Dense Object Detection
21 papers with code • 1 benchmarks • 3 datasets
Libraries
Use these libraries to find Dense Object Detection models and implementations
DOTA
SKU110K
xView3-SAR
Most implemented papers
Mixture Dense Regression for Object Detection and Human Pose Estimation
alivaramesh/MixtureDenseRegression
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CVPR 2020
We realize the framework for object detection and human pose estimation.
Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection
We train a standard object detector on a small, normally packed dataset with data augmentation techniques.
Robust and Efficient Post-Processing for Video Object Detection (REPP)
AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection
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Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks.
SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images
sigma10010/nuclei_cells_det
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The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning.
Rethinking the Misalignment Problem in Dense Object Detection
On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results.
Prediction-Guided Distillation for Dense Object Detection
Based on this, we propose Prediction-Guided Distillation (PGD), which focuses distillation on these key predictive regions of the teacher and yields considerable gains in performance over many existing KD baselines.
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection
xudangliatiger/ape-loss
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CVPR 2022
However, a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed. In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples. Based on this observation, we propose two strategies to improve the AP loss.
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
PaddlePaddle/PaddleDetection
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CVPR 2023
It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
CrossKD: Cross-Head Knowledge Distillation for Object Detection
Moreover, as mimicking the teacher's predictions is the target of KD, CrossKD offers more task-oriented information in contrast with feature imitation.
Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection
tinytigerpan/bckd
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ICCV 2023
Thus, the optimum of the distillation loss does not necessarily lead to the optimal student classification scores for dense object detectors.
