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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 80 papers

TitleStatusHype
Effective Deterministic Initialization for k-Means-Like Methods via Local Density Peaks Searching—0
Emergence of Selective Invariance in Hierarchical Feed Forward Networks—0
Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment—0
Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks—0
Finding Optimal Combination of Kernels using Genetic Programming—0
From N to N+1: Multiclass Transfer Incremental Learning—0
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
Heterogeneous Visual Features Fusion via Sparse Multimodal Machine—0
Humans can decipher adversarial images—0
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification—0
Data augmentation instead of explicit regularizationCode0
Learning robust visual representations using data augmentation invarianceCode0
Deep Learning Human Mind for Automated Visual ClassificationCode0
Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorizationCode0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object RecognitionCode0
Vocabulary-informed Zero-shot and Open-set LearningCode0
Improved object recognition using neural networks trained to mimic the brain's statistical propertiesCode0
Vision CNNs trained to estimate spatial latents learned similar ventral-stream-aligned representationsCode0
Systematic evaluation of CNN advances on the ImageNetCode0
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN ModelsCode0
Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection NetworkCode0
Recurrent Convolutional Fusion for RGB-D Object RecognitionCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNsCode0
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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