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 1151–1200 of 2042 papers

TitleStatusHype
Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines—0
Weakly Supervised Localization using Deep Feature Maps—0
Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning—0
Weighted Sigmoid Gate Unit for an Activation Function of Deep Neural Network—0
What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation—0
What are the visual features underlying human versus machine vision?—0
What Can I Do Around Here? Deep Functional Scene Understanding for Cognitive Robots—0
What can we learn about CNNs from a large scale controlled object dataset?—0
What deep learning can tell us about higher cognitive functions like mindreading?—0
What do We Learn by Semantic Scene Understanding for Remote Sensing imagery in CNN framework?—0
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?—0
What's in a Name? Beyond Class Indices for Image Recognition—0
What's in my Room? Object Recognition on Indoor Panoramic Images—0
What takes the brain so long: Object recognition at the level of minimal images develops for up to seconds of presentation time—0
What you need to know about the state-of-the-art computational models of object-vision: A tour through the models—0
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation—0
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks—0
Why The Brain Separates Face Recognition From Object Recognition—0
Winograd Convolution for Deep Neural Networks: Efficient Point Selection—0
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model—0
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
You Only Speak Once to See—0
Your head is there to move you around: Goal-driven models of the primate dorsal pathway—0
Zero-Aliasing Correlation Filters for Object Recognition—0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery—0
Zero-shot counting with a dual-stream neural network model—0
Zero Shot Hashing—0
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature—0
Zero-shot Learning with Deep Neural Networks for Object Recognition—0
Zero-shot object prediction using semantic scene knowledge—0
Zero-Shot Object Recognition by Semantic Manifold Distance—0
Zero-Shot Object Recognition System based on Topic Model—0
Zero-shot recognition with unreliable attributes—0
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?—0
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM—0
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks—0
Generating Image Descriptions with Gold Standard Visual Inputs: Motivation, Evaluation and Baselines—0
Generating Photo-realistic Images from LiDAR Point Clouds with Generative Adversarial Networks—0
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models—0
Generic decoding of seen and imagined objects using hierarchical visual features—0
Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks—0
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing—0
Geometry Aware Constrained Optimization Techniques for Deep Learning—0
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning—0
Glitch Classification and Clustering for LIGO with Deep Transfer Learning—0
Global Deconvolutional Networks for Semantic Segmentation—0
Going Deeper into Action Recognition: A Survey—0
Gradient-based Laplacian Feature Selection—0
Gradients of Counterfactuals—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