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Visual Alignment Constraint for Continuous Sign Language Recognition

Yuecong Min, Aiming Hao, Xiujuan Chai, Xilin Chen

arXiv:2104.02330v2cs.CVcs.HC

TL;DR

CSLR training can overfit the alignment module and insufficiently train the feature extractor, while iterative training improves this issue at added time cost. The paper introduces VAC, using two auxiliary losses and two inconsistency metrics; experiments show competitive performance and narrower auxiliary-primary prediction gaps.

  • Problem

    End-to-end CSLR training can undertrain the feature extractor because the alignment module overfits, while iterative training requires additional fine-tuning time.

  • Method

    VAC adds visual enhancement and visual alignment losses, and introduces WDR and WAR to measure prediction inconsistency between visual and alignment modules.

  • Results

    VAC makes CSLR networks end-to-end trainable, achieves competitive performance on PHOENIX14 and CSL, and narrows auxiliary-primary prediction gaps.

  • Takeaways & Limitations

    The proposed metrics and experiments offer a perspective on the relationship between visual and alignment modules in CSLR and other sequence classification tasks.

  • Takeaways & Limitations

    A qualitative failure case shows the primary classifier can neglect correct visual-only predictions, an error not reflected in WER.

Abstract

from arXiv · show

Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize unsegmented signs from image streams. Overfitting is one of the most critical problems in CSLR training, and previous works show that the iterative training scheme can partially solve this problem while also costing more training time. In this study, we revisit the iterative training scheme in recent CSLR works and realize that sufficient training of the feature extractor is critical to solving the overfitting problem. Therefore, we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with alignment supervision. Specifically, the proposed VAC comprises two auxiliary losses: one focuses on visual features only, and the other enforces prediction alignment between the feature extractor and the alignment module. Moreover, we propose two metrics to reflect overfitting by measuring the prediction inconsistency between the feature extractor and the alignment module. Experimental results on two challenging CSLR datasets show that the proposed VAC makes CSLR networks end-to-end trainable and achieves competitive performance.

1. Introduction

CSLR uses weakly supervised sequence recognition, but end-to-end training can undertrain the feature extractor while the alignment module overfits. VAC addresses this imbalance with auxiliary alignment supervision and metrics for prediction inconsistency.

  • CSLR recognizes signs from image streams, helping bridge communication between Deaf and hearing people.
  • Weak supervision provides sentence-level labels, so CSLR architectures pair a feature extractor with an alignment module.
  • End-to-end CTC training can limit feature-extractor discrimination, whereas iterative training improves performance but adds fine-tuning time.
  • Alignment-module overfitting can leave the feature extractor insufficiently trained because the module converges faster and provides inadequate feedback.
  • VAC uses visual enhancement and visual alignment losses to supervise the feature extractor and align visual with contextual predictions.
  • WDR and WAR measure contributions from the feature extractor and alignment module and serve as overfitting indicators.

2. Related Work

Related CSLR work evolved from HMM-based and hand-crafted approaches toward CTC-based end-to-end recognition, but weak supervision still challenges feature-extractor training. Auxiliary learning provides the basis for VAC’s visual alignment constraint.

  • CSLR methods are commonly divided into isolated and continuous recognition, with continuous approaches usually using sentence-level weak supervision.
  • Early CSLR systems used HMM-based divide-and-conquer recognition and hand-crafted visual features for limited-data settings.
  • CNN-based approaches offer powerful visual representations but require frame-wise annotations unavailable in weakly supervised CSLR.
  • CTC enables end-to-end CSLR training, yet studies report that it may fail to train the feature extractor properly and therefore retain iterative training.
  • Auxiliary learning assigns supporting tasks to improve the primary task, and VAC applies this strategy to constrain the feature extractor visually.
  • VAC’s framework combines a feature extractor, alignment module, and auxiliary classifier, with losses aligning visual features and predictions.

3. Revisiting the Iterative Training in CSLR

The section revisits how CTC-based CSLR models learn through a feature extractor and alignment module, focusing on gate behavior, feature magnitudes, and iterative training. It argues that limited data can let temporal modeling overfit while leaving visual features insufficiently trained, motivating visual alignment constraints.

