Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment

• URL: http://arxiv.org/abs/2503.22359v1
• Date: Fri, 28 Mar 2025 11:59:27 GMT
• Title: Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment
• Authors: Jiahao Xia, Min Xu, Wenjian Huang, Jianguo Zhang, Haimin Zhang, Chunxia Xiao,
• Abstract summary: Despite the similar structures of human faces, existing face alignment methods cannot learn unified knowledge from multiple datasets.<n>This paper presents a strategy to unify knowledge from multiple datasets.<n>The successful mitigation of discrepancies also enhances the efficiency of knowledge transferring to a novel dataset.
• Score: 30.501432077729245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the similar structures of human faces, existing face alignment methods cannot learn unified knowledge from multiple datasets with different landmark annotations. The limited training samples in a single dataset commonly result in fragile robustness in this field. To mitigate knowledge discrepancies among different datasets and train a task-agnostic unified face alignment (TUFA) framework, this paper presents a strategy to unify knowledge from multiple datasets. Specifically, we calculate a mean face shape for each dataset. To explicitly align these mean shapes on an interpretable plane based on their semantics, each shape is then incorporated with a group of semantic alignment embeddings. The 2D coordinates of these aligned shapes can be viewed as the anchors of the plane. By encoding them into structure prompts and further regressing the corresponding facial landmarks using image features, a mapping from the plane to the target faces is finally established, which unifies the learning target of different datasets. Consequently, multiple datasets can be utilized to boost the generalization ability of the model. The successful mitigation of discrepancies also enhances the efficiency of knowledge transferring to a novel dataset, significantly boosts the performance of few-shot face alignment. Additionally, the interpretable plane endows TUFA with a task-agnostic characteristic, enabling it to locate landmarks unseen during training in a zero-shot manner. Extensive experiments are carried on seven benchmarks and the results demonstrate an impressive improvement in face alignment brought by knowledge discrepancies mitigation.

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