TBT-Former: Learning Temporal Boundary Distributions for Action Localization

• URL: http://arxiv.org/abs/2512.01298v1
• Date: Mon, 01 Dec 2025 05:38:13 GMT
• Title: TBT-Former: Learning Temporal Boundary Distributions for Action Localization
• Authors: Thisara Rathnayaka, Uthayasanker Thayasivam,
• Abstract summary: Temporal Boundary Transformer (TBT-Former) is a new architecture for temporal action localization.<n>Inspired by the principles of Generalized Focal Loss (GFL), this new head recasts the challenging task of boundary regression as a more flexible probability distribution learning problem.<n>TBT-Former sets a new level of performance on the highly competitive THUMOS14 and EPIC-Kitchens 100 datasets.
• Score: 1.2461503242570642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal Action Localization (TAL) remains a fundamental challenge in video understanding, aiming to identify the start time, end time, and category of all action instances within untrimmed videos. While recent single-stage, anchor-free models like ActionFormer have set a high standard by leveraging Transformers for temporal reasoning, they often struggle with two persistent issues: the precise localization of actions with ambiguous or "fuzzy" temporal boundaries and the effective fusion of multi-scale contextual information. In this paper, we introduce the Temporal Boundary Transformer (TBT-Former), a new architecture that directly addresses these limitations. TBT-Former enhances the strong ActionFormer baseline with three core contributions: (1) a higher-capacity scaled Transformer backbone with an increased number of attention heads and an expanded Multi-Layer Perceptron (MLP) dimension for more powerful temporal feature extraction; (2) a cross-scale feature pyramid network (FPN) that integrates a top-down pathway with lateral connections, enabling richer fusion of high-level semantics and low-level temporal details; and (3) a novel boundary distribution regression head. Inspired by the principles of Generalized Focal Loss (GFL), this new head recasts the challenging task of boundary regression as a more flexible probability distribution learning problem, allowing the model to explicitly represent and reason about boundary uncertainty. Within the paradigm of Transformer-based architectures, TBT-Former advances the formidable benchmark set by its predecessors, establishing a new level of performance on the highly competitive THUMOS14 and EPIC-Kitchens 100 datasets, while remaining competitive on the large-scale ActivityNet-1.3. Our code is available at https://github.com/aaivu/In21-S7-CS4681-AML-Research-Projects/tree/main/projects/210536K-Multi-Modal -Learning_Video-Understanding

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