Object Categorization
25 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
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
These methods lack a mechanism to map deep layer feature maps to input dimensions.
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
MRLoghmani/relative-rotation
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Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions.
Deep Learning Human Mind for Automated Visual Classification
AliAbyaneh/Extracting-Image-from-EEG-signals
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CVPR 2017
In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.
Data augmentation instead of explicit regularization
alexhernandezgarcia/data-aug-invariance
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ICLR 2018
Despite the fact that some (explicit) regularization techniques, such as weight decay and dropout, require costly fine-tuning of sensitive hyperparameters, the interplay between them and other elements that provide implicit regularization is not well understood yet.
Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network
YBZh/PartNet
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In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization.
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
dicarlolab/cornet
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NeurIPS 2019
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream.
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
ofa-sys/ofa
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In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization.
Collaborative Receptive Field Learning
However, measuring pairwise distance of RF's for building the similarity graph is a nontrivial problem.
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.
