Object Categorization
24 papers with code • 1 benchmarks • 2 datasets
Object categorization identifies which label, from a given set, best corresponds to an image region defined by an input image and bounding box.
Benchmarks
These leaderboards are used to track progress in Object Categorization
| Trend | Dataset | Best Model | Paper | Code | Compare |
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GRIT
UR5 Tool Dataset
Most implemented papers
RotationNet: 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.
Systematic 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.
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.
Are we done with object recognition? The iCub robot's perspective
VadymV/ICOS
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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.
Recurrent 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.
Improved object recognition using neural networks trained to mimic the brain's statistical properties
cfederer/TrainCNNsWithNeuralData
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To test this, we trained DCNNs on a composite task, wherein networks were trained to: a) classify images of objects; while b) having intermediate representations that resemble those observed in neural recordings from monkey visual cortex.
Learning robust visual representations using data augmentation invariance
alexhernandezgarcia/data-aug-invariance
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Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream.
Learning Physical Graph Representations from Visual Scenes
neuroailab/tnn
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NeurIPS 2020
To overcome these limitations, we introduce the idea of Physical Scene Graphs (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts.
IAUnet: Global Context-Aware Feature Learning for Person Re-Identification
blue-blue272/ImgReID-IAnet
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Furthermore, a Channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts.
Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization
kietzmannlab/svrhm21_rnn_explain
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NeurIPS Workshop SVRHM 2021
Recurrent neural networks (RNNs) have been shown to perform better than feedforward architectures in visual object categorization tasks, especially in challenging conditions such as cluttered images.
