Papers Object Categorization
“Object Categorization” 태그가 달린 논문 80편 · 필터 해제
Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment
Biologically inspired model (BIM) for image recognition is a robust computational architecture, which has attracted widespread attention. BIM can be described as a four-layer structure based on the mechanisms of the visu…
Object CategorizationAre we done with object recognition? The iCub robot's perspective
We report on an extensive study of the benefits and limitations of current deep learning approaches to object recognition in robot vision scenarios, introducing a novel dataset used for our investigation. To avoid the bi…
Deep LearningImage RetrievalObjectObject Categorization+3SimiNet: a Novel Method for Quantifying Brain Network Similarity
Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, m…
Graph SimilarityObject CategorizationCross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we pr…
ClassificationComputational EfficiencyDictionary LearningFace Recognition+5Weakly-Supervised Spatial Context Networks
We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a representation of one image patch from …
ObjectObject CategorizationEvolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks
A promising paradigm for achieving highly efficient deep neural networks is the idea of evolutionary deep intelligence, which mimics biological evolution processes to progressively synthesize more efficient networks. A c…
Object Categorizationobject-detectionObject DetectionObject categorization in finer levels requires higher spatial frequencies, and therefore takes longer
The human visual system contains a hierarchical sequence of modules that take part in visual perception at different levels of abstraction, i.e., superordinate, basic, and subordinate levels. One important question is to…
Object CategorizationObject RecognitionLearning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a …
ObjectObject CategorizationObject DiscoveryEmergence of Selective Invariance in Hierarchical Feed Forward Networks
Many theories have emerged which investigate how in- variance is generated in hierarchical networks through sim- ple schemes such as max and mean pooling. The restriction to max/mean pooling in theoretical and empirical …
Object CategorizationCost-Effective Active Learning for Deep Image Classification
Recent successes in learning-based image classification, however, heavily rely on the large number of annotated training samples, which may require considerable human efforts. In this paper, we propose a novel active lea…
Active LearningClassificationFace RecognitionGeneral Classification+4Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks
Deep convolutional neural networks (CNNs) trained for object classification have a number of striking similarities with the primate ventral visual stream. In particular, activity in early, intermediate, and late layers i…
ObjectObject CategorizationEffective Deterministic Initialization for $k$-Means-Like Methods via Local Density Peaks Searching
The $k$-means clustering algorithm is popular but has the following main drawbacks: 1) the number of clusters, $k$, needs to be provided by the user in advance, 2) it can easily reach local minima with randomly selected …
ClusteringObject CategorizationA deep representation for depth images from synthetic data
Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, th…
ColorizationObject CategorizationDeep Learning Human Mind for Automated Visual Classification
What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by h…
ClassificationDeep LearningEEGElectroencephalogram (EEG)+3Systematic evaluation of CNN advances on the ImageNet
The paper systematically studies the impact of a range of recent advances in CNN architectures and learning methods on the object categorization (ILSVRC) problem. The evalution tests the influence of the following choice…
Object CategorizationA New Manifold Distance Measure for Visual Object Categorization
Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level comparison and thus easily affected by image rota…
ClusteringObjectObject CategorizationObject Recognition+1Semi-supervised Vocabulary-informed Learning
Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, po…
Object CategorizationOpen Set LearningZero-Shot LearningContextual object categorization with energy-based model
Object categorization is a hot issue of an image mining. Contextual information between objects is one of the important semantic knowledge of an image. However, the previous researches for an object categorization have n…
modelObjectObject CategorizationCan Boosting with SVM as Week Learners Help?
Object recognition in images involves identifying objects with partial occlusions, viewpoint changes, varying illumination, cluttered backgrounds. Recent work in object recognition uses machine learning techniques SVM-KN…
ObjectObject CategorizationObject RecognitionThesis: Multiple Kernel Learning for Object Categorization
Object Categorization is a challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. In the past, many descriptors have been proposed which aid object categoriz…
ObjectObject Categorization