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Seeing Semantic Shift: Difference-Aware Sentence-Level Temporal Segmentation of Sign Language Videos
Bowen Guo, Shiwei Gan, Yafeng Yin, Xiao Liu, Kuizhuang Liu, Zhiwei Jiang, Lei Xie
TL;DR
Long continuous sign-language videos are challenging for recognition and translation, and visual-only sentence-level segmentation targets their boundaries without captions. SignShift uses multi-scale temporal differences from global, hand, and facial features alongside a sentence-count prior; the paper reports more accurate segmentation than state-of-the-art baselines.
Problem
Recognition and translation methods that perform well on short single-sentence videos deteriorate on long continuous signing, motivating visual-only sentence-level segmentation without captions.
Source: §1 · p. 1Method
SignShift fuses multi-scale temporal differences from global, hand, and facial features with a predicted sentence count to guide boundary selection.
Source: §1 · p. 2, §3.3 · p. 4, §3.4 · p. 4Results
SignShift achieves significantly more accurate segmentation than state-of-the-art baselines on benchmark datasets.
Source: §1 · p. 2Takeaways & Limitations
Visual-only sentence-level segmentation is presented as a prerequisite for downstream sign-language recognition and translation on realistic long videos.
Source: §1 · p. 1
Abstract
from arXiv · showhide
Recent advances in sign language understanding have achieved impressive success on short, single-sentence videos, yet their performance drops sharply when applied to long, continuous sign language videos. To bridge this gap, we focus on a challenging and realistic setting: Visual-only Sentence-level Sign Language Segmentation (Vis-SSLS), which aims to partition continuous sign language videos into non-overlapping sentence-level segments without any caption assistance, serving as a crucial prerequisite for downstream recognition and translation tasks. However, sentence transitions in sign language are often smooth and visually ambiguous, lacking explicit pauses or posture resets. As a result, static frame representations may fail to capture the subtle temporal changes that indicate sentence boundaries. To address this challenge, we propose \textbf{SignShift}, a difference-aware segmentation framework that explicitly models frame-to-frame feature variation as semantic cues for sentence boundary detection. First, to model the feature variation, we design a Temporal Difference Module, which incorporates full-frame, facial, and hand cues, and employs inter-frame differencing to learn multi-scale temporal variations that capture both fine-grained local kinematics and global semantic transitions. Second, to mitigate over- and under-segmentation issues, we design a Segment Count Prediction module, which predicts the number of sentences to guide boundary selection. Extensive experiments on benchmark datasets demonstrate that SignShift substantially outperforms existing methods, validating its effectiveness.
1 Introduction
The introduction frames visual-only sentence-level segmentation as a needed step for processing continuous sign-language videos, where sentence boundaries are semantically important but visually ambiguous. SignShift addresses this setting with temporal-difference cues, local hand and facial features, and a predicted sentence count.
1 Introduction
- Long-video sign-language recognition and translation deteriorate without sentence boundaries, accumulating alignment errors and hindering large-scale deployment.The paper notes that natural signing typically consists of continuous streams rather than isolated sentences.Source: §1 · p. 1
- Vis-SSLS partitions continuous signing videos into non-overlapping sentence-level clips using visual cues alone, without aligned captions.The task is presented as a realistic setting that supports downstream recognition and translation.Source: §1 · p. 1, Figure 1 · p. 2
- Unlike action segmentation, Vis-SSLS locates latent semantic transitions between visually continuous sentences, each of which may contain multiple actions.Smooth transitions, limited use of facial and hand cues, and the lack of a global sentence-count constraint complicate boundary detection.Source: Figure 1 · p. 2
- SignShift models multi-scale temporal differences across global, hand, and facial features and predicts sentence count to mitigate over- and under-segmentation.The paper reports that it achieves significantly more accurate segmentation than state-of-the-art baselines.Source: §1 · p. 2
2 Related Work
Prior sign-language alignment work largely targets glosses or uses textual supervision for sentence boundaries, while temporal action segmentation models boundaries between action categories. SignShift is positioned as visual-only sentence-level segmentation that models semantic transitions and uses a global sentence-count constraint.
2.1 Alignment and Segmentation in Sign Language
- Prior sign-language alignment methods target gloss localization, while sentence-level approaches use pose dynamics or caption-based cues; existing sentence-level methods rely on external textual supervision.The review contrasts these approaches with a visual-only sentence-level paradigm.Source: §2.1 · p. 2
2.2 Temporal Action Segmentation
- Temporal Action Segmentation assigns action labels to frames and locates action boundaries, whereas sign-language sentence boundaries mark semantic transitions that may span multiple actions.The paper argues that action-centric methods cannot be directly applied to Vis-SSLS and motivates multi-scale temporal differences plus a global sentence-count prior.Source: §2.2 · pp. 2–3
3 Method
SignShift frames Vis-SSLS as frame-wise sentence-boundary prediction and combines multi-scale visual differences, global sentence-count prediction, and boundary selection. Its components produce contiguous sentence-level segments and train boundary predictions with classification and smoothness losses.
