Object Detection
3704 papers with code • 91 benchmarks • 257 datasets
Object Detection is a computer vision task in which the goal is to detect and locate objects of interest in an image or video. The task involves identifying the position and boundaries of objects in an image, and classifying the objects into different categories. It forms a crucial part of vision recognition, alongside image classification and retrieval.
The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods:
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One-stage methods prioritize inference speed, and example models include YOLO, SSD and RetinaNet.
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Two-stage methods prioritize detection accuracy, and example models include Faster R-CNN, Mask R-CNN and Cascade R-CNN.
The most popular benchmark is the MSCOCO dataset. Models are typically evaluated according to a Mean Average Precision metric.
( Image credit: Detectron )
Benchmarks
These leaderboards are used to track progress in Object Detection
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Libraries
Use these libraries to find Object Detection models and implementationsDatasets
MS COCO
KITTI
nuScenes
Visual Genome
LVIS
SUN RGB-D
Waymo Open Dataset
BDD100K
MVTecAD
Manga109
Subtasks
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3D Object Detection
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Real-Time Object Detection
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RGB Salient Object Detection
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Few-Shot Object Detection
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Few-Shot Object Detection
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Video Object Detection
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RGB-D Salient Object Detection
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Open Vocabulary Object Detection
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Object Detection In Aerial Images
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Weakly Supervised Object Detection
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Small Object Detection
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Robust Object Detection
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Medical Object Detection
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Zero-Shot Object Detection
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Open World Object Detection
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Co-Salient Object Detection
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Dense Object Detection
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Object Proposal Generation
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Video Salient Object Detection
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Camouflaged Object Segmentation
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License Plate Detection
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Head Detection
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Multiview Detection
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3D Object Detection From Monocular Images
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One-Shot Object Detection
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Moving Object Detection
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Surgical tool detection
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Described Object Detection
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Body Detection
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Pupil Detection
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Object Detection In Indoor Scenes
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Class-agnostic Object Detection
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Semantic Part Detection
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Object Skeleton Detection
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Fish Detection
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Multiple Affordance Detection
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Weakly Supervised 3D Detection
Latest papers with no code
Watch Your Step: Optimal Retrieval for Continual Learning at Scale
One of the most widely used approaches in continual learning is referred to as replay.
Camera clustering for scalable stream-based active distillation
We present a scalable framework designed to craft efficient lightweight models for video object detection utilizing self-training and knowledge distillation techniques.
OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery
Our method also excels in low-data settings, outperforming supervised baselines using a fraction of the training data.
VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection
Therefore, an effective solution involves transforming monocular images into LiDAR-like representations and employing a LiDAR-based 3D object detector to predict the 3D coordinates of objects.
Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stres
The novelty lies in the synergistic combination of a pretrained network with carefully designed custom layers.
Fusion-Mamba for Cross-modality Object Detection
In this paper, we investigate cross-modality fusion by associating cross-modal features in a hidden state space based on an improved Mamba with a gating mechanism.
TEXT2TASTE: A Versatile Egocentric Vision System for Intelligent Reading Assistance Using Large Language Model
The LLM processes the data and allows the user to interact with the text and responds to a given query, thus extending the functionality of corrective lenses with the ability to find and summarize knowledge from the text.
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection
This is followed by a fine-tuning stage that leverages a small number of high-resolution samples to further enhance detection performance.
Coreset Selection for Object Detection
Coreset selection is a method for selecting a small, representative subset of an entire dataset.
BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection
When training the enhancement branch, the object detection subnet in the enhancement branch guides the image enhancement subnet to be optimized towards the direction that is most conducive to the detection task.
