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In pursuit of a greater spatiotemporal modeling capability, our\napproach increases the transition depth between adjacent states by leveraging a\nnovel recurrent unit, which is named Causal LSTM for re-organizing the spatial\nand temporal memories in a cascaded mechanism. However, there is still a\ndilemma in video predictive learning: increasingly deep-in-time models have\nbeen designed for capturing complex variations, while introducing more\ndifficulties in the gradient back-propagation. To alleviate this undesirable\neffect, we propose a Gradient Highway architecture, which provides alternative\nshorter routes for gradient flows from outputs back to long-range inputs. This\narchitecture works seamlessly with causal LSTMs, enabling PredRNN++ to capture\nshort-term and long-term dependencies adaptively. We assess our model on both\nsynthetic and real video datasets, showing its ability to ease the vanishing\ngradient problem and yield state-of-the-art prediction results even in a\ndifficult objects occlusion scenario.","url_abs":"http://arxiv.org/abs/1804.06300v2","url_pdf":"http://arxiv.org/pdf/1804.06300v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/Yunbo426/predrnn-pp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/dzhv/Spatio-Temporal-mobile-traffic-forecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/stevenolvil/PredRNN-V2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/2023-MindSpore-1/ms-code-215/tree/main/predrnn%2B%2B","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/Flunzmas/vp-suite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/MS-Mind/MS-Code-06/tree/main/predrnn%2B%2B","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/Mind23-2/MindCode-5/tree/main/predrnn%2B%2B","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/predrnn%2B%2B","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/code-implementation1/Code6/tree/main/predrnn%2B%2B","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/mindspore-ai/models/tree/master/official/cv/predrnn%2B%2B","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"predrnn-towards-a-resolution-of-the-deep-in","repo_url":"https://github.com/thuml/predrnn-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"video-prediction","task_name":"Video Prediction"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-prediction-on-kth","task":"Video Prediction","dataset":"KTH","model":"PredRNN++","rank_in_archive_order":18,"of":31,"metrics":{"Cond":"10","PSNR":"28.47","Pred":"20","SSIM":"0.865"},"uses_additional_data":false},{"leaderboard":"/sota/video-prediction-on-moving-mnist","task":"Video Prediction","dataset":"Moving MNIST","model":"Causal LSTM","rank_in_archive_order":27,"of":31,"metrics":{"MAE":"106.8","MSE":"46.5","SSIM":"0.898"},"uses_additional_data":false},{"leaderboard":"/sota/video-prediction-on-synpickvp","task":"Video Prediction","dataset":"SynpickVP","model":"PredRNN++","rank_in_archive_order":2,"of":5,"metrics":{"LPIPS":"0.053","MSE":"51.73","PSNR":"27.50","SSIM":"0.894"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.06300","atlas_url":"https://app.syntology.ai/?focus=1804.06300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06300"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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