Skip to content
Log in
Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Anyang Tong

h-index 2 View on Semantic Scholar

We have 2 of 9 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

2026

Toward Trustworthy Dynamic Facial Expression Recognition via Information Bottleneck Modeling

Due to the presence of semantic ambiguity among similar expression categories and the inherent imbalance in spatio-temporal feature intensities, dynamic facial expression recognition (DFER) in the wild poses significant challenges for building trustworthy and robust systems. These factors often lead to inconsistent feature representations and unreliable decision boundaries, which hinder the model’s ability to perform stable and accurate recognition under uncertainty, and further pose a serious safety hazard, e.g., misdiagnosis of depression. To tackle these challenges, we propose a novel adaptive framework, Semantic-Aware Facial Expression Recognition framework (SAFE), which is developed from an Information Bottleneck (IB)-inspired perspective to improve the robustness and prediction reliability of DFER in complex, unconstrained scenarios. Specifically, we first design a Temporal-aware Augmentation Module (TAM) to introduce structurally perturbed yet temporally coherent training samples, effectively mitigating spatio-temporal feature imbalance. Then, to ensure stable long-range modeling under temporal variation, we introduce the Spatio-temporal Modeling Module (STM) with a sparsity-aware state-space fusion gate. Furthermore, an Ambiguity-aware Calibration Loss (ACL) is formulated to dynamically refine decision boundaries by focusing on confusing and underrepresented categories, improving the model’s resilience to distributional skew and semantic uncertainty. Extensive experiments on two large-scale in-the-wild DFER benchmarks, DFEW and FERV39k, demonstrate that SAFE consistently outperforms state-of-the-art methods across multiple metrics, particularly under ambiguous and imbalanced conditions. These results validate the effectiveness of our approach in promoting more robust and stable expression recognition, which is important for trustworthy DFER in real-world environments. Codes are released at https://github.com/QIcita/SAFE_DFER

Feng-Qi Cui, Anyang Tong, Jinyang Huang et al. · 0 citations
Jul 2026

Boosting Semi-Supervised Learning With Entropy-Guided Adaptive Reward Maximization

Existing semi-supervised learning (SSL) methods rely predominantly on pseudo-labeling and consistency regularization to leverage unlabeled data, demonstrating significant performance improvements. However, we pinpoint that these methods suffer from a confidence-for-weighting issue, overvaluing high-confidence pseudo-labels while undervaluing low-confidence yet informative samples that are critical for robust generalization. In this paper, we introduce EntropyMatch, an entropy-driven SSL framework that redefines sample importance through prediction entropy rather than confidence alone. EntropyMatch employs a bidirectional weighting strategy: upward exploitation exploits reliable hard samples to refine decision boundaries while downward exploration cautiously explores uncertain ones to reduce noise. Additionally, EntropyMatch features an adaptive training mechanism that aligns with model maturity, shifting focus from safe exploration to strategic exploitation as training progresses. Experiments on eight benchmarks across various SSL tasks—spanning image classification, facial expression recognition, and human action recognition—validate EntropyMatch’s robustness and effectiveness. It consistently achieves state-of-the-art results, notably matching state-of-the-art LION’s performance on RAF-DB with just half the labeled data, demonstrating superior data efficiency and generalization.

Anyang Tong, Zenglin Shi, Zhun Zhong et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.