Search Results for author: Osamu Yoshie

Found 11 papers, 4 papers with code

Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective

no code implementations8 Mar 2022 Quan Cui, Bingchen Zhao, Zhao-Min Chen, Borui Zhao, RenJie Song, Jiajun Liang, Boyan Zhou, Osamu Yoshie

This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i. e., image classification.

Image Classification Transfer Learning

ZeroVL: A Strong Baseline for Aligning Vision-Language Representations with Limited Resources

1 code implementation17 Dec 2021 Quan Cui, Boyan Zhou, Yu Guo, Weidong Yin, Hao Wu, Osamu Yoshie

However, these works require a tremendous amount of data and computational resources (e. g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration.

Contrastive Learning

PP-YOLOv2: A Practical Object Detector

1 code implementation21 Apr 2021 Xin Huang, Xinxin Wang, Wenyu Lv, Xiaying Bai, Xiang Long, Kaipeng Deng, Qingqing Dang, Shumin Han, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma, Osamu Yoshie

To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged.

OTA: Optimal Transport Assignment for Object Detection

1 code implementation CVPR 2021 Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, Jian Sun

Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.

object-detection Object Detection

A Reinforcement learning method for Optical Thin-Film Design

no code implementations13 Feb 2021 Anqing Jiang, LiangYao Chen, Osamu Yoshie

Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design.

reinforcement-learning

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection

1 code implementation12 Jan 2021 Zheng Ge, JianFeng Wang, Xin Huang, Songtao Liu, Osamu Yoshie

A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.

object-detection Object Detection +1

ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval

no code implementations ECCV 2020 Quan Cui, Qing-Yuan Jiang, Xiu-Shen Wei, Wu-Jun Li, Osamu Yoshie

Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.

Image Retrieval

Delving into the Imbalance of Positive Proposals in Two-stage Object Detection

no code implementations23 May 2020 Zheng Ge, Zequn Jie, Xin Huang, Chengzheng Li, Osamu Yoshie

The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i. e., post-classification layers) become highly biased towards the negative proposals in the early training stage.

object-detection Object Detection

NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing

no code implementations CVPR 2020 Xin Huang, Zheng Ge, Zequn Jie, Osamu Yoshie

To acquire the visible parts, a novel Paired-Box Model (PBM) is proposed to simultaneously predict the full and visible boxes of a pedestrian.

Pedestrian Detection

PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression

no code implementations16 Mar 2020 Zheng Ge, Zequn Jie, Xin Huang, Rong Xu, Osamu Yoshie

PS-RCNN first detects slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out.

Human Detection Object Detection

A new multilayer optical film optimal method based on deep q-learning

no code implementations7 Dec 2018 Anqing Jiang, Osamu Yoshie, LiangYao Chen

This model can converge the global optimum of the optical thin film structure, this will greatly improve the design efficiency of multi-layer films.

Q-Learning

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