  • Feature extraction and alignment: The feature extractor supplies visual features and initial key-frame localization, while BiLSTM layers refine localization and model long-term relationships.This division supports efficient training but leaves the feature extractor dependent on feedback from the alignment module.
  • CTC-based CSLR: CTC trains CSLR models on unsegmented sequences by summing probabilities over feasible alignments, using blank labels and repeated-label removal.CTC predictions typically concentrate on a few non-blank key frames and many high-confidence blank frames.
  • Visualization of LSTM gates: Gate values and predictions correlate on training data, reaching local maxima on similar frame subsets, but these correlations weaken as iterative training progresses.Input and output gates become larger and smoother, suggesting changing gate behavior across iterations.
  • Magnitude hypothesis: Feature l2 norms show tendencies similar to gate values and predictions, while gloss and sequence magnitude variance decreases across iterations.The analysis treats larger activation magnitudes as indicators of important frames under CTC’s spike phenomenon.
  • Overfitting analysis: Because CSLR datasets are relatively small, BiLSTM layers can overfit training data using partial visual information, leaving the feature extractor insufficiently explored.The resulting visual features may be less robust during inference and harm generalization.
  • Motivation for VAC: The proposed visual alignment constraint addresses this imbalance by requiring the feature extractor to predict independently and aligning visual and contextual spike responses.It is presented as an alternative to relying on iterative fine-tuning and pseudo-label feedback alone.

4. Visual Alignment Constraint

VAC enhances CSLR feature extraction with two auxiliary losses and introduces metrics for measuring prediction inconsistency between visual and contextual classifiers.

  • Loss Design of VAC: VAC uses Visual Enhancement and Visual Alignment losses to provide additional alignment supervision for the feature extractor.The VE loss uses local visual information, while the VA loss aligns visual predictions with long-term contextual predictions.
  • Loss Design of VAC: The VE loss adds an auxiliary classifier on visual features and applies CTC supervision without requiring pseudo labels.This loss supervises the feature extractor and auxiliary classifier using local visual information only.
  • Loss Design of VAC: The VA loss addresses classifier misalignment by distilling predictions from the entire network into the visual feature extractor.A high temperature softens the probability distributions during knowledge distillation.
  • Prediction Inconsistency Measurement: Word Error Rate counts substitutions, deletions, and insertions after aligning recognized and reference sequences.WER is defined as (#sub + #del + #ins) / #reference.
  • Prediction Inconsistency Measurement: WDR measures correct auxiliary predictions lost by the primary classifier, whereas WAR measures correct primary predictions absent from the auxiliary classifier.The metrics compare aligned sentence triplets and expose prediction differences that WER alone cannot measure.
  • Prediction Inconsistency Measurement: The adopted alignment approach causes slight performance degradation compared with general WER.This scope caveat is stated in the footnote accompanying the evaluation discussion.

5. Experiments

Experiments on PHOENIX14 and CSL examine iterative training, learning-rate balance, VAC components, temporal designs, and qualitative behavior. VAC improves feature supervision, narrows prediction inconsistencies, and achieves competitive results across the evaluated settings.

  • Datasets: PHOENIX14 and CSL provide the evaluation settings, while ablations are performed on PHOENIX14.PHOENIX14 contains 6,841 sentences and 1,295 glosses; CSL contains 25,000 videos across 100 sentences.
  • Ablations: Adding batch normalization improves Dev performance by 5.5%, 3.4%, and 3.6% across iterations, indicating insufficient feature-extractor training.Iterative training also yields noticeable gains over non-iterative training.
  • Ablations: The VAC visual-enhancement and visual-alignment losses improve Dev recognition by 2.1% and 0.9%, respectively.The combined auxiliary losses further improve performance and reduce the actual prediction gap between classifiers.
  • Qualitative results: Qualitative examples show baseline classifier disagreement on training data and an alignment-module failure on Dev data.In the Dev example, the auxiliary classifier correctly recognizes HEUTE, OST, and SCHON, whereas the primary classifier gives a worse result.
  • Prediction consistency: Baseline Dev predictions show a 4.9% classifier gap, while WAR reaches 11.3% and WDR reaches 6.5%.These metrics expose contextual predictions that ignore correct visual information despite a small overall performance gap.
  • Comparison: VAC achieves competitive PHOENIX14 results with a gloss-wise temporal layer and improves over the baseline on CSL under the same setting.The CSL comparison has limited practical value because no official split is provided.
  • Discussion: The authors frame VAC as a visual-information approach alongside methods that enhance the feature extractor or weaken the alignment module.They identify using visual features with a more powerful temporal model as a challenging open problem.

6. Conclusion

The conclusion presents VAC as an end-to-end solution to feature-extractor undertraining caused by overfitting in CTC-based CSLR. It also introduces prediction-inconsistency metrics and reports narrower auxiliary-primary classifier gaps.

