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 1351–1375 of 2042 papers

TitleStatusHype
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision—0
Training Deep Spiking Neural Networks—0
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts—0
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods—0
Training the Untrainable: Introducing Inductive Bias via Representational Alignment—0
Transferable Adversarial Attacks on Black-Box Vision-Language Models—0
Transfer Learning for Material Classification using Convolutional Networks—0
Transferred Fusion Learning using Skipped Networks—0
Transferring Landmark Annotations for Cross-Dataset Face Alignment—0
Transformational Sparse Coding—0
Transformer-Based Microbubble Localization—0
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection—0
Transformer in Touch: A Survey—0
Transparency and Explanation in Deep Reinforcement Learning Neural Networks—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
TULIP: Towards Unified Language-Image Pretraining—0
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model—0
UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking—0
Understanding Adversarial Examples Through Deep Neural Network's Response Surface and Uncertainty Regions—0
Understanding Bayesian Rooms Using Composite 3D Object Models—0
Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges—0
Understanding Low- and High-Level Contributions to Fixation Prediction—0
Understanding Tools: Task-Oriented Object Modeling, Learning and Recognition—0
Unity of Opposites: SelfNorm and CrossNorm for Model Robustness—0
Universal adversarial perturbation for remote sensing images—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