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.
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.
Cost-Effective Active Learning for Deep Image Classification
In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner.
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.
Collaborative Receptive Field Learning
However, measuring pairwise distance of RF's for building the similarity graph is a nontrivial problem.
