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[Doc] Fix download links and add paper abstracts and images (#1124)
* update download links * add abstract and image for softwingloss and awingoloss * fix typo
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| # Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression | ||
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| <!-- [ALGORITHM] --> | ||
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| <details> | ||
| <summary align="right"><a href="https://arxiv.org/pdf/1904.07399.pdf">AdaptiveWingloss (ICCV'2019)</a></summary> | ||
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| ```bibtex | ||
| @inproceedings{wang2019adaptive, | ||
| title={Adaptive wing loss for robust face alignment via heatmap regression}, | ||
| author={Wang, Xinyao and Bo, Liefeng and Fuxin, Li}, | ||
| booktitle={Proceedings of the IEEE/CVF international conference on computer vision}, | ||
| pages={6971--6981}, | ||
| year={2019} | ||
| } | ||
| ``` | ||
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| </details> | ||
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| ## Abstract | ||
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| <!-- [ABSTRACT] --> | ||
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| Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability penalizes loss more on foreground pixels while less on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on | ||
| various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks. | ||
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| <!-- [IMAGE] --> | ||
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| <div align=center> | ||
| <img src="https://user-images.githubusercontent.com/15977946/148007960-a06a34d8-8090-49e1-80db-6bbe4a7e7e8d.png"> | ||
| </div> |
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| # Structure-Coherent Deep Feature Learning for Robust Face Alignment | ||
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| <!-- [ALGORITHM] --> | ||
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| <details> | ||
| <summary align="right"><a href="https://ieeexplore.ieee.org/document/9442331/">SoftWingloss (TIP'2021)</a></summary> | ||
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| ```bibtex | ||
| @article{lin2021structure, | ||
| title={Structure-Coherent Deep Feature Learning for Robust Face Alignment}, | ||
| author={Lin, Chunze and Zhu, Beier and Wang, Quan and Liao, Renjie and Qian, Chen and Lu, Jiwen and Zhou, Jie}, | ||
| journal={IEEE Transactions on Image Processing}, | ||
| year={2021}, | ||
| publisher={IEEE} | ||
| } | ||
| ``` | ||
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| </details> | ||
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| ## Abstract | ||
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| <!-- [ABSTRACT] --> | ||
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| In this paper, we propose a structure-coherent deep feature learning method for face alignment. Unlike most existing face alignment methods which overlook the facial structure cues, we explicitly exploit the relation among facial landmarks to make the detector robust to hard cases such as occlusion and large pose. Specifically, we leverage a landmark-graph relational network to enforce the structural relationships among landmarks. We consider the facial landmarks as structural graph nodes and carefully design the neighborhood to passing features among the most related nodes. Our method dynamically adapts the weights of node neighborhood to eliminate distracted information from noisy nodes, such as occluded landmark point. Moreover, different from most previous works which only tend to penalize the landmarks absolute position during the training, we propose a relative location loss to enhance the information of relative location of landmarks. This relative location supervision further regularizes the facial structure. Our approach considers the interactions among facial landmarks and can be easily implemented on top of any convolutional backbone to boost the performance. Extensive experiments on three popular benchmarks, including WFLW, COFW and 300W, demonstrate the effectiveness of the proposed method. In particular, due to explicit structure modeling, our approach is especially robust to challenging cases resulting in impressive low failure rate on COFW and WFLW datasets. | ||
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| <!-- [IMAGE] --> | ||
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| <div align=center> | ||
| <img src="https://user-images.githubusercontent.com/15977946/148014510-93149a98-f462-49e7-bc92-7dd50fd90a45.png"> | ||
| </div> |
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