PESFormer: Boosting Macro- and Micro-expression Spotting with Direct Timestamp Encoding
This work addresses the challenge of precisely localizing sparse and varying-duration expressions in videos, which is important for applications like emotion analysis, but it appears incremental as it builds on existing vision transformer architectures with specific modifications.
The paper tackles the problem of macro- and micro-expression spotting in untrimmed videos by introducing PESFormer, which uses direct timestamp encoding to replace anchor-based methods and zero-padding to preserve training intervals, resulting in state-of-the-art performance on three datasets.
The task of macro- and micro-expression spotting aims to precisely localize and categorize temporal expression instances within untrimmed videos. Given the sparse distribution and varying durations of expressions, existing anchor-based methods often represent instances by encoding their deviations from predefined anchors. Additionally, these methods typically slice the untrimmed videos into fixed-length sliding windows. However, anchor-based encoding often fails to capture all training intervals, and slicing the original video as sliding windows can result in valuable training intervals being discarded. To overcome these limitations, we introduce PESFormer, a simple yet effective model based on the vision transformer architecture to achieve point-to-interval expression spotting. PESFormer employs a direct timestamp encoding (DTE) approach to replace anchors, enabling binary classification of each timestamp instead of optimizing entire ground truths. Thus, all training intervals are retained in the form of discrete timestamps. To maximize the utilization of training intervals, we enhance the preprocessing process by replacing the short videos produced through the sliding window method.Instead, we implement a strategy that involves zero-padding the untrimmed training videos to create uniform, longer videos of a predetermined duration. This operation efficiently preserves the original training intervals and eliminates video slice enhancement.Extensive qualitative and quantitative evaluations on three datasets -- CAS(ME)^2, CAS(ME)^3 and SAMM-LV -- demonstrate that our PESFormer outperforms existing techniques, achieving the best performance.