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 1051–1100 of 2042 papers

TitleStatusHype
Applications of knowledge graphs for food science and industry—0
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition—0
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation—0
Towards ontology driven learning of visual concept detectors—0
Towards Open World Recognition—0
Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors—0
Towards real-time object recognition and pose estimation in point clouds—0
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation—0
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition—0
Towards Zero-Shot & Explainable Video Description by Reasoning over Graphs of Events in Space and Time—0
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition—0
TrackVLA: Embodied Visual Tracking in the Wild—0
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
Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition—0
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons—0
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces—0
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching—0
Unsupervised Domain Adaptation using Graph Transduction Games—0
Unsupervised feature learning by augmenting single images—0
Unsupervised Feature Learning by Deep Sparse Coding—0
Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation—0
Unsupervised Feature Learning with C-SVDDNet—0
Unsupervised Foveal Vision Neural Networks with Top-Down Attention—0
Unsupervised Generation of a Viewpoint Annotated Car Dataset From Videos—0
Unsupervised Learning of Invariant Representations in Hierarchical Architectures—0
Unsupervised Learning using Pretrained CNN and Associative Memory Bank—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