Video Action Segmentation via Contextually Refined Temporal Keypoints

Abstract

Video action segmentation involves categorizing each frame or short snippet of an untrimmed video into predefined action categories. Despite notable advancements in recent years, a considerable number of current approaches still rely on frame-wise segmentation that tends to render fragmentary results. To address it, we present an innovative approach for video action segmentation, centered around contextually refined temporal keypoints. Initially, our method identifies a set of sparse, over-complete temporal keypoints through non-local visual cues, with each keypoint representing a potential action segment candidate. Subsequent enhancements to these initial keypoints are achieved through iterative refining and re-assembling operations. Driven by the notion that optimal temporal keypoints should collectively resemble the true ground-truth structurally, we introduce a module that conducts graph matching between the keypoint-derived graph and the reference graph constructed from accurate annotations. This module effectively learns structural features used to further refine the initial keypoints. Moreover, a set of predefined rules is applied to re-assemble all temporal keypoints. The unfiltered temporal keypoints, resulting from these operations, are harnessed to generate the final action segments. We extensively evaluate our method across three video benchmarks: 50salads, GTEA, and Breakfast. Our proposed approach consistently demonstrates substantial improvements over existing methods, establishing its superiority in video action segmentation. It achieves F 1@50 scores (one of the key performance metrics for this task) of 79.5%, 83.4%, and 60.5%, respectively, v.s. previous state-of-the-art 78.5%, 79.8% and 57.4%.