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 2021–2030 of 2042 papers

Title	Date	Tasks	Status
Domain Generalization In Robust Invariant Representation	Apr 7, 2023	Domain GeneralizationObject Recognition	CodeCode Available
Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification	May 20, 2022	image-classificationImage Classification	CodeCode Available
Local Aggregation for Unsupervised Learning of Visual Embeddings	Mar 29, 2019	ClusteringContrastive Learning	CodeCode Available
Domain Generalization by Solving Jigsaw Puzzles	Jun 1, 2019	Domain GeneralizationObject Recognition	CodeCode Available
Deep Discrete Hashing with Self-supervised Pairwise Labels	Jul 7, 2017	Deep HashingGeneral Classification	CodeCode Available
Visually Interpretable Subtask Reasoning for Visual Question Answering	May 12, 2025	AttributeObject Recognition	CodeCode Available
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment	Nov 16, 2017	Domain AdaptationObject Recognition	CodeCode Available
The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns	Jul 8, 2024	object-detectionObject Detection	CodeCode Available
PANDA: Pose Aligned Networks for Deep Attribute Modeling	Nov 21, 2013	AttributeFacial Attribute Classification	CodeCode Available
Robust Visual Tracking via Hierarchical Convolutional Features	Jul 12, 2017	Object RecognitionVisual Tracking	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