Improving Viewpoint-Invariance and Temporal Consistency for Action Detection

Problem
This paper addresses the limitations of existing action detection methods in untrimmed videos, specifically focusing on viewpoint change invariance and temporal consistency. Current appearance-based methods often lack sufficient viewpoint diversity during training, leading to suboptimal performance in real-world scenarios. Additionally, motion-based approaches typically fail to capture fine-grained temporal relationships across consecutive motion windows. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose a two-stage action detection framework aimed at enhancing viewpoint invariance and temporal coherence. In the first stage, they utilize augmented virtual viewpoints to extract motion features, which are exclusively employed during the training phase. This augmentation allows the model to learn from a broader range of perspectives, thereby improving its robustness to viewpoint variations. The second stage introduces a novel view-invariant, multi-scale temporal encoder that leverages selective state-space sequence modeling. This encoder aggregates information across different viewpoints and time scales, effectively modeling the temporal dynamics of actions. The architecture details, including the specific configurations of the encoder and the training compute, are not disclosed in the abstract.

Results
The proposed method was evaluated on the PKU-MMD and BABEL benchmarks, where it demonstrated significant improvements over state-of-the-art action detection methods. The authors report that their approach outperformed existing baselines across all considered splits, although specific numerical results and effect sizes are not provided in the abstract. The improvements in viewpoint invariance and temporal consistency are expected to translate into better action detection performance in diverse and unstructured video environments.

Limitations
The authors acknowledge that their approach relies heavily on the quality and diversity of the augmented virtual viewpoints used during training. If the augmentation does not sufficiently cover the range of possible real-world viewpoints, the model’s performance may still be limited. Additionally, the paper does not discuss the computational overhead introduced by the two-stage architecture, which may impact real-time applications. Other potential limitations include the generalizability of the model to other datasets not covered in the experiments and the absence of a detailed analysis of failure cases.

Why it matters
This work has significant implications for the field of action detection, particularly in scenarios involving untrimmed videos where viewpoint diversity and temporal consistency are critical. By addressing these gaps, the proposed method could enhance the robustness and accuracy of action detection systems in real-world applications, such as surveillance, sports analytics, and human-computer interaction. The availability of code and trained models further facilitates downstream research and development, allowing other researchers to build upon these findings.

Authors: Yannick Porto, Renato Martins, Thomas Chalumeau, Cedric Demonceaux
Source: arXiv cs.CV
URL: https://arxiv.org/abs/2605.22695v1