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Dense Object Detection

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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection

Dense Object Detection Based on De-homogenized Queries

Hybrid Classification-Regression Adaptive Loss for Dense Object Detection

Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data Volume

Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement

Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection

CrossKD: Cross-Head Knowledge Distillation for Object Detection

Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

Transferring dense object detection models to event-based data

Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection

Unitail: Detecting, Reading, and Matching in Retail Scene

PP-YOLOE: An