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1. Structure-Aware Image Segmentation with Homotopy Warping

   Hu, Xiaoling (December 2022, The Thirty-sixth Conference on Neural Information Processing Systems)

   Besides per-pixel accuracy, topological correctness is also crucial for the segmentation of images with fine-scale structures, e.g., satellite images and biomedical images. In this paper, by leveraging the theory of digital topology, we identify pixels in an image that are critical for topology. By focusing on these critical pixels, we propose a new homotopy warping loss to train deep image segmentation networks for better topological accuracy. To efficiently identify these topologically critical pixels, we propose a new algorithm exploiting the distance transform. The proposed algorithm, as well as the loss function, naturally generalize to different topological structures in both 2D and 3D settings. The proposed loss function helps deep nets achieve better performance in terms of topology-aware metrics, outperforming state-of-the-art structure/topology-aware segmentation methods. 

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2. Information-Theoretic Segmentation by Inpainting Error Maximization

   Savarese, P.; Kim, S. S.; Maire, M.; Shakhnarovich, G.; & McAllester, D. (July 2021, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)

   null (Ed.)

   We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specifically, we group image pixels into foreground and background, with the goal of minimizing predictability of one set from the other. An easily computed loss drives a greedy search process to maximize inpainting error over these partitions. Our method does not involve training deep networks, is computationally cheap, class-agnostic, and even applicable in isolation to a single unlabeled image. Experiments demonstrate that it achieves a new state-of-the-art in unsupervised segmentation quality, while being substantially faster and more general than competing approaches. 

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3. The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention

   Nowara, Ewa M. (October 2021, Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021)

   null (Ed.)

   Attention networks perform well on diverse computer vision tasks. The core idea is that the signal of interest is stronger in some pixels ("foreground"), and by selectively focusing computation on these pixels, networks can extract subtle information buried in noise and other sources of corruption. Our paper is based on one key observation: in many real-world applications, many sources of corruption, such as illumination and motion, are often shared between the "foreground" and the "background" pixels. Can we utilize this to our advantage? We propose the utility of inverse attention networks, which focus on extracting information about these shared sources of corruption. We show that this helps to effectively suppress shared covariates and amplify signal information, resulting in improved performance. We illustrate this on the task of camera-based physiological measurement where the signal of interest is weak and global illumination variations and motion act as significant shared sources of corruption. We perform experiments on three datasets and show that our approach of inverse attention produces state-of-the-art results, increasing the signal-to-noise ratio by up to 5.8 dB, reducing heart rate and breathing rate estimation errors by as much as 30 %, recovering subtle waveform dynamics, and generalizing from RGB to NIR videos without retraining. 

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4. Foreground-aware Image Inpainting

   Xiong, Wei; Lin, Zhe; Luo, Jiebo (January 2019, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition)

   Existing image inpainting methods typically fill holes by borrowing information from surrounding pixels. They often produce unsatisfactory results when the holes overlap with or touch foreground objects due to lack of information about the actual extent of foreground and background regions within the holes. These scenarios, however, are very important in practice, especially for applications such as the removal of distracting objects. To address the problem, we propose a foreground-aware image inpainting system that explicitly disentangles structure inference and content completion. Specifically, our model learns to predict the foreground contour first, and then inpaints the missing region using the predicted contour as guidance. We show that by such disentanglement, the contour completion model predicts reasonable contours of objects, and further substantially improves the performance of image inpainting. Experiments show that our method significantly outperforms existing methods and achieves superior inpainting results on challenging cases with complex compositions. 

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5. Understanding and Mitigating Accuracy Disparity in Regression

   Jianfeng Chi, Yuan Tian (January 2021, Proceedings of the 38th International Conference on Machine Learning)

   With the widespread deployment of large-scale prediction systems in high-stakes domains, e.g., face recognition, criminal justice, etc., disparity on prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it. In this paper, we study the accuracy disparity problem in regression. To begin with, we first propose an error decomposition theorem, which decomposes the accuracy disparity into the distance between marginal label distributions and the distance between conditional representations, to help explain why such accuracy disparity appears in practice. Motivated by this error decomposition and the general idea of distribution alignment with statistical distances, we then propose an algorithm to reduce this disparity, and analyze its game-theoretic optima of the proposed objective functions. To corroborate our theoretical findings, we also conduct experiments on five benchmark datasets. The experimental results suggest that our proposed algorithms can effectively mitigate accuracy disparity while maintaining the predictive power of the regression models. 

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