Dense Object Detection
21 papers with code • 1 benchmarks • 3 datasets
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Sparse Generation: Making Pseudo Labels Sparse for weakly supervision with points
In recent years, research on point weakly supervised object detection (PWSOD) methods in the field of computer vision has attracted people's attention.
Transferring dense object detection models to event-based data
Event-based image representations are fundamentally different to traditional dense images.
Unitail: Detecting, Reading, and Matching in Retail Scene
To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene.
Mutual Supervision for Dense Object Detection
In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency.
Semi-supervised Learning for Dense Object Detection in Retail Scenes
We show that using unlabeled data with the noisy student training methodology, we can improve the state of the art on precise detection of objects in densely packed retail scenes.
Learning Gaussian Maps for Dense Object Detection
We show the gain of 6\% and 5\% in mAP with respect to baseline RetinaNet.
LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection
In this paper, we propose a new way to efficiently learn a single-shot detector which offers a very good compromise between these two objectives.
Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning
Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time.