2022-11-28 更新
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs
Authors:Sebastian Gerard, Josephine Sullivan
Domain-specific variants of contrastive learning can construct positive pairs from two distinct images, as opposed to augmenting the same image twice. Unlike in traditional contrastive methods, this can result in positive pairs not matching perfectly. Similar to false negative pairs, this could impede model performance. Surprisingly, we find that downstream semantic segmentation is either robust to the noisy pairs or even benefits from them. The experiments are conducted on the remote sensing dataset xBD, and a synthetic segmentation dataset, on which we have full control over the noise parameters. As a result, practitioners should be able to use such domain-specific contrastive methods without having to filter their positive pairs beforehand.
PDF 8 pages, 8 figures
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2022-11-28 更新
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations
Authors:Jiahang Zhang, Lilang Lin, Jiaying Liu
Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to generate different movement patterns of skeletons for the same semantics. However, it is still a pending issue to apply strong augmentations, which distort the images/skeletons’ structures and cause semantic loss, due to their resulting unstable training. In this paper, we investigate the potential of adopting strong augmentations and propose a general hierarchical consistent contrastive learning framework (HiCLR) for skeleton-based action recognition. Specifically, we first design a gradual growing augmentation policy to generate multiple ordered positive pairs, which guide to achieve the consistency of the learned representation from different views. Then, an asymmetric loss is proposed to enforce the hierarchical consistency via a directional clustering operation in the feature space, pulling the representations from strongly augmented views closer to those from weakly augmented views for better generalizability. Meanwhile, we propose and evaluate three kinds of strong augmentations for 3D skeletons to demonstrate the effectiveness of our method. Extensive experiments show that HiCLR outperforms the state-of-the-art methods notably on three large-scale datasets, i.e., NTU60, NTU120, and PKUMMD.
PDF Accepted by AAAI 2023. Project page: https://jhang2020.github.io/Projects/HiCLR/HiCLR.html
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Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
Authors:Atsuyuki Miyai, Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
Rotation is frequently listed as a candidate for data augmentation in contrastive learning but seldom provides satisfactory improvements. We argue that this is because the rotated image is always treated as either positive or negative. The semantics of an image can be rotation-invariant or rotation-variant, so whether the rotated image is treated as positive or negative should be determined based on the content of the image. Therefore, we propose a novel augmentation strategy, adaptive Positive or Negative Data Augmentation (PNDA), in which an original and its rotated image are a positive pair if they are semantically close and a negative pair if they are semantically different. To achieve PNDA, we first determine whether rotation is positive or negative on an image-by-image basis in an unsupervised way. Then, we apply PNDA to contrastive learning frameworks. Our experiments showed that PNDA improves the performance of contrastive learning. The code is available at \url{ https://github.com/AtsuMiyai/rethinking_rotation}.
PDF Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023
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How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?
Authors:Thomas M. Hehn, Julian F. P. Kooij, Dariu M. Gavrila
Various state-of-the-art self-supervised visual representation learning approaches take advantage of data from multiple sensors by aligning the feature representations across views and/or modalities. In this work, we investigate how aligning representations affects the visual features obtained from cross-view and cross-modal contrastive learning on images and point clouds. On five real-world datasets and on five tasks, we train and evaluate 108 models based on four pretraining variations. We find that cross-modal representation alignment discards complementary visual information, such as color and texture, and instead emphasizes redundant depth cues. The depth cues obtained from pretraining improve downstream depth prediction performance. Also overall, cross-modal alignment leads to more robust encoders than pre-training by cross-view alignment, especially on depth prediction, instance segmentation, and object detection.
PDF
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Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Authors:Yuanyuan Liu, Wenbin Wang, Yibing Zhan, Zhe Chen, Shaoze Feng, Kejun Liu
Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily. However, analytically, current contrastive-based self-supervised learning still performs unsatisfactorily for learning facial representation. More specifically, existing contrastive learning (CL) tends to learn pose-invariant features that cannot depict the pose details of faces, compromising the learning performance. To conquer the above limitation of CL, we propose a novel Pose-disentangled Contrastive Learning (PCL) method for general self-supervised facial representation. Our PCL first devises a pose-disentangled decoder (PDD) with a delicately designed orthogonalizing regulation, which disentangles the pose-related features from the face-aware features; therefore, pose-related and other pose-unrelated facial information could be performed in individual subnetworks and do not affect each other’s training. Furthermore, we introduce a pose-related contrastive learning scheme that learns pose-related information based on data augmentation of the same image, which would deliver more effective face-aware representation for various downstream tasks. We conducted a comprehensive linear evaluation on three challenging downstream facial understanding tasks, i.e., facial expression recognition, face recognition, and AU detection. Experimental results demonstrate that our method outperforms cutting-edge contrastive and other self-supervised learning methods with a great margin.
PDF
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Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network
Authors:Sangmin Bae, Sungnyun Kim, Jongwoo Ko, Gihun Lee, Seungjong Noh, Se-Young Yun
Contrastive loss has significantly improved performance in supervised classification tasks by using a multi-viewed framework that leverages augmentation and label information. The augmentation enables contrast with another view of a single image but enlarges training time and memory usage. To exploit the strength of multi-views while avoiding the high computation cost, we introduce a multi-exit architecture that outputs multiple features of a single image in a single-viewed framework. To this end, we propose Self-Contrastive (SelfCon) learning, which self-contrasts within multiple outputs from the different levels of a single network. The multi-exit architecture efficiently replaces multi-augmented images and leverages various information from different layers of a network. We demonstrate that SelfCon learning improves the classification performance of the encoder network, and empirically analyze its advantages in terms of the single-view and the sub-network. Furthermore, we provide theoretical evidence of the performance increase based on the mutual information bound. For ImageNet classification on ResNet-50, SelfCon improves accuracy by +0.6% with 59% memory and 48% time of Supervised Contrastive learning, and a simple ensemble of multi-exit outputs boosts performance up to +1.5%. Our code is available at https://github.com/raymin0223/self-contrastive-learning.
PDF AAAI 2023
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