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Yingjie Chen

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5 papers

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EAAI Journal 2025 Journal Article

Improved U-net with weighted loss function and its application in the prediction of remaining oil distribution

Kang Zhou
Ke Su
Zhibin An
Yingjie Chen
Jian Hou

Research on predicting the remaining oil distribution using artificial neural networks has been thoroughly conducted. However, conventional methods often perform poorly in regions exhibiting drastic changes in oil saturation. To address this limitation, the paper adopts U-net as the prediction model and improves the loss function by dynamically adjusting the loss weights of each pixel based on the extent of changes within the remaining oil distribution map. Furthermore, an exponential relationship between the weight of the loss function and the variance of oil saturation is established, and thus the loss function is modified to be saturation-variance-dependent. The findings demonstrate that modifying the weighted loss function enhances U-net's learning capability in regions with drastic changes in oil saturation, thereby effectively improving prediction performance in these challenging areas. Moreover, the optimal prediction performance of U-net is achieved when the base value of the exponential weighted function is set to 2. The proportion of predictions with R2 greater than 0. 95 is 72 % using U-net with the weighted loss function, marking a 48 % increase compared to the U-net without modifications. The study provides a promising method for prediction of remaining oil distribution, and can also serve as a valuable reference for application of the U-net in image prediction with drastic local changes.

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AAAI Conference 2024 Conference Paper

Trend-Aware Supervision: On Learning Invariance for Semi-supervised Facial Action Unit Intensity Estimation

Yingjie Chen
Jiarui Zhang
Tao Wang
Yun Liang

With the increasing need for facial behavior analysis, semi-supervised AU intensity estimation using only keyframe annotations has emerged as a practical and effective solution to relieve the burden of annotation. However, the lack of annotations makes the spurious correlation problem caused by AU co-occurrences and subject variation much more prominent, leading to non-robust intensity estimation that is entangled among AUs and biased among subjects. We observe that trend information inherent in keyframe annotations could act as extra supervision and raising the awareness of AU-specific facial appearance changing trends during training is the key to learning invariant AU-specific features. To this end, we propose Trend-AwareSupervision (TAS), which pursues three kinds of trend awareness, including intra-trend ranking awareness, intra-trend speed awareness, and inter-trend subject awareness. TAS alleviates the spurious correlation problem by raising trend awareness during training to learn AU-specific features that represent the corresponding facial appearance changes, to achieve intensity estimation invariance. Experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of each kind of awareness. And under trend-aware supervision, the performance can be improved without extra computational or storage costs during inference.

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AAAI Conference 2022 Conference Paper

Causal Intervention for Subject-Deconfounded Facial Action Unit Recognition

Yingjie Chen
Diqi Chen
Tao Wang
Yizhou Wang
Yun Liang

Subject-invariant facial action unit (AU) recognition remains challenging for the reason that the data distribution varies among subjects. In this paper, we propose a causal inference framework for subject-invariant facial action unit recognition. To illustrate the causal effect existing in AU recognition task, we formulate the causalities among facial images, subjects, latent AU semantic relations, and estimated AU occurrence probabilities via a structural causal model. By constructing such a causal diagram, we clarify the causal effect among variables and propose a plug-in causal intervention module, CIS, to deconfound the confounder Subject in the causal diagram. Extensive experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the effectiveness of our CIS, and the model with CIS inserted, CISNet, has achieved state-of-the-art performance.

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IJCAI Conference 2022 Conference Paper

Improved Deep Unsupervised Hashing with Fine-grained Semantic Similarity Mining for Multi-Label Image Retrieval

Zeyu Ma
Xiao Luo
Yingjie Chen
Mixiao Hou
Jinxing Li
Minghua Deng
Guangming Lu

In this paper, we study deep unsupervised hashing, a critical problem for approximate nearest neighbor research. Most recent methods solve this problem by semantic similarity reconstruction for guiding hashing network learning or contrastive learning of hash codes. However, in multi-label scenarios, these methods usually either generate an inaccurate similarity matrix without reflection of similarity ranking or suffer from the violation of the underlying assumption in contrastive learning, resulting in limited retrieval performance. To tackle this issue, we propose a novel method termed HAMAN, which explores semantics from a fine-grained view to enhance the ability of multi-label image retrieval. In particular, we reconstruct the pairwise similarity structure by matching fine-grained patch features generated by the pre-trained neural network, serving as reliable guidance for similarity preserving of hash codes. Moreover, a novel conditional contrastive learning on hash codes is proposed to adopt self-supervised learning in multi-label scenarios. According to extensive experiments on three multi-label datasets, the proposed method outperforms a broad range of state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

DenserNet: Weakly Supervised Visual Localization Using Multi-Scale Feature Aggregation

Dongfang Liu
Yiming Cui
Liqi Yan
Christos Mousas
Baijian Yang
Yingjie Chen

In this work, we introduce a Denser Feature Network (DenserNet) for visual localization. Our work provides three principal contributions. First, we develop a convolutional neural network (CNN) architecture which aggregates feature maps at different semantic levels for image representations. Using denser feature maps, our method can produce more keypoint features and increase image retrieval accuracy. Second, our model is trained end-to-end without pixel-level annotation other than positive and negative GPS-tagged image pairs. We use a weakly supervised triplet ranking loss to learn discriminative features and encourage keypoint feature repeatability for image representation. Finally, our method is computationally efficient as our architecture has shared features and parameters during forwarding propagation. Our method is flexible and can be crafted on a light-weighted backbone architecture to achieve appealing efficiency with a small penalty on accuracy. Extensive experiment results indicate that our method sets a new state-of-the-art on four challenging large-scale localization benchmarks and three image retrieval benchmarks with the same level of supervision. The code is available at https: //github. com/goodproj13/ DenserNet.

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