SOTAVerified

Object Categorization

Object categorization identifies which label, from a given set, best corresponds to an image region defined by an input image and bounding box.

Papers

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Showing 21–30 of 80 papers

TitleStatusHype
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNsCode0
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning—0
Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition—0
Visualizing Representational Dynamics with Multidimensional Scaling Alignment—0
Learning robust visual representations using data augmentation invarianceCode0
Improved object recognition using neural networks trained to mimic the brain's statistical propertiesCode0
Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures—0
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras—0
Humans can decipher adversarial images—0
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Unified-IOXLCategorization (ablation)61.7—Unverified
2GPV-2Categorization (ablation)54.7—Unverified
3CLIPCategorization (ablation)48.1—Unverified
4OFA_LargeCategorization (ablation)22.6—Unverified