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• The proposed lightweight dual-loss training framework improves fine-grained segmentation quality with only one additional output channel and two auxiliary loss terms.method 90%
• The framework requires minimal architectural modification to existing TAS models.technical 85%
• The boundary-regression loss promotes accurate temporal localization via a single-channel boundary prediction.method 90%
• The CDF-based segment-level regularization loss encourages coherent within-segment structure by matching cumulative distributions over predicted and ground-truth segments.method 90%
• The framework is architecture-agnostic and can be integrated into existing TAS models (e.g., MS-TCN, C2F-TCN, FACT) as a training-time loss function.technical 90%
• Across three benchmark datasets, the proposed method improves segment-level consistency and boundary quality, yielding higher F1 and Edit scores across three different models.result 85%
• Frame-wise accuracy remains largely unchanged with the proposed method.result 80%
• Precise segmentation can be achieved through simple loss design rather than heavier architectures or inference-time refinements.technical 75%

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