19 citations · 83 across the 16 of their papers we have counts for
16 papers
BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo
Yinhao Li, Jinrong Yang, Jianjian Sun +3
Bounded by the inherent ambiguity of depth perception, contemporary multi-view 3D object detection methods fall into the performance bottleneck. Intuitively, leveraging temporal mu…
Robust computation of optimal transport by -potential regularization
Shintaro Nakamura, Han Bao, Masashi Sugiyama
Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions. For instance, OT is a popular loss…
Will Large-scale Generative Models Corrupt Future Datasets?
Ryuichiro Hataya, Han Bao, Hiromi Arai
Recently proposed large-scale text-to-image generative models such as DALLE 2, Midjourney, and StableDiffusion can generate high-quality and realistic images from users' pro…
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo
Yinhao Li, Han Bao, Zheng Ge +3
Bounded by the inherent ambiguity of depth perception, contemporary camera-based 3D object detection methods fall into the performance bottleneck. Intuitively, leveraging temporal…
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao, Yoshihiro Nagano, Kento Nozawa
Contrastive representation learning encourages data representation to make semantically similar pairs closer than randomly drawn negative samples, which has been successful in vari…
Pairwise Supervision Can Provably Elicit a Decision Boundary
Han Bao, Takuya Shimada, Liyuan Xu +2
Similarity learning is a general problem to elicit useful representations by predicting the relationship between a pair of patterns. This problem is related to various important pr…
Calibrated Surrogate Losses for Adversarially Robust Classification
Han Bao, Clayton Scott, Masashi Sugiyama
Adversarially robust classification seeks a classifier that is insensitive to adversarial perturbations of test patterns. This problem is often formulated via a minimax objective,…
Learning from Noisy Similar and Dissimilar Data
Soham Dan, Han Bao, Masashi Sugiyama
With the widespread use of machine learning for classification, it becomes increasingly important to be able to use weaker kinds of supervision for tasks in which it is hard to obt…
Investigations of the Influences of a CNN's Receptive Field on Segmentation of Subnuclei of Bilateral Amygdalae
Han Bao
Segmentation of objects with various sizes is relatively less explored in medical imaging, and has been very challenging in computer vision tasks in general. We hypothesize that th…
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images
Yanyuet Man, Xiangyun Ding, Xingcheng Yao +1
Throughout the world, breast cancer is one of the leading causes of female death. Recently, deep learning methods are developed to automatically grade breast cancer of histological…
Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification
Han Bao, Masashi Sugiyama
Complex classification performance metrics such as the F-measure and Jaccard index are often used, in order to handle class-imbalanced cases such as information retrieval and…
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
Takuya Shimada, Han Bao, Issei Sato +1
Pairwise similarities and dissimilarities between data points might be easier to obtain than fully labeled data in real-world classification problems, e.g., in privacy-aware situat…
Imitation Learning from Imperfect Demonstration
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao +2
Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectivel…
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
Seiichi Kuroki, Nontawat Charoenphakdee, Han Bao +3
Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different, and labels in the target domain are unavai…
Classification from Pairwise Similarity and Unlabeled Data
Han Bao, Gang Niu, Masashi Sugiyama
Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high. To overcome th…
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
Han Bao, Tomoya Sakai, Issei Sato +1
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances)…