MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909787114962944 |
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| author | Wasim, Syed Talal Suleman, Hamid Zatsarynna, Olga Naseer, Muzammal Gall, Juergen |
| author_facet | Wasim, Syed Talal Suleman, Hamid Zatsarynna, Olga Naseer, Muzammal Gall, Juergen |
| contents | We present MixANT, a novel architecture for stochastic long-term dense anticipation of human activities. While recent State Space Models (SSMs) like Mamba have shown promise through input-dependent selectivity on three key parameters, the critical forget-gate ($\textbf{A}$ matrix) controlling temporal memory remains static. We address this limitation by introducing a mixture of experts approach that dynamically selects contextually relevant $\textbf{A}$ matrices based on input features, enhancing representational capacity without sacrificing computational efficiency. Extensive experiments on the 50Salads, Breakfast, and Assembly101 datasets demonstrate that MixANT consistently outperforms state-of-the-art methods across all evaluation settings. Our results highlight the importance of input-dependent forget-gate mechanisms for reliable prediction of human behavior in diverse real-world scenarios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_11394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation Wasim, Syed Talal Suleman, Hamid Zatsarynna, Olga Naseer, Muzammal Gall, Juergen Computer Vision and Pattern Recognition We present MixANT, a novel architecture for stochastic long-term dense anticipation of human activities. While recent State Space Models (SSMs) like Mamba have shown promise through input-dependent selectivity on three key parameters, the critical forget-gate ($\textbf{A}$ matrix) controlling temporal memory remains static. We address this limitation by introducing a mixture of experts approach that dynamically selects contextually relevant $\textbf{A}$ matrices based on input features, enhancing representational capacity without sacrificing computational efficiency. Extensive experiments on the 50Salads, Breakfast, and Assembly101 datasets demonstrate that MixANT consistently outperforms state-of-the-art methods across all evaluation settings. Our results highlight the importance of input-dependent forget-gate mechanisms for reliable prediction of human behavior in diverse real-world scenarios. |
| title | MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.11394 |