Describing people: A poselet-based approach to attribute classification

By Lubomir D. Bourdev, Subhransu Maji, Jitendra Malik. 2011

384 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Learning Face Attributes in the Wild (2014, background): “[4] built a three-level SVM system to extract higherlevel information.”
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition (2014, background): “The second dataset is the H3D dataset [6] which deﬁnes 9 attributes for a subset of the person images from Pascal VOC 2007.”
UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory (2016, background): “[10, 17, 71], but as the number of task grows it becomes impossible to use one dataset for all.”
Bilinear CNN Models for Fine-Grained Visual Recognition (2015, background): “Recently variants of such models based on convolutional neural networks (CNNs) [2, 38] have been shown to significantly improve over earlier work that relied on hand-crafted features [1, 11, 39].”
PANDA: Pose Aligned Networks for Deep Attribute Modeling (2013, methodology): “[4], which is a three-layer feed forward classification system and the first layer predicts each attribute value for each poselet type.”
From generic to specific deep representations for visual recognition (2014, background): “PASCAL VOC Object [9] H3D human attributes [6] Cat&Dog breeds [25] VOC Human Action [9] Holiday scenes [16] MIT 67 Indoor Scenes [29] Object attributes [10] Bird subordinate [39] Stanford 40 Actions [42] Paris buildings [27] SUN 397 Scene [41] SUN scene attributes [26] 102 Flowers [23] Visual Phrase”
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids (2014, neutral): “Traditionally, algorithms such as Deformable Parts Models (DPMs) [7] and Poselets [8] achieved high-quality object detection with sliding-window multi-template detectors on HOG [9] features.”
Symbiotic Segmentation and Part Localization for Fine-Grained Categorization (2013, background): “It successfully pulls together a number of recent research strands: the use of distinctive parts for registration when discriminating sub-ordinate categories [5, 24, 32, 34, 35]; unsupervised discovery of midlevel discriminative patches [23, 28, 32]; learning a DPM given only weak annotation (a loos”
A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects (2013, background): “Attributes are widely used in many different applications like object describing [6], zero-shot learning [17, 11], face verification [15], object recognition [34, 10], person and clothing describing [1, 2], image search and retrieval [16, 13, 36], and action/activity recognition [19, 35, 9] etc.”
Adapting Models to Signal Degradation using Distillation (2016, methodology): “Models In our experiments, both f and g are based on CNNs pre-trained on the ImageNet dataset [14].”