Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Image Parsing: Unifying Segmentation, Detection, and Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Hierarchical Models of Scenes, Objects, and Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Depth from Familiar Objects: A Hierarchical Model for 3D Scenes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
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The problem of holistic scene understanding involves many vision tasks such as depth estimation, scene categorization, event categorization, etc. Each of these tasks explores some aspects of the scene but, these tasks are related in that, they represent attributes of the same scene. An intuition is that one task can provide meaningful attributes to aid the learning process of another task. In this work, we propose a generic model (together with learning and inference techniques) for connecting different vision tasks in the form of a 2-layer cascade. Our model considers the first layer as a hidden layer, where the latent variables are inferred by feedback from the second layer. The feedback mechanism allows the first layer classifiers to focus on more important image modes, and draws their output towards "attributes" rather than the original "labels". Our model also automatically discovers sparse connections between the learned attributes on the first layer and the target task on the second layer. Note that in our model, the same vision tasks can act as attribute learners as well as target tasks, while being set up on different layers. In extensive experiments, we show that the same proposed model improves the performance in all the tasks we consider: single image depth estimation, scene categorization, saliency detection and event categorization.