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1. Multiview Supervision By Registration

   Zhang, Y.; Park, H.S. (January 2020, IEEE Winter Conference on Applications of Computer Vision)

   This paper presents a semi-supervised learning framework to train a keypoint detector using multiview image streams given the limited number of labeled instances (typically <4%). We leverage three self-supervisionary signals in multiview tracking to utilize the unlabeled data: (1) a keypoint in one view can be supervised by other views via epipolar geometry; (2) a keypoint detection must be consistent across time; (3) a visible keypoint in one view is likely to be visible in the adjacent view. We design a new end-toend network that can propagate these self-supervisionary signals across the unlabeled data from the labeled data in a differentiable manner. We show that our approach outperforms existing detectors including DeepLabCut tailored to the keypoint detection of non-human species such as monkeys, dogs, and mice. 

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2. Multiview Cross-supervision for Semantic Segmentation

   Yao, Y.; Park, H.S. (January 2020, IEEE Winter Conference on Applications of Computer Vision)

   This paper presents a semi-supervised learning framework for a customized semantic segmentation task using multiview image streams. A key challenge of the customized task lies in the limited accessibility of the labeled data due to the requirement of prohibitive manual annotation effort. We hypothesize that it is possible to leverage multiview image streams that are linked through the underlying 3D geometry, which can provide an additional supervisionary signal to train a segmentation model. We formulate a new cross-supervision method using a shape belief transfer---the segmentation belief in one image is used to predict that of the other image through epipolar geometry analogous to shape-from-silhouette. The shape belief transfer provides the upper and lower bounds of the segmentation for the unlabeled data where its gap approaches asymptotically to zero as the number of the labeled views increases. We integrate this theory to design a novel network that is agnostic to camera calibration, network model, and semantic category and bypasses the intermediate process of suboptimal 3D reconstruction. We validate this network by recognizing a customized semantic category per pixel from realworld visual data including non-human species and a subject of interest in social videos where attaining large-scale annotation data is infeasible. 

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3. On the Instability of Relative Pose Estimation and RANSAC's Role

   Hongyi Fan, Joe Kileel (June 2022, IEEE Computer Society Conference on Computer Vision and Pattern Recognition)

   Relative pose estimation using the 5-point or 7-point Random Sample Consensus (RANSAC) algorithms can fail even when no outliers are present and there are enough inliers to support a hypothesis. These cases arise due to numerical instability of the 5- and 7-point minimal problems. This paper characterizes these instabilities, both in terms of minimal world scene configurations that lead to infinite condition number in epipolar estimation, and also in terms of the related minimal image feature pair correspondence configurations. The instability is studied in the context of a novel framework for analyzing the conditioning of minimal problems in multiview geometry, based on Riemannian manifolds. Experiments with synthetic and real-world data reveal that RANSAC does not only serve to filter out outliers, but RANSAC also selects for well-conditioned image data, sufficiently separated from the ill-posed locus that our theory predicts. These findings suggest that, in future work, one could try to accelerate and increase the success of RANSAC by testing only well-conditioned image data. 

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4. Learning Local-Global Contextual Adaptation for Multi-Person Pose Estimation

   Xue, Nan and (June 2022, IEEE/CVF CVPR)

   This paper studies the problem of multi-person pose estimation in a bottom-up fashion. With a new and strong observation that the localization issue of the center-offset formulation can be remedied in a local-window search scheme in an ideal situation, we propose a multi-person pose estimation approach, dubbed as LOGO-CAP, by learning the LOcal-GlObal Contextual Adaptation for human Pose. Specifically, our approach learns the keypoint attraction maps (KAMs) from the local keypoints expansion maps (KEMs) in small local windows in the first step, which are subsequently treated as dynamic convolutional kernels on the keypoints-focused global heatmaps for contextual adaptation, achieving accurate multi-person pose estimation. Our method is end-to-end trainable with near real-time inference speed in a single forward pass, obtaining state-of-the-art performance on the COCO keypoint benchmark for bottom-up human pose estimation. With the COCO trained model, our method also outperforms prior arts by a large margin on the challenging OCHuman dataset. 

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5. Keypoint abstraction using large models for object-relative imitation learning.

   Fang, Xiaolin; Huang, Bo-Ruei; Mao, Jiayuan; Shone, Jasmine; Tenenbaum, Joshua; Lozano-Perez, Tomas; Kaelbling, Leslie (June 2025, Proceedings IEEE International Conference on Robotics and Automation)

   Generalization to novel object configurations and instances across diverse tasks and environments is a critical challenge in robotics. Keypoint-based representations have been proven effective as a succinct representation for capturing essential object features, and for establishing a reference frame in action prediction, enabling data-efficient learning of robot skills. However, their manual design nature and reliance on additional human labels limit their scalability. In this paper, we propose KALM, a framework that leverages large pre-trained vision-language models (LMs) to automatically generate taskrelevant and cross-instance consistent keypoints. KALM distills robust and consistent keypoints across views and objects by generating proposals using LMs and verifies them against a small set of robot demonstration data. Based on the generated keypoints, we can train keypoint-conditioned policy models that predict actions in keypoint-centric frames, enabling robots to generalize effectively across varying object poses, camera views, and object instances with similar functional shapes. Our method demonstrates strong performance in the real world, adapting to different tasks and environments from only a handful of demonstrations while requiring no additional labels. 

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