LiquidTAD: An Efficient Method for Temporal Action Detection via Liquid Neural Dynamics

Temporal Action Detection (TAD) in untrimmed videos is currently dominated by Transformer-based architectures. While high-performing, their quadratic computational complexity and substantial parameter redundancy limit deployment in resource-constrained environments. In this paper, we propose LiquidTAD, a novel parameter-efficient framework that replaces cumbersome self-attention layers with parallelized ActionLiquid blocks. Unlike traditional Liquid Neural Networks (LNNs) that suffer from sequential execution bottlenecks, LiquidTAD leverages a closed-form continuous-time (CfC) formulation, allowing the model to be reformulated as a parallelizable operator while preserving the intrinsic physical prior of continuous-time dynamics. This architecture captures complex temporal dependencies with $O(N)$ linear complexity and adaptively modulates temporal sensitivity through learned time-constants ($τ$), providing a robust mechanism for handling varying action durations. To the best of our knowledge, this work is the first to introduce a parallelized LNN-based architecture to the TAD domain. Experimental results on the THUMOS-14 dataset demonstrate that LiquidTAD achieves a highly competitive Average mAP of 69.46\% with only 10.82M parameters -- a 63\% reduction compared to the ActionFormer baseline. Further evaluations on ActivityNet-1.3 and Ego4D benchmarks confirm that LiquidTAD achieves an optimal accuracy-efficiency trade-off and exhibits superior robustness to temporal sampling variations, advancing the Pareto frontier of modern TAD frameworks.