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ACCV
2010
Springer
13 years 1 months ago
Unsupervised Selective Transfer Learning for Object Recognition
Abstract. We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for r...
Wei-Shi Zheng, Shaogang Gong, Tao Xiang
CVPR
2004
IEEE
14 years 8 months ago
Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting
We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter...
Fu Jie Huang, Léon Bottou, Yann LeCun
CVPR
2007
IEEE
14 years 8 months ago
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
Li-Jia Li, Gang Wang, Fei-Fei Li 0002
BMVC
2010
13 years 4 months ago
A general boosting-based framework for active object recognition
We propose a novel general framework with a boosting algorithm to achieve active object classification by view selection. The proposed framework actively decides the next best vie...
Zhaoyin Jia, Yao-Jen Chang, Tsuhan Chen
SIP
2003
13 years 8 months ago
Object Structure from Noisy Images
We describe the establishment of a compound object model for object recognition purposes which provides the frame for the extraction of object structure from images degraded by no...
Gabriele Peters