Results 1 - 10 of 75

Beyond Spatial Pooling: Fine-Grained Representation Learning in Multiple Domains

by Chi Li , Austin Reiter , Gregory D Hager
"... Abstract Object recognition systems have shown great progress over recent years. However, creating object representations that are robust to changes in viewpoint while capturing local visual details continues to be a challenge. In particular, recent convolutional architectures employ spatial poolin ..."
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Abstract Object recognition systems have shown great progress over recent years. However, creating object representations that are robust to changes in viewpoint while capturing local visual details continues to be a challenge. In particular, recent convolutional architectures employ spatial

Learning mid-level features for recognition

by Y-lan Boureau, Francis Bach, Yann LeCun, Jean Ponce , 2010
"... Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps: (1) a coding step, which performs a pointwise tra ..."
Abstract - Cited by 228 (13 self) - Add to MetaCart
transformation of the descriptors into a representation better adapted to the task, and (2) a pooling step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pooling schemes have been proposed in the literature. The goal of this paper is threefold. We seek

Hierarchical Part Matching for Fine-Grained Visual Categorization

by Lingxi Xie, Qi Tian, Richang Hong, Shuicheng Yan, Bo Zhang , 2013
"... As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing atten-tion these years. Different with traditional image classifi-cation tasks in which objects have large inter-class varia-tion, the visual concepts in the fine-grained datasets, such as hu ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
to train a robust model. In this paper, we propose a powerful flowchart named Hierarchical Part Matching (HPM) to cope with fine-grained classification tasks. We extend the Bag-of-Features (BoF) model by introducing several novel modules to inte-grate into image representation, including foreground in

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

by Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell
"... We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks an ..."
Abstract - Cited by 203 (22 self) - Add to MetaCart
and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained

T.: Deformable part descriptors for fine-grained recognition and attribute prediction

by Ning Zhang, Ryan Farrell, Forrest I, Ola Trevor Darrell - In: ICCV (2013
"... Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories. Discriminative markings are often highly localized, leading traditional object recognition approaches to struggle with the large pose variation often present in these domains ..."
Abstract - Cited by 24 (3 self) - Add to MetaCart
-normalized descriptors from the latent parts of a weakly-supervised DPM. These representations enable pooling across pose and viewpoint, in turn facilitating tasks such as fine-grained recognition and attribute prediction. Experiments conducted on the Caltech-UCSD Birds 200 dataset and Berkeley Human Attribute dataset

GloVe: Global Vectors for Word Representation

by Jeffrey Pennington, Richard Socher, Christopher D. Manning
"... Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arith-metic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regular ..."
Abstract - Cited by 123 (9 self) - Add to MetaCart
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arith-metic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed

Semi-supervised bilinear subspace learning

by Dong Xu, Shuicheng Yan - IEEE Trans. Image Process , 2009
"... Abstract—Recent research has demonstrated the success of tensor based subspace learning in both unsupervised and supervised configurations (e.g., 2-D PCA, 2-D LDA, and DATER). In this correspondence, we present a new semi-supervised subspace learning algorithm by integrating the tensor representatio ..."
Abstract - Cited by 14 (3 self) - Add to MetaCart
. An iterative algorithm, referred to as adaptive reg-ularization based semi-supervised discriminant analysis with tensor rep-resentation (ARSDA/T), is also developed to compute the solution. In ad-dition to handling tensor data, a vector-based variant (ARSDA/V) is also presented, in which the tensor data

The brain of musicians: a model for functional and structural adaptation

by Gottfried Schlaug - Ann NY Acad Sci 930 , 2001
"... ABSTRACT: Musicians form an ideal subject pool in which one can investigate possible cerebral adaptations to unique requirements of skilled performance as well as cerebral correlates of unique musical abilities such as absolute pitch and others. There are several reasons for this. First, the commenc ..."
Abstract - Cited by 84 (8 self) - Add to MetaCart
ABSTRACT: Musicians form an ideal subject pool in which one can investigate possible cerebral adaptations to unique requirements of skilled performance as well as cerebral correlates of unique musical abilities such as absolute pitch and others. There are several reasons for this. First

Adaptive Deconvolutional Networks for Mid and High Level Feature Learning

by Matthew D. Zeiler, Graham W. Taylor, Rob Fergus
"... We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high-level objec ..."
Abstract - Cited by 55 (3 self) - Add to MetaCart
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high

Adaptive precision pooling of model neuron activities

by unknown authors
"... When performing a perceptual task, precision pooling occurs when an organism’s decisions are based on the activities of a small set of highly informative neurons. The Adaptive Precision Pooling Hypothesis links perceptual learning and decision making by stating that improvements in performance occur ..."
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When performing a perceptual task, precision pooling occurs when an organism’s decisions are based on the activities of a small set of highly informative neurons. The Adaptive Precision Pooling Hypothesis links perceptual learning and decision making by stating that improvements in performance
Results 1 - 10 of 75