SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1751–1800 of 2042 papers

TitleStatusHype
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction—0
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition—0
MMSS: Multi-Modal Sharable and Specific Feature Learning for RGB-D Object Recognition—0
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling—0
Unsupervised Generation of a Viewpoint Annotated Car Dataset From Videos—0
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization—0
Hierarchical Convolutional Features for Visual Tracking—0
Query Adaptive Similarity Measure for RGB-D Object Recognition—0
RIDE: Reversal Invariant Descriptor Enhancement—0
Feature-based Attention in Convolutional Neural Networks—0
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training DataCode0
Convolutional Models for Joint Object Categorization and Pose Estimation—0
Basic Level Categorization Facilitates Visual Object Recognition—0
Hand-Object Interaction and Precise Localization in Transitive Action Recognition—0
Multimodal Skip-gram Using Convolutional Pseudowords—0
Visual7W: Grounded Question Answering in Images—0
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer—0
Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images—0
Pixel-wise Segmentation of Street with Neural Networks—0
Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation—0
Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations—0
Generic decoding of seen and imagined objects using hierarchical visual features—0
PERCH: Perception via Search for Multi-Object Recognition and Localization—0
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization—0
Better Exploiting OS-CNNs for Better Event Recognition in Images—0
DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer—0
Online Vision- and Action-Based Object Classification Using Both Symbolic and Subsymbolic Knowledge Representations—0
Compression of Deep Neural Networks on the Fly—0
Deep Trans-layer Unsupervised Networks for Representation Learning—0
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing—0
Amodal Completion and Size Constancy in Natural Scenes—0
Feature Evaluation of Deep Convolutional Neural Networks for Object Recognition and Detection—0
Analyzing structural characteristics of object category representations from their semantic-part distributions—0
On Binary Classification with Single-Layer Convolutional Neural Networks—0
DeepSat - A Learning framework for Satellite ImageryCode0
Hierarchical Deep Learning Architecture For 10K Objects Classification—0
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces—0
Object Recognition from Short Videos for Robotic Perception—0
Generating Image Descriptions with Gold Standard Visual Inputs: Motivation, Evaluation and Baselines—0
From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes—0
Visual Classifier Prediction by Distributional Semantic Embedding of Text Descriptions—0
Partitioning Large Scale Deep Belief Networks Using Dropout—0
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition—0
Places205-VGGNet Models for Scene RecognitionCode0
Sublinear Partition EstimationCode0
HFirst: A Temporal Approach to Object Recognition—0
Deep supervised learning for hyperspectral data classification through convolutional neural networksCode0
Thinning Algorithm Using Hypergraph Based Morphological Operators—0
Multimodal Deep Learning for Robust RGB-D Object RecognitionCode0
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition—0
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All datasetsshape biasCIFAR10-DVSN-Caltech 101ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 GestureMECCANON-CARS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7—Unverified
2Stable Diffusionshape bias92.7—Unverified
3Partishape bias91.7—Unverified
4ViT-22B-384shape bias86.4—Unverified
5ViT-22B-560shape bias83.8—Unverified
6CLIP (ViT-B)shape bias79.9—Unverified
7ViT-22B-224shape bias78—Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5—Unverified
9ResNet-50 (with strong augmentations)shape bias62.2—Unverified
10SWSL (ResNeXt-101)shape bias49.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55—Unverified
2SSNNAccuracy (% )78.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62—Unverified
2SSNNAccuracy (% )79.25—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75—Unverified
2yunTop 5 Accuracy14.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2DYTop 5 Accuracy0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2AJ2021Top 5 Accuracy27.68—Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96—Unverified