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Understanding Hyperbolic Metric Learning through Hard Negative Sampling
Apr 23, 2024
In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision. While these methods have achieved state-of-the-art performance on various metric learning tasks using hyperbolic distance measurements, the underlying theoretical analysis supporting this superior performance remains under-exploited. In this study, we investigate the effects of integrating hyperbolic space into metric learning, particularly when training with contrastive loss. We identify a need for a comprehensive comparison between Euclidean and hyperbolic spaces regarding the temperature effect in the contrastive loss within the existing literature. To address this gap, we conduct an extensive investigation to benchmark the results of Vision Transformers (ViTs) using a hybrid objective function that combines loss from Euclidean and hyperbolic spaces. Additionally, we provide a theoretical analysis of the observed performance improvement. We also reveal that hyperbolic metric learning is highly related to hard negative sampling, providing insights for future work. This work will provide valuable data points and experience in understanding hyperbolic image embeddings. To shed more light on problem-solving and encourage further investigation into our approach, our code is available online (https://github.com/YunYunY/HypMix).
Hyperbolic Contrastive Learning
Feb 02, 2023
Learning good image representations that are beneficial to downstream tasks is a challenging task in computer vision. As such, a wide variety of self-supervised learning approaches have been proposed. Among them, contrastive learning has shown competitive performance on several benchmark datasets. The embeddings of contrastive learning are arranged on a hypersphere that results in using the inner (dot) product as a distance measurement in Euclidean space. However, the underlying structure of many scientific fields like social networks, brain imaging, and computer graphics data exhibit highly non-Euclidean latent geometry. We propose a novel contrastive learning framework to learn semantic relationships in the hyperbolic space. Hyperbolic space is a continuous version of trees that naturally owns the ability to model hierarchical structures and is thus beneficial for efficient contrastive representation learning. We also extend the proposed Hyperbolic Contrastive Learning (HCL) to the supervised domain and studied the adversarial robustness of HCL. The comprehensive experiments show that our proposed method achieves better results on self-supervised pretraining, supervised classification, and higher robust accuracy than baseline methods.
FPCC-Net: Fast Point Cloud Clustering for Instance Segmentation
Jan 19, 2021
Instance segmentation is an important pre-processing task in numerous real-world applications, such as robotics, autonomous vehicles, and human-computer interaction. However, there has been little research on 3D point cloud instance segmentation of bin-picking scenes in which multiple objects of the same class are stacked together. Compared with the rapid development of deep learning for two-dimensional (2D) image tasks, deep learning-based 3D point cloud segmentation still has a lot of room for development. In such a situation, distinguishing a large number of occluded objects of the same class is a highly challenging problem. In a usual bin-picking scene, an object model is known and the number of object type is one. Thus, the semantic information can be ignored; instead, the focus is put on the segmentation of instances. Based on this task requirement, we propose a network (FPCC-Net) that infers feature centers of each instance and then clusters the remaining points to the closest feature center in feature embedding space. FPCC-Net includes two subnets, one for inferring the feature centers for clustering and the other for describing features of each point. The proposed method is compared with existing 3D point cloud and 2D segmentation methods in some bin-picking scenes. It is shown that FPCC-Net improves average precision (AP) by about 40\% than SGPN and can process about 60,000 points in about 0.8 [s].