Papers Object Categorization
“Object Categorization” 태그가 달린 논문 80편 · 필터 해제
Finding Optimal Combination of Kernels using Genetic Programming
In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorization. It is a challenging problem, especially when the images have clutter background, occlusions or di…
ObjectObject CategorizationRotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category. Unlike previous approaches that use known…
3D Object ClassificationObjectObject CategorizationPose EstimationConvolutional Models for Joint Object Categorization and Pose Estimation
In the task of Object Recognition, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a rep…
ObjectObject CategorizationObject RecognitionPose EstimationBasic Level Categorization Facilitates Visual Object Recognition
Recent advances in deep learning have led to significant progress in the computer vision field, especially for visual object recognition tasks. The features useful for object classification are learned by feed-forward de…
ObjectObject CategorizationObject RecognitionTransfer LearningClassifier Adaptation at Prediction Time
Classifiers for object categorization are usually evaluated by their accuracy on a set of i.i.d. test examples. This provides us with an estimate of the expected error when applying the classifiers to a single new image.…
Object CategorizationPredictionSegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label p…
DecoderObject CategorizationBio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition
Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and nonrigid deformations. But despite these huge variati…
ObjectObject CategorizationObject RecognitionTowards Learning free Naive Bayes Nearest Neighbor-based Domain Adaptation
As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train …
Domain AdaptationObject CategorizationA Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization
We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical …
Distributed OptimizationObjectObject CategorizationPose EstimationA Unified Semantic Embedding: Relating Taxonomies and Attributes
We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes. Contrary to prior work which only uti…
Object CategorizationDeep Fisher Kernels - End to End Learning of the Fisher Kernel GMM Parameters
Fisher Kernels and Deep Learning were two developments with significant impact on large-scale object categorization in the last years. Both approaches were shown to achieve state-of-the-art results on large-scale object …
Learning TheoryObject CategorizationCollaborative Receptive Field Learning
The challenge of object categorization in images is largely due to arbitrary translations and scales of the foreground objects. To attack this difficulty, we propose a new approach called collaborative receptive field le…
General ClassificationObject CategorizationLearning Mid-Level Features and Modeling Neuron Selectivity for Image Classification
We now know that mid-level features can greatly enhance the performance of image learning, but how to automatically learn the image features efficiently and in an unsupervised manner is still an open question. In this pa…
Age EstimationClusteringFace RecognitionGender Classification+6Controlled Sparsity Kernel Learning
Multiple Kernel Learning(MKL) on Support Vector Machines(SVMs) has been a popular front of research in recent times due to its success in application problems like Object Categorization. This success is due to the fact t…
Object CategorizationAttribute-Based Classification for Zero-Shot Visual Object Categorization
We study the problem of object recognition for categories for which we have no training examples, a task also called zero-data or zero-shot learning. This situation has hardly been studied in computer vision research, e…
AttributeClassificationObjectObject Categorization+2From N to N+1: Multiclass Transfer Incremental Learning
Since the seminal work of Thrun [17], the learning to learn paradigm has been defined as the ability of an agent to improve its performance at each task with experience, with the number of tasks. Within the object catego…
Incremental LearningObject CategorizationTransfer LearningA Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit
Data fusion, which effectively fuses multiple prediction lists from different kinds of features to obtain an accurate model, is a crucial component in various computer vision applications. Robust late fusion (RLF) is a r…
Event DetectionObject CategorizationHeterogeneous Visual Features Fusion via Sparse Multimodal Machine
To better understand, search, and classify image and video information, many visual feature descriptors have been proposed to describe elementary visual characteristics, such as the shape, the color, the texture, etc. Ho…
Feature Importanceimage-classificationImage ClassificationMulti-Label Image Classification+2Semi-supervised Node Splitting for Random Forest Construction
Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples. Existing solutions fail to properly partition the feature space if there are insufficient training d…
Image SegmentationObject CategorizationSemantic SegmentationOn the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
Motivated from real world problems, like object categorization, we study a particular mixed-norm regularization for Multiple Kernel Learning (MKL). It is assumed that the given set of kernels are grouped into distinct co…
Object Categorization