynamics in the future
Xiaojie Jin, Huaxin Xiao, Xiaohui Shen, Jimei Yang, Zhe Lin, Yunpeng Chen, Zequn Jie, Jiashi Feng, and Shuicheng Yan · 2017
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Predicting deeper into the future of semantic segmentation
Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, and Yann LeCun · 2017
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, and Sergey Levine · 2018
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Stochastic adversarial video prediction
Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Predicting future instance segmentation by forecasting convolutional features
Pauline Luc, Camille Couprie, Yann Lecun, and Jakob Verbeek · 2018
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Future semantic segmentation with convolutional lstm
Seyed Shahabeddin Nabavi, Mrigank Rochan, and Yang Wang · 2018
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Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model
George Papandreou, Tyler Zhu, Liang-Chieh Chen, Spyros Gidaris, Jonathan Tompson, and Kevin Murphy · 2018
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Eidetic 3d lstm: A model for video prediction and beyond
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, and Li Fei-Fei · 2018
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Structure preserving video prediction
Jingwei Xu, Bingbing Ni, Zefan Li, Shuo Cheng, and Xiaokang Yang · 2018
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Improved conditional vrnns for video prediction
Lluis Castrejon, Nicolas Ballas, and Aaron Courville · 2019
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Multi-timescale context encoding for scene parsing prediction
Xin Chen and Yahong Han · 2019
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3d motion decomposition for rgbd future dynamic scene synthesis
Xiaojuan Qi, Zhengzhe Liu, Qifeng Chen, and Jiaya Jia · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
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Single level feature-to-feature forecasting with deformable convolutions
Josip Šarić, Marin Oršić, Tonći Antunović, Sacha Vražić, and Siniša Šegvić · 2019
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Predicting future instance segmentation with contextual pyramid convlstms
Jiangxin Sun, Jiafeng Xie, Jian-Fang Hu, Zihang Lin, Jianhuang Lai, Wenjun Zeng, and Wei-Shi Zheng · 2019
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Recurrent flow-guided semantic forecasting
Adam Terwilliger, Garrick Brazil, and Xiaoming Liu · 2019
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
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Segmenting the future
Hsu-kuang Chiu, Ehsan Adeli, and Juan Carlos Niebles · 2020
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Probabilistic future prediction for video scene understanding
Anthony Hu, Fergal Cotter, Nikhil Mohan, Corina Gurau, and Alex Kendall · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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A review on deep learning techniques for video prediction
Sergiu Oprea, Pablo Martinez-Gonzalez, Alberto Garcia-Garcia, John Alejandro Castro-Vargas, Sergio Orts-Escolano, Jose Garcia-Rodriguez, and Antonis Argyros · 2020
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Joint forecasting of panoptic segmentations with difference attention
Colin Graber, Cyril Jazra, Wenjie Luo, Liangyan Gui, and Alexander G. Schwing · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Perceiver IO: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J Henaff, Matthew Botvinick, Andrew Zisserman, Oriol Vinyals, and Joao Carreira · 2022
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How far can i go?: A self-supervised approach for deterministic video depth forecasting
Sauradip Nag, Nisarg Shah, Anran Qi, and Raghavendra Ramachandra · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, and Evgeny Burnaev · 2020
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Warp to the future: Joint forecasting of features and feature motion
Josip Saric, Marin Orsic, Tonci Antunovic, Sacha Vrazic, and Sinisa Segvic · 2020
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2020
Cited alongside, same era.
On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
Cited alongside, same era.
Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Panoptic Segmentation Forecasting
Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, and Alexander Schwing · 2021
Cited alongside, same era.
Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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A pytorch reproduction of masked generative image transformer
Victor Besnier and Mickael Chen · 2023
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Imagebind: One embedding space to bind them all
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra · 2023
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Maskvit: Masked visual pre-training for video prediction
Agrim Gupta, Stephen Tian, Yunzhi Zhang, Jiajun Wu, Roberto Martín-Martín, and Li Fei-Fei · 2023
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Gaia-1: A generative world model for autonomous driving
Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, and Gianluca Corrado · 2023
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Meta-auxiliary learning for future depth prediction in videos
Huan Liu, Zhixiang Chi, Yuanhao Yu, Yang Wang, Jun Chen, and Jin Tang · 2023
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4m: Massively multimodal masked modeling
David Mizrahi, Roman Bachmann, Oguzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, and Amir Zamir · 2023
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Magvit: Masked generative video transformer
Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, et al · 2023
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4m-21: An any-to-any vision model for tens of tasks and modalities
Roman Bachmann, Oğuzhan Fatih Kar, David Mizrahi, Ali Garjani, Mingfei Gao, David Griffiths, Jiaming Hu, Afshin Dehghan, and Amir Zamir · 2024
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Sequential modeling enables scalable learning for large vision models
Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan L Yuille, Trevor Darrell, Jitendra Malik, and Alexei A Efros · 2024
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Flodcast: Flow and depth forecasting via multimodal recurrent architectures
Andrea Ciamarra, Federico Becattini, Lorenzo Seidenari, and Alberto Del Bimbo · 2024
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Vista: A generalizable driving world model with high fidelity and versatile controllability
Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, and Hongyang Li · 2024
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Videopoet: A large language model for zero-shot video generation
Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jose Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, Krishna Somandepalli, Hassan Akbari, Yair Alon, Yong Cheng, Joshua V. Dillon, Agrim Gupta, Meera Hahn, Anja Hauth, David Hendon, Alonso Martinez, David Minnen, Mikhail Sirotenko, Kihyuk Sohn, Xuan Yang, Hartwig Adam, Ming-Hsuan Yang, Irfan Essa, Huisheng Wang, David A Ross, Bryan Seybold, and Lu Jiang · 2024
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Autoregressive image generation without vector quantization
Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He · 2024
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Unified-io 2: Scaling autoregressive multimodal models with vision language audio and action
Jiasen Lu, Christopher Clark, Sangho Lee, Zichen Zhang, Savya Khosla, Ryan Marten, Derek Hoiem, and Aniruddha Kembhavi · 2024
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A simple and generalist approach for panoptic segmentation
Nedyalko Prisadnikov, Wouter Van Gansbeke, Danda Pani Paudel, and Luc Van Gool · 2024
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Generative multimodal models are in-context learners
Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiying Yu, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, and Xinlong Wang · 2024
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Florence-2: Advancing a unified representation for a variety of vision tasks
Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan · 2024
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Language model beats diffusion - tokenizer is key to visual generation
Lijun Yu, Jose Lezama, Nitesh Bharadwaj Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, Boqing Gong, Ming-Hsuan Yang, Irfan Essa, David A Ross, and Lu Jiang · 2024
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Photorealistic video generation with diffusion models
Agrim Gupta, Lijun Yu, Kihyuk Sohn, Xiuye Gu, Meera Hahn, Fei-Fei Li, Irfan Essa, Lu Jiang, and José Lezama · 2025
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Givt: Generative infinite-vocabulary transformers
Michael Tschannen, Cian Eastwood, and Fabian Mentzer · 2025
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