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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1911–1920 of 2042 papers

Title	Date	Tasks	Status
Reconstruction-guided attention improves the robustness and shape processing of neural networks	Sep 27, 2022	ObjectObject Recognition	CodeCode Available
Facial Expression Recognition Research Based on Deep Learning	Apr 22, 2019	Deep LearningFacial Expression Recognition	CodeCode Available
Optimizing Spatio-Temporal Information Processing in Spiking Neural Networks via Unconstrained Leaky Integrate-and-Fire Neurons and Hybrid Coding	Aug 22, 2024	object-detectionObject Detection	CodeCode Available
Recurrent Attention Models with Object-centric Capsule Representation for Multi-object Recognition	Oct 11, 2021	DecoderObject	CodeCode Available
SHTOcc: Effective 3D Occupancy Prediction with Sparse Head and Tail Voxels	May 28, 2025	Autonomous DrivingGPU	CodeCode Available
Captioning Images with Diverse Objects	Jun 24, 2016	ObjectObject Recognition	CodeCode Available
Recurrent Convolutional Fusion for RGB-D Object Recognition	Jun 5, 2018	ObjectObject Categorization	CodeCode Available
Exploring Novel Object Recognition and Spontaneous Location Recognition Machine Learning Analysis Techniques in Alzheimer's Mice	Dec 12, 2023	Object RecognitionPose Estimation	CodeCode Available
Experiments with mmWave Automotive Radar Test-bed	Dec 29, 2019	Object Recognition	CodeCode Available
Object Recognition under Multifarious Conditions: A Reliability Analysis and A Feature Similarity-based Performance Estimation	Feb 18, 2019	Object Recognition	CodeCode Available

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All datasets shape bias CIFAR10-DVS N-Caltech 101 ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 Gesture MECCANO N-CARS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Imagen	shape bias	98.7	—	Unverified
2	Stable Diffusion	shape bias	92.7	—	Unverified
3	Parti	shape bias	91.7	—	Unverified
4	ViT-22B-384	shape bias	86.4	—	Unverified
5	ViT-22B-560	shape bias	83.8	—	Unverified
6	CLIP (ViT-B)	shape bias	79.9	—	Unverified
7	ViT-22B-224	shape bias	78	—	Unverified
8	ResNet-50 (L2 eps 5.0 adv trained)	shape bias	69.5	—	Unverified
9	ResNet-50 (with strong augmentations)	shape bias	62.2	—	Unverified
10	SWSL (ResNeXt-101)	shape bias	49.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.55	—	Unverified
2	SSNN	Accuracy (% )	78.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.62	—	Unverified
2	SSNN	Accuracy (% )	79.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	18.75	—	Unverified
2	yun	Top 5 Accuracy	14.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	DY	Top 5 Accuracy	0.08	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	AJ2021	Top 5 Accuracy	27.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSNN	Accuracy (% )	94.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Faster-RCNN	mAP	30.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	96	—	Unverified