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 1501–1550 of 2042 papers

TitleStatusHype
Multi-View Task-Driven Recognition in Visual Sensor Networks—0
Mutual exclusivity as a challenge for deep neural networks—0
Mutual Exclusivity Loss for Semi-Supervised Deep Learning—0
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks—0
Natural Language Descriptions of Human Activities Scenes: Corpus Generation and Analysis—0
Natural Scene Recognition Based on Superpixels and Deep Boltzmann Machines—0
NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?—0
NeRD: a Neural Response Divergence Approach to Visual Salience Detection—0
Nested Graph Words for Object Recognition—0
Nested Learning For Multi-Granular Tasks—0
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage—0
Neural Image Captioning—0
NeurAll: Towards a Unified Visual Perception Model for Automated Driving—0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans—0
Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs—0
Neurosymbolic AI - Why, What, and How—0
Neurosymbolic hybrid approach to driver collision warning—0
New Graph-based Features For Shape Recognition—0
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs—0
Noise-Adaptive Intelligent Programmable Meta-Imager—0
Non-iterative recomputation of dense layers for performance improvement of DCNN—0
Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Forests—0
Number detectors spontaneously emerge in a deep neural network designed for visual object recognition—0
Object and Text-guided Semantics for CNN-based Activity Recognition—0
Object-aware Feature Aggregation for Video Object Detection—0
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification—0
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer—0
Object-Centric World Model for Language-Guided Manipulation—0
Object classification in images of Neoclassical furniture using Deep Learning—0
Object-conditioned Bag of Instances for Few-Shot Personalized Instance Recognition—0
Object Sorting Using a Global Texture-Shape 3D Feature Descriptor—0
Object Detection based on LIDAR Temporal Pulses using Spiking Neural Networks—0
Object Detection based on the Collection of Geometric Evidence—0
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations—0
Object Localization with a Weakly Supervised CapsNet—0
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models—0
Object Proposal Generation using Two-Stage Cascade SVMs—0
Object-QA: Towards High Reliable Object Quality Assessment—0
Object Recognition and Identification Using ESM Data—0
Object Recognition as Classification via Visual Properties—0
Object Recognition Based on Amounts of Unlabeled Data—0
Object Recognition by a Minimally Pre-Trained System in the Process of Environment Exploration—0
Object Recognition by Using Multi-level Feature Point Extraction—0
Object Recognition for Economic Development from Daytime Satellite Imagery—0
Object recognition for robotics from tactile time series data utilising different neural network architectures—0
Object Recognition from Scientific Document based on Compartment Refinement Framework—0
Object Recognition from Short Videos for Robotic Perception—0
Object Recognition from very few Training Examples for Enhancing Bicycle Maps—0
Crowding in humans is unlike that in convolutional neural networks—0
Show:102550
← PrevPage 31 of 41Next →
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