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 301–325 of 2042 papers

TitleStatusHype
Proactive Adversarial Defense: Harnessing Prompt Tuning in Vision-Language Models to Detect Unseen Backdoored Images—0
CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs—0
Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis—0
Can foundation models actively gather information in interactive environments to test hypotheses?—0
Optimized CNNs for Rapid 3D Point Cloud Object Recognition—0
LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language ModelsCode0
Textured As-Is BIM via GIS-informed Point Cloud Segmentation—0
Verbalized Representation Learning for Interpretable Few-Shot GeneralizationCode0
Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agentsCode0
NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?—0
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets—0
Fine-Grained Open-Vocabulary Object Recognition via User-Guided Segmentation—0
ViSTa Dataset: Do vision-language models understand sequential tasks?Code0
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media ContextsCode0
LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection—0
Multiscale Dubuc: A New Similarity Measure for Time SeriesCode0
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction—0
DipMe: Haptic Recognition of Granular Media for Tangible Interactive Applications—0
Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language ModelsCode0
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral StreamCode0
Object Recognition in Human Computer Interaction:- A Comparative Analysis—0
Lost in Context: The Influence of Context on Feature Attribution Methods for Object RecognitionCode0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
Learning Where to Edit Vision TransformersCode0
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural NetworksCode0
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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