Results 1 to 10 of about 76,480 (326)

Self Paced Deep Learning for Weakly Supervised Object Detection

, 2018
In a weakly-supervised scenario object detectors need to be trained using image-level annotation alone. Since bounding-box-level ground truth is not available, most of the solutions proposed so far are based on an iterative, Multiple Instance Learning ...
Culibrk, Dubravko, Nabi, Moin, Sangineto, Enver, Sebe, Nicu   +3 more
core   +1 more source

Audio Event Detection using Weakly Labeled Data

, 2016
Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data.
Gencoglu O., J. F., Kons Z., Kumar A., Mandel M. I., Pancoast S., Pikrakis A., Rumelhart D. E., Stowell D., Wang F., Wang J., Werbos P. J., Zhou Z.-H.   +12 more
core   +1 more source

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

, 2018
Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism.
Huang, Kaiqi, Li, Debang, Wu, Huikai, Zhang, Junge   +3 more
core   +1 more source

A Weakly Supervised Network for Coarse-to-Fine Change Detection in Hyperspectral Images

Remote Sensing
Hyperspectral image change detection (HSI-CD) provides substantial value in environmental monitoring, urban planning and other fields. In recent years, deep-learning based HSI-CD methods have made remarkable progress due to their powerful nonlinear ...
Yadong Zhao, Zhao Chen
doaj   +1 more source

A weakly-supervised follicle segmentation method in ultrasound images

Scientific Reports
Accurate follicle segmentation in ultrasound images is crucial for monitoring follicle development, a key factor in fertility treatments. However, obtaining pixel-level annotations for fully supervised instance segmentation is often impractical due to ...
Guanyu Liu, Weihong Huang, Yanping Li, Qiong Zhang, Jing Fu, Hongying Tang, Jia Huang, Zhongteng Zhang, Lei Zhang, Yu Wang, Jianzhong Hu   +10 more
doaj   +1 more source

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Remote Sensing
Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security.
Lukang Wang, Min Zhang, Xu Gao, Wenzhong Shi   +3 more
doaj   +1 more source

Weakly-Supervised Locally Linear Embedding Model for Discriminant Feature Learning

, 2022
Luqing Wang, Chengsu Wang, Hongjun Wang, Chongshou Li, Jie Hu, Tianrui Li   +5 more
openalex   +1 more source

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization [PDF]

, 2020
Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang   +2 more
openalex   +1 more source

A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning [PDF]

, 2022
Ruichao Yang, Jing Ma, Hongzhan Lin, Wei Gao   +3 more
openalex   +3 more sources

Seed perception learning for weakly supervised semantic segmentation

Complex & Intelligent Systems
The core challenge in image-level weakly supervised semantic segmentation lies in generating high-quality object localization maps from simple image labels.
Wanchun Sun, Shujia Li, Xinyu Duan
doaj   +1 more source