{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/intention-conditioned-long-term-human","title":"Intention-Conditioned Long-Term Human Egocentric Action Forecasting","arxiv_id":"2207.12080","date":"2022-07-25","proceeding":null,"authors":["Esteve Valls Mascaro","Hyemin Ahn","Dongheui Lee"],"abstract":"To anticipate how a human would act in the future, it is essential to understand the human intention since it guides the human towards a certain goal. In this paper, we propose a hierarchical architecture which assumes a sequence of human action (low-level) can be driven from the human intention (high-level). Based on this, we deal with Long-Term Action Anticipation task in egocentric videos. Our framework first extracts two level of human information over the N observed videos human actions through a Hierarchical Multi-task MLP Mixer (H3M). Then, we condition the uncertainty of the future through an Intention-Conditioned Variational Auto-Encoder (I-CVAE) that generates K stable predictions of the next Z=20 actions that the observed human might perform. By leveraging human intention as high-level information, we claim that our model is able to anticipate more time-consistent actions in the long-term, thus improving the results over baseline methods in EGO4D Challenge. This work ranked first in both CVPR@2022 and ECVV@2022 EGO4D LTA Challenge by providing more plausible anticipated sequences, improving the anticipation of nouns and overall actions. Webpage: https://evm7.github.io/icvae-page/","url_abs":"https://arxiv.org/abs/2207.12080v4","url_pdf":"https://arxiv.org/pdf/2207.12080v4.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":"intention-conditioned-long-term-human","repo_url":"https://github.com/evm7/ego4dlta-icvae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"action-anticipation","task_name":"Action Anticipation"},{"task_slug":"long-term-action-anticipation","task_name":"Long Term Action Anticipation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.12080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12080"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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