Papers Object Categorization
“Object Categorization” 태그가 달린 논문 80편 · 필터 해제
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning
The importance of wild video based image set recognition is becoming monotonically increasing. However, the contents of these collected videos are often complicated, and how to efficiently perform set modeling and featur…
ClassificationEmotion RecognitionFace RecognitionGeneral Classification+3Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition
Service robots are expected to operate effectively in human-centric environments for long periods of time. In such realistic scenarios, fine-grained object categorization is as important as basic-level object categorizat…
3D Object RecognitionObjectObject CategorizationObject RecognitionVisualizing Representational Dynamics with Multidimensional Scaling Alignment
Representational similarity analysis (RSA) has been shown to be an effective framework to characterize brain-activity profiles and deep neural network activations as representational geometry by computing the pairwise di…
Object CategorizationLearning robust visual representations using data augmentation invariance
Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream. Yet, artificial and biological networks s…
Data AugmentationObject CategorizationImproved object recognition using neural networks trained to mimic the brain's statistical properties
The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are capable of human-level performance on many …
ObjectObject CategorizationObject RecognitionTransfer LearningAligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures
Deep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processi…
AnatomyObject CategorizationPCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras
We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high tempor…
Dimensionality Reductionfeature selectionObjectObject Categorization+3Humans can decipher adversarial images
How similar is the human mind to the sophisticated machine-learning systems that mirror its performance? Models of object categorization based on convolutional neural networks (CNNs) have achieved human-level benchmarks …
Autonomous VehiclesClassificationGeneral ClassificationObject CategorizationGrassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. In order to extend the advantages of the Euclidean based dimensionality reduction methods to …
Dimensionality ReductionFace RecognitionGeneral ClassificationGesture Recognition+4Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network
Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. The task is challenging due to the facts that (1) training images with gro…
ObjectObject CategorizationBest sources forward: domain generalization through source-specific nets
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distribut…
Domain AdaptationDomain GeneralizationObject CategorizationSparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?
The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization. Despite the success of SR/CR bas…
ClassificationFace RecognitionGeneral ClassificationObject CategorizationData augmentation instead of explicit regularization
Contrary to most machine learning models, modern deep artificial neural networks typically include multiple components that contribute to regularization. Despite the fact that some (explicit) regularization techniques, s…
Data AugmentationObject CategorizationRecurrent Convolutional Fusion for RGB-D Object Recognition
Providing machines with the ability to recognize objects like humans has always been one of the primary goals of machine vision. The introduction of RGB-D cameras has paved the way for a significant leap forward in this …
ObjectObject CategorizationObject RecognitionMultiple Manifolds Metric Learning with Application to Image Set Classification
In image set classification, a considerable advance has been made by modeling the original image sets by second order statistics or linear subspace, which typically lie on the Riemannian manifold. Specifically, they are …
ClassificationFace RecognitionGeneral ClassificationMetric Learning+1A Simple Riemannian Manifold Network for Image Set Classification
In the domain of image-set based classification, a considerable advance has been made by representing original image sets as covariance matrices which typical lie in a Riemannian manifold. Specifically, it is a Symmetric…
ClassificationFace RecognitionGeneral ClassificationObject CategorizationVisual Object Categorization Based on Hierarchical Shape Motifs Learned From Noisy Point Cloud Decompositions
Object shape is a key cue that contributes to the semantic understanding of objects. In this work we focus on the categorization of real-world object point clouds to particular shape types. Therein surface description an…
ObjectObject CategorizationVISER: Visual Self-Regularization
In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation. Given a visual model trained by a labeled dataset in a supervised fashion, w…
Object CategorizationRetrievalConvolutional Networks for Object Category and 3D Pose Estimation from 2D Images
Current CNN-based algorithms for recovering the 3D pose of an object in an image assume knowledge about both the object category and its 2D localization in the image. In this paper, we relax one of these constraints and …
3D Pose EstimationObjectObject CategorizationPose EstimationDual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-…
General ClassificationObjectObject Categorization