  • Conclusion: VAC makes CSLR networks end-to-end trainable by providing additional alignment supervision to the feature extractor.Its two auxiliary losses constrain visual predictions and align them with contextual predictions.
  • Conclusion: WDR and WAR measure inconsistent predictions between the feature extractor and alignment module.The metrics offer a perspective on the relationship between visual and alignment modules.
  • Conclusion: Experiments show that VAC narrows the prediction gap between auxiliary and primary classifiers.The authors suggest these metrics and experiments may inform future CSLR and sequence-classification studies.

A.1. Training process of VAC

VAC accelerates learning relative to the baseline, with its two losses becoming effective at different stages of training. Visual enhancement acts early, while visual alignment takes effect after alignment convergence begins.

  • Training process: VAC achieves better performance than the baseline after the first learning-rate decay.The comparison uses PHOENIX14 WER learning curves with learning-rate decays at 40 and 60 epochs.
  • Training process: The visual-enhancement loss accelerates training immediately at the beginning.This loss supplies early supervision to the visual feature extractor.
  • Training process: The visual-alignment loss takes effect when the alignment model begins to converge after the first learning-rate decay.Its timing reflects the later availability of alignment-model supervision.

A.2. Ablation on Dataset Size

VAC continues improving as training-data size increases, whereas the baseline visual extractor saturates. The appendix also reports tuned temperature and distillation-weight settings.

  • Dataset size: VAC steadily improves as the training-data size increases, while the baseline visual extractor’s WER_a shows a saturation trend.The authors interpret the saturation as evidence that available training data is insufficient for the baseline visual extractor.
  • Hyperparameters: τ=8 is selected as the proper temperature in the ablation study.Low temperatures produce spike responses, whereas high temperatures produce noisy supervision.
  • Hyperparameters: The optimal distillation-loss weight is 25.Performance first increases and then decreases as the distillation weight grows.

A.5. Ablation on Data Augmentation

The ablation evaluates random crop, horizontal flip, and random temporal scaling during training. Data augmentation improves performance, with random crop especially effective and horizontal flip providing a 0.6% gain despite consistent signer handedness.

  • A.5. Ablation on Data Augmentation: Data augmentation significantly improves WER, especially when using random crop.The evaluated strategies are random crop, horizontal flip, and random temporal scaling.
  • A.5. Ablation on Data Augmentation: Horizontal flip improves results by 0.6% even though all PHOENIX14 signers use their right hand dominantly.The authors suggest random cropping encourages higher-level features and mitigates shortcuts such as absolute hand position.

B.2. Details on Proposed Metrics

The proposed metrics compare auxiliary and primary classifier predictions after aligning them with the reference. They quantify recognition changes between classifiers while exposing limitations introduced by the alignment procedure.

  • B.2. Details on Proposed Metrics: WDR measures words correct for the auxiliary classifier but misrecognized by the primary classifier, while WAR measures the opposite change.The metrics require first aligning the reference and both recognized sentences.
  • B.2. Details on Proposed Metrics: 22.22% WDR and 22.22% WAR indicate equal deterioration and amelioration, leaving total WER unchanged in the example.Both classifiers have WER 22.22% before the directional comparison is interpreted.
  • B.2. Details on Proposed Metrics: The three-sentence alignment strategy can slightly degrade performance relative to general WER because of alignment and operation-weight differences.The authors consider substitution errors being split into deletion and insertion errors acceptable because only a small ratio of sequences is affected.
  • B.2. Details on Proposed Metrics: VAC visualizations show dynamically changing activations, whereas the baseline mainly focuses on the central frame area.The activation maps are computed from the l2 norm of 7x7 ResNet18 feature maps.
  • B.2. Details on Proposed Metrics: VAC produces more consistent feature-norm behavior across training and development data than the baseline.The visualization compares gate values, feature l2 norms, and final predictions; baseline correlations weaken on development data, whereas Baseline+VAC remains consistent.

C.3. More Qualitative Recognition Results

Qualitative examples show that the primary and auxiliary classifiers can disagree because context helps in some cases but can also override useful visual information. VAC seeks to use both information sources more effectively.

  • C.3. More Qualitative Recognition Results: The primary classifier can outperform the auxiliary classifier by incorporating more context information.The qualitative comparison marks primary and auxiliary outputs as (P) and (A).
  • C.3. More Qualitative Recognition Results: The primary classifier can also neglect visual information or predict incorrect glosses, producing worse results in some cases.The examples illustrate that the two classifiers are not always consistent.
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