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Shasha Mao

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JBHI Journal 2025 Journal Article

Multi-Task Hybrid Conv-Transformer With Emotional Localized Ambiguity Exploration for Facial Pain Assessment

Shasha Mao
Angze Li
Yanjia Luo
Shuiping Gou
Mengnan Qi
Tianhuan Li
Xinyi Wei
Binxiao Su

Recently, there has been significant progress in automatic pain assessment based on facial expression analysis. However, the performance of pain assessment remains unsatisfactory, due to a lack of analysis on local pain-related action units and emotional ambiguity. In particular, ambiguous pain expressions complicate the estimation of pain. It is argued that certain facial local regions related to pain should receive more attention while estimating pain intensities. Based on this, we propose a multi-task hybrid Conv-Transformer method for facial pain assessment, which utilizes the self-attention mechanism to explore facial local features related to pain intensities and constructs a multi-task joint optimizing module to mitigate facial emotional ambiguity. In particular, the proposed method modifies the network structure of the vision transformer model to better estimate continuous pain intensities. Meanwhile, a multi-task module is constructed to jointly optimize the classification and the regression tasks of pain assessment, which effectively regularizes the extracted features and facilitates a better fit of the regressed prediction to the given label. Finally, experimental results on the UNBC Pain dataset illustrate that the proposed method performs better with pain assessment compared with state-of-the-art methods.

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ICLR Conference 2025 Conference Paper

Rethinking Multiple-Instance Learning From Feature Space to Probability Space

Zhaolong Du
Shasha Mao
Xuequan Lu
Mengnan Qi
Yimeng Zhang
Jing Gu
Licheng Jiao

Multiple-instance learning (MIL) was initially proposed to identify key instances within a set (bag) of instances when only one bag-level label is provided. Current deep MIL models mostly solve multi-instance problem in feature space. Nevertheless, with the increasing complexity of data, we found this paradigm faces significant risks in representation learning stage, which could lead to algorithm degradation in deep MIL models. We speculate that the degradation issue stems from the persistent drift of instances in feature space during learning. In this paper, we propose a novel Probability-Space MIL network (PSMIL) as a countermeasure. In PSMIL, a self-training alignment strategy is introduced in probability space to cope with the drift problem in feature space, and the alignment target objective is proven mathematically optimal. Furthermore, we reveal that the widely-used attention-based pooling mechanism in current deep MIL models is easily affected by the perturbation in feature space and further introduce an alternative called probability-space attention pooling. It effectively captures the key instance in each bag from feature space to probability space, and further eliminates the impact of selection drift in the pooling stage. To summarize, PSMIL seeks to solve a MIL problem in probability space rather than feature space. Experimental results illustrate that PSMIL could potentially achieve performance close to supervised learning level in complex tasks (gap within 5\%), with the incremental alignment in propability space bring more than 19\% accuracy improvements for current existing mainstream models in simulated CIFAR datasets. For existing publicly available MIL benchmarks/datasets, attention in probability space also achieves competitive performance to the state-of-the-art deep MIL models. Codes are available at \url{https://github.com/LMBDA-design/PSAMIL}.

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NeurIPS Conference 2023 Conference Paper

RGMIL: Guide Your Multiple-Instance Learning Model with Regressor

Zhaolong Du
Shasha Mao
Yimeng Zhang
Shuiping Gou
Licheng Jiao
Lin Xiong

In video analysis, an important challenge is insufficient annotated data due to the rare occurrence of the critical patterns, and we need to provide discriminative frame-level representation with limited annotation in some applications. Multiple Instance Learning (MIL) is suitable for this scenario. However, many MIL models paid attention to analyzing the relationships between instance representations and aggregating them, but neglecting the critical information from the MIL problem itself, which causes difficultly achieving ideal instance-level performance compared with the supervised model. To address this issue, we propose the $\textbf{\textit{Regressor-Guided MIL network} (RGMIL)}$, which effectively produces discriminative instance-level representations in a general multi-classification scenario. In the proposed method, we make full use of the $\textit{regressor}$ through our newly introduced $\textit{aggregator}$, $\textbf{\textit{Regressor-Guided Pooling} (RGP)}$. RGP focuses on simulating the correct inference process of humans while facing similar problems without introducing new parameters, and the MIL problem can be accurately described through the critical information from the $\textit{regressor}$ in our method. In experiments, RGP shows dominance on more than 20 MIL benchmark datasets, with the average bag-level classification accuracy close to 1. We also perform a series of comprehensive experiments on the MMNIST dataset. Experimental results illustrate that our $\textit{aggregator}$ outperforms existing methods under different challenging circumstances. Instance-level predictions are even possible under the guidance of RGP information table in a long sequence. RGMIL also presents comparable instance-level performance with S-O-T-A supervised models in complicated applications. Statistical results demonstrate the assumption that a MIL model can compete with a supervised model at the instance level, as long as a structure that accurately describes the MIL problem is provided. The codes are available on $\url{https: //github. com/LMBDA-design/RGMIL}$.

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