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 441–450 of 2042 papers

Title	Date	Tasks	Status
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition	Mar 6, 2024	Contrastive LearningFine-Grained Visual Recognition	—Unverified
Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval	Mar 1, 2024	ObjectObject Recognition	—Unverified
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model	Feb 29, 2024	Language ModelingLanguage Modelling	—Unverified
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments	Feb 29, 2024	AttributeCollision Avoidance	—Unverified
Probing Multimodal Large Language Models for Global and Local Semantic Representations	Feb 27, 2024	Image to textobject-detection	CodeCode Available
ISCUTE: Instance Segmentation of Cables Using Text Embedding	Feb 19, 2024	Instance SegmentationObject Recognition	—Unverified
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Autonomous Agents	Feb 17, 2024	GPUNeural Architecture Search	—Unverified
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection	Feb 13, 2024	Contrastive LearningObject	—Unverified
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View	Feb 12, 2024	3D Point Cloud ClassificationContinual Learning	—Unverified
Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems	Feb 12, 2024	Object Recognition	—Unverified

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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