Papers by Taiki Sekii
Flashback: Memory Mechanism for Enhancing Memory Efficiency and Speed in Deep Sequential Models (2025.coling-main)
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| Challenge: | Existing deep sequential processing models have problems with memory degradation and inaccurate gradient backpropagation. |
| Approach: | They propose a Flashback property that preserves memory as an identity mapping until it is overwritten by a hidden state at a different time step. |
| Outcome: | The proposed model can be implemented in Transformers and Mamba, and it performs well. |
A2O: LLM-based Agentic Learning of Action-to-Object Features for Video Action Recognition (2026.findings-acl)
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| Challenge: | Recent action recognition based on vision–language pretraining and self-supervised video foundation models tends to induce spurious correlations and shortcut learning by relying on action-irrelevant cues. |
| Approach: | They propose a framework in which an LLM agent integrates the two approaches within an agentic learning paradigm to design motion features tailored to the target actions. |
| Outcome: | The proposed model is based on the commonsense knowledge of large language models (LLMs) and the open vocabulary object detector to make the model attend to objects in a video required for recognizing the target actions. |