Object Recognition
485 papers with code • 7 benchmarks • 42 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Object Recognition
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Libraries
Use these libraries to find Object Recognition models and implementationsDatasets
CIFAR-10
ImageNet
CORe50
N-Caltech 101
DVS128 Gesture
SUN Attribute
OCID
CIFAR10-DVS
Latest papers with no code
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private training data just by repeatedly querying the network and inspecting its outputs.
Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors
Unmanned Aerial Vehicles (UAVs) are gaining popularity in civil and military applications.
ViTCN: Vision Transformer Contrastive Network For Reasoning
Machine learning models have achieved significant milestones in various domains, for example, computer vision models have an exceptional result in object recognition, and in natural language processing, where Large Language Models (LLM) like GPT can start a conversation with human-like proficiency.
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning
Recognising the characteristics of objects while a robot handles them is crucial for adjusting motions that ensure stable and efficient interactions with containers.
Generalized Relevance Learning Grassmann Quantization
The proposed model returns a set of prototype subspaces and a relevance vector.
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning
The rapid proliferation of digital content and the ever-growing need for precise object recognition and segmentation have driven the advancement of cutting-edge techniques in the field of object classification and segmentation.
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition
Robots require a semantic understanding of their surroundings to operate in an efficient and explainable way in human environments.
Textureless Object Recognition: An Edge-based Approach
It has been challenging to obtain good accuracy in real time because of its lack of discriminative features and reflectance properties which makes the techniques for textured object recognition insufficient for textureless objects.
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation
It is based on a two-stream deep neural network model that factorizes spatial and temporal features to generate dynamic visual stimuli whose model layer activations are matched to those of input videos.
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition
In this paper, we present to incorporate the subtle local fine-grained feature learning into global self-supervised contrastive learning through a pure self-supervised global-local fine-grained contrastive learning framework.
