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Object Detection

491 papers with code · Computer Vision

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

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Latest papers without code

WHAT DATA IS USEFUL FOR MY DATA: TRANSFER LEARNING WITH A MIXTURE OF SELF-SUPERVISED EXPERTS

ICLR 2020

We assume that a client, a target application with its own small labeled dataset, is only interested in fetching a subset of the server’s data that is most relevant to its own target domain.

IMAGE CLASSIFICATION INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION TRANSFER LEARNING

Geometry-Aware Visual Predictive Models of Intuitive Physics

ICLR 2020

We propose neural architectures that learn to disentangle an RGB-D video steam into camera motion and 3D scene appearance, and capture the latter into 3D feature representations that can be trained end-to-end with 3D object detection and object motion forecasting.

3D OBJECT DETECTION MOTION FORECASTING

Mixture Density Networks Find Viewpoint the Dominant Factor for Accurate Spatial Offset Regression

ICLR 2020

Offset regression is a standard method for spatial localization in many vision tasks, including human pose estimation, object detection, and instance segmentation.

INSTANCE SEGMENTATION OBJECT DETECTION POSE ESTIMATION REGRESSION SEMANTIC SEGMENTATION

Meta-RCNN: Meta Learning for Few-Shot Object Detection

ICLR 2020

Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data.

FEW-SHOT OBJECT DETECTION META-LEARNING OBJECT CLASSIFICATION

Data-Efficient Image Recognition with Contrastive Predictive Coding

ICLR 2020

Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge.

OBJECT DETECTION

Self-supervised Training of Proposal-based Segmentation via Background Prediction

ICLR 2020

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on.

HUMAN DETECTION OBJECT DETECTION SEMANTIC SEGMENTATION

Compositional Embeddings: Joint Perception and Comparison of Class Label Sets

ICLR 2020

We explore the idea of compositional set embeddings that can be used to infer not just a single class, but the set of classes associated with the input data (e. g., image, video, audio signal).

OBJECT DETECTION OMNIGLOT ONE-SHOT LEARNING SPEAKER DIARIZATION

Recurrent Layer Attention Network

ICLR 2020

Capturing long-range feature relations has been a central issue on convolutional neural networks(CNNs).

IMAGE CLASSIFICATION OBJECT DETECTION

Fast Neural Network Adaptation via Parameters Remapping

ICLR 2020

Some recent neural architecture search (NAS) methods search for the backbone of seg/det networks.

NEURAL ARCHITECTURE SEARCH OBJECT DETECTION SEMANTIC SEGMENTATION