Koshiro Nagano | Searcharxiv

arXiv2026

Skeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining

We propose a novel pretraining strategy for skeleton-based zero-shot spatio-temporal action localization to estimate unseen actions for person instances while overcoming high annotation costs for training via new target actions and pretraining using large-scale action scenery datasets. Specifically, our approach, termed Skeleton-Language feature Pooling Switching, introduces a weakly-supervised vision-language pretraining mechanism. This mechanism transitions pooling kernels from pretraining, which aggregates skeleton features at the video level and aligns them with each video's known action text embeddings, to the inference phase that computes instance-level features without training via target actions. Furthermore, we propose Scene-Mixed Discriminative Contrastive Learning to distinguish actions at the instance level within the combined scene through the MIL framework. Our experiments on four public spatio-temporal action localization and classification datasets demonstrate that the proposed method effectively addresses annotation limitations.

cs.CV↗

arXiv2026

Learning from Synthetic Data via Provenance-Based Input Gradient Guidance

Learning methods using synthetic data have attracted attention as an effective approach for increasing the diversity of training data while reducing collection costs, thereby improving the robustness of model discrimination. However, many existing methods improve robustness only indirectly through the diversification of training samples and do not explicitly teach the model which regions in the input space truly contribute to discrimination; consequently, the model may learn spurious correlations caused by synthesis biases and artifacts. Motivated by this limitation, this paper proposes a learning framework that uses provenance information obtained during the training data synthesis process, indicating whether each region in the input space originates from the target object, as an auxiliary supervisory signal to promote the acquisition of representations focused on target regions. Specifically, input gradients are decomposed based on information about target and non-target regions during synthesis, and input gradient guidance is introduced to suppress gradients over non-target regions. This suppresses the model's reliance on non-target regions and directly promotes the learning of discriminative representations for target regions. Experiments demonstrate the effectiveness and generality of the proposed method across multiple tasks and modalities, including weakly supervised object localization, spatio-temporal action localization, and image classification.

cs.CV↗