Object
3296 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Object models and implementationsMost implemented papers
Frustum PointNets for 3D Object Detection from RGB-D Data
charlesq34/frustum-pointnets
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CVPR 2018
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
EfficientDet: Scalable and Efficient Object Detection
Model efficiency has become increasingly important in computer vision.
R-FCN: Object Detection via Region-based Fully Convolutional Networks
daijifeng001/r-fcn
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NeurIPS 2016
In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
Striving for Simplicity: The All Convolutional Net
pytorch/captum
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Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers.
End-to-End Object Detection with Transformers
facebookresearch/detr
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ECCV 2020
We present a new method that views object detection as a direct set prediction problem.
Spatial Memory for Context Reasoning in Object Detection
endernewton/tf-faster-rcnn
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ICCV 2017
On the other hand, modeling object-object relationships requires {\bf spatial} reasoning -- not only do we need a memory to store the spatial layout, but also a effective reasoning module to extract spatial patterns.
Microsoft COCO: Common Objects in Context
PaddlePaddle/PaddleDetection
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We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
ifzhang/FairMOT
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Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.
Fast R-CNN
rbgirshick/fast-rcnn
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ICCV 2015
Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks.
Point Transformer
Pointcept/Pointcept
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ICCV 2021
For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
