Dense Object Detection
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Benchmarks
SKU-110K
Most implemented
Focal Loss for Dense Object Detection
PP-YOLOE: An evolved version of YOLO
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
Precise Detection in Densely Packed Scenes
Papers
Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Pathology foundation models (FMs) are models trained on vast amounts of typically unlabeled data and have been shown to yield regularized latent spaces that can be used effectively in downstream classification tasks. Thi…
Dense Object DetectionA Probabilistic Framework for Improving Dense Object Detection in Underwater Image Data via Annealing-Based Data Augmentation
Object detection models typically perform well on images captured in controlled environments with stable lighting, water clarity, and viewpoint, but their performance degrades substantially in real-world underwater setti…
Dense Object DetectionData AugmentationRFAssigner: A Generic Label Assignment Strategy for Dense Object Detection
Label assignment is a critical component in training dense object detectors. State-of-the-art methods typically assign each training sample a positive and a negative weight, optimizing the assignment scheme during traini…
Dense Object DetectionAdapting SAM with Dynamic Similarity Graphs for Few-Shot Parameter-Efficient Small Dense Object Detection: A Case Study of Chickpea Pods in Field Conditions
Parameter-Efficient Fine-Tuning (PEFT) of foundation models for agricultural computer vision tasks remains challenging due to limited training data and complex field conditions. This study introduces a Dynamic Similarity…
parameter-efficient fine-tuningDense Object DetectionInstance SegmentationGroup Evidence Matters: Tiling-based Semantic Gating for Dense Object Detection
Dense small objects in UAV imagery are often missed due to long-range viewpoints, occlusion, and clutter[cite: 5]. This paper presents a detector-agnostic post-processing framework that converts overlap-induced redundanc…
Dense Object DetectionVoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection
3D semantic occupancy prediction aims to reconstruct the 3D geometry and semantics of the surrounding environment. With dense voxel labels, prior works typically formulate it as a dense segmentation task, independently c…
3D geometry3D Semantic Occupancy PredictionDense Object Detectionobject-detection+1