3.1 Problem Setting
- Vis-SSLS predicts a binary boundary label for each frame, where a positive label marks the ending frame of a sentence and the final boundary is the last video frame.The ordered boundary indices partition the video into non-overlapping segments, each corresponding to one complete sign language sentence.Source: §3.1 · p. 3
3.2 Overview of SignShift
- SignShift processes global video, facial, and hand features with a Temporal Difference Module, then combines frame-wise boundary prediction with a sentence-count prior to produce segment boundaries.The method fixes the final boundary at the last video frame and selects the remaining boundaries using frame-wise confidence scores and a temporal spacing constraint.Source: §3.2 · p. 3, §3.5 · p. 5
3.3 Temporal Difference Module (TDM)
- The Temporal Difference Module computes frame-to-frame differences at multiple temporal offsets across global, hand, and facial feature streams, then fuses their difference-enhanced representations.Small offsets capture short-range fluctuations, while larger offsets capture coarse structural variations; the fused representation also retains original semantic context through a residual projection.Source: §3.3 · p. 4
3.4 Segment Count Prediction (SCP)
- The Segment Count Prediction module estimates a distribution over sentence counts from the fused feature sequence, providing a global prior to regulate boundary decisions.It uses dilated temporal convolutions and attention pooling; a Gaussian-smoothed target softly penalizes small deviations around the true count.Source: §3.4 · pp. 4–5
3.5 Boundary Learning and Selection
- Boundary selection chooses M*−1 internal boundaries from frame-wise confidence scores, subject to a spacing constraint, and fixes the last video frame as the final boundary.The selected boundaries define contiguous sentence-level segments that reach the end of the video.Source: §3.5 · p. 5
3.6 Segmentation Loss
- Frame-wise boundary prediction is supervised with binary cross-entropy and a mean-squared-error smoothness loss.The smoothness-loss balance is controlled by λ, set to 0.15 in all experiments.Source: §3.6 · pp. 5–6
4 Experiments
Experiments evaluate SignShift on How2Sign and OpenASL, with benchmark comparisons, component ablations, efficiency tests, and qualitative analysis. Results report gains over DiffAct and examine how SignShift behaves across components and video durations.
4.1 Datasets
- SignShift is evaluated on How2Sign and OpenASL using continuous clips of 0–10 minutes and sentence-level annotations.The resulting splits contain 3,720/461/464 samples for How2Sign and 11,023/1,378/1,379 for OpenASL (train/development/test).Source: §4.1 · p. 6
4.2 Experimental Setup
- All models use the same feature inputs, data splits, and evaluation protocol, with segmental F1@{10, 25, 50} and SER as metrics.SignShift is trained for 60 epochs; the predicted segment count guides boundary selection at inference.Source: §4.2 · p. 6
4.4 Ablation Study
- TDM and hand/face streams improve boundary localization, while the better complete backbone differs by dataset.TDM improves F1@50 for both backbones on both datasets; hand and face cues further raise F1@10 and reduce SER. ASFormer is stronger on How2Sign F1@10, whereas MS-TCN has higher OpenASL F1@50.Source: §4.4 · pp. 6–7, Table 2 · p. 7
4.3 Overall Performance
- On How2Sign test, SignShift raises F1@50 from 55.69 to 62.47 (+6.78) and lowers SER from 0.34 to 0.22 (-0.12) versus DiffAct.On OpenASL test, it raises F1@50 from 65.93 to 68.25 and lowers SER from 0.29 to 0.18.Source: §4.3 · p. 6, Table 1 · p. 7
4.4 Ablation Study
- SCP improves segmentation F1@50 and SER under both backbones, and lowers count MAE from 1.734 to 1.177 on How2Sign, 1.970 to 1.275 on OpenASL, and 1.337 to 0.798 on PHOENIX14T.The count estimates compare SCP predictions with counts inferred from segmentation output.Source: Table 2 · p. 7, §4.4 · p. 7, Figure 4 · p. 7
- On How2Sign, F1@10 shifts from 85.19 to 83.65 and F1@50 from 66.80 to 51.03 between short and long clips; SER falls from 0.25 to 0.13.The authors report that precise boundary localization becomes more difficult as video duration increases.Source: Table 3 · p. 7, §4.4 · pp. 7–8
4.5 Computational Efficiency
- SignShift improves F1@50 by 8.67 percentage points over ASFormer, with comparable computational complexity and faster inference.It also improves boundary localization over SignBD and DiffAct at competitive costs.Source: Table 4 · p. 8, §4.5 · p. 8
4.6 Qualitative Analysis
- On OpenASL examples, SignShift produces more consistent, semantically coherent segments than MS-TCN++ and SignBD.The examples contrast MS-TCN++ fragmented boundaries with SignBD's smoother but misaligned segments.Source: §4.6 · p. 8
5 Conclusion
SignShift segments long continuous signing videos using visual cues, combining multi-scale differences across full-frame, hand, and facial features with a video-level sentence-count prior. Experiments report good segmentation results.
5 Conclusion
- SignShift models multi-scale temporal differences in full-frame, hand, and facial features, then fuses them to enhance boundary perception.Its Segment Count Prediction module estimates the video-level sentence count as a global prior for boundary selection.Source: §5 · p. 8
- The framework targets coherent sentence-level clips in long continuous signing videos using purely visual cues.Source: §5 · p. 8
- SignShift achieves good segmentation results; Figure 5 marks correctly identified OpenASL boundaries and baseline models' incorrect or missed boundaries.Checkmarks indicate SignShift's correct boundaries, while crosses indicate baseline errors or missed boundaries.Source: §5 · p. 8, Figure 5 · p. 8