Object Recognition
484 papers with code • 7 benchmarks • 39 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-CARS
SUN Attribute
OCID
Washington RGB-D
MECCANO
N-ImageNet
Latest papers with no code
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured
Based on various types of non-learnable operators, including gradient, sort, local binary pattern, maximum, etc., this paper designs a set of new convolution operations that are natually invariant to arbitrary rotations.
How to deal with glare for improved perception of Autonomous Vehicles
In this paper, we investigate various glare reduction techniques, including the proposed saturated pixel-aware glare reduction technique for improved performance of the computer vision (CV) tasks employed by the perception layer of AVs.
A Diffusion-based Data Generator for Training Object Recognition Models in Ultra-Range Distance
A challenging example is Ultra-Range Gesture Recognition (URGR) in human-robot interaction where the user exhibits directive gestures at a distance of up to 25~m from the robot.
Learning State-Invariant Representations of Objects from Image Collections with State, Pose, and Viewpoint Changes
We believe that this dataset will facilitate research in fine-grained object recognition and retrieval of objects that are capable of state changes.
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning
Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity.
Object-conditioned Bag of Instances for Few-Shot Personalized Instance Recognition
Nowadays, users demand for increased personalization of vision systems to localize and identify personal instances of objects (e. g., my dog rather than dog) from a few-shot dataset only.
SUGAR: Pre-training 3D Visual Representations for Robotics
SUGAR employs a versatile transformer-based model to jointly address five pre-training tasks, namely cross-modal knowledge distillation for semantic learning, masked point modeling to understand geometry structures, grasping pose synthesis for object affordance, 3D instance segmentation and referring expression grounding to analyze cluttered scenes.
Efficient Multi-Band Temporal Video Filter for Reducing Human-Robot Interaction
Although mobile robots have on-board sensors to perform navigation, their efficiency in completing paths can be enhanced by planning to avoid human interaction.
PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation
We frame this problem as the task of learning a low-dimensional visual-tactile embedding, wherein we encode a depth patch from which we decode the tactile signal.
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition
To this end, we propose a novel framework, dubbed EventDance for this unsupervised source-free cross-modal adaptation problem.
