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1. Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency

   Zhou, Xingyi; Karpur, Arjun; Gan, Chuang; Luo, Linjie; Huang, Qixing (September 2018, European Conference on Computer Vision)

   In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency can provide effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques. 

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2. KGNv2: Separating Scale and Pose Prediction for Keypoint-Based 6-DoF Grasp Synthesis on RGB-D Input

   https://doi.org/10.1109/IROS55552.2023.10342514  

   Chen, Yiye; Xu, Ruinian; Lin, Yunzhi; Chen, Hongyi; Vela, Patricio A (October 2023, IEEE)

   We propose an improved keypoint approach for 6-DoF grasp pose synthesis from RGB-D input. Keypoint-based grasp detection from image input demonstrated promising results in a previous study, where the visual information provided by color imagery compensates for noisy or imprecise depth measurements. However, it relies heavily on accurate keypoint prediction in image space. We devise a new grasp generation network that reduces the dependency on precise keypoint estimation. Given an RGB-D input, the network estimates both the grasp pose and the camera-grasp length scale. Re-design of the keypoint output space mitigates the impact of keypoint prediction noise on Perspective-n-Point (PnP) algorithm solutions. Experiments show that the proposed method outperforms the baseline by a large margin, validating its design. Though trained only on simple synthetic objects, our method demonstrates sim-to-real capacity through competitive results in real-world robot experiments. 

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3. 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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4. CyCADA: Cycle-Consistent Adversarial Domain Adaptation

   Hoffman, Judy; Tzeng, Eric; Park, Taesung; Zhu, Jun-Yan; Isola, Phillip; Saenko, Kate; Efros, Alexei; Darrell, Trevor (January 2018, Proceedings of the 35th International Conference on Machine Learning)

   Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models have shown tremendous progress towards adapting to new environments by focusing either on discovering domain invariant representations or by mapping between unpaired image domains. While feature space methods are difficult to interpret and sometimes fail to capture pixel-level and low-level domain shifts, image space methods sometimes fail to incorporate high level semantic knowledge relevant for the end task. We propose a model which adapts between domains using both generative image space alignment and latent representation space alignment. Our approach, Cycle-Consistent Adversarial Domain Adaptation (CyCADA), guides transfer between domains according to a specific discriminatively trained task and avoids divergence by enforcing consistency of the relevant semantics before and after adaptation. We evaluate our method on a variety of visual recognition and prediction settings, including digit classification and semantic segmentation of road scenes, advancing state-of-the-art performance for unsupervised adaptation from synthetic to real world driving domains. 

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5. Open-Set Graph Domain Adaptation via Separate Domain Alignment

   https://doi.org/10.1609/aaai.v38i8.28765  

   Wang, Yu; Zhu, Ronghang; Ji, Pengsheng; Li, Sheng (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Domain adaptation has become an attractive learning paradigm, as it can leverage source domains with rich labels to deal with classification tasks in an unlabeled target domain. A few recent studies develop domain adaptation approaches for graph-structured data. In the case of node classification task, current domain adaptation methods only focus on the closed-set setting, where source and target domains share the same label space. A more practical assumption is that the target domain may contain new classes that are not included in the source domain. Therefore, in this paper, we introduce a novel and challenging problem for graphs, i.e., open-set domain adaptive node classification, and propose a new approach to solve it. Specifically, we develop an algorithm for efficient knowledge transfer from a labeled source graph to an unlabeled target graph under a separate domain alignment (SDA) strategy, in order to learn discriminative feature representations for the target graph. Our goal is to not only correctly classify target nodes into the known classes, but also classify unseen types of nodes into an unknown class. Experimental results on real-world datasets show that our method outperforms existing methods on graph domain adaptation. 

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