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 61–70 of 2042 papers

Title	Date	Tasks	Status	Hype
Deep learning based infrared small object segmentation: Challenges and future directions	Feb 20, 2025	Autonomous VehiclesObject Recognition	—Unverified	0
RAPTOR: Refined Approach for Product Table Object Recognition	Feb 19, 2025	ObjectObject Recognition	—Unverified	0
Revealing Bias Formation in Deep Neural Networks Through the Geometric Mechanisms of Human Visual Decoupling	Feb 17, 2025	ObjectObject Recognition	—Unverified	0
"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models	Feb 17, 2025	Object RecognitionQuestion Answering	—Unverified	0
Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition	Feb 15, 2025	3D Object RecognitionObject Recognition	—Unverified	0
Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models	Feb 12, 2025	AttributeDiagnostic	CodeCode Available	1
DCENWCNet: A Deep CNN Ensemble Network for White Blood Cell Classification with LIME-Based Explainability	Feb 8, 2025	Data AugmentationObject Recognition	—Unverified	0
Unveiling the Potential of iMarkers: Invisible Fiducial Markers for Advanced Robotics	Jan 26, 2025	Object RecognitionScene Understanding	—Unverified	0
Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similarities	Jan 25, 2025	HallucinationObject	—Unverified	0
NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild	Jan 23, 2025	Earth ObservationObject Recognition	CodeCode Available	2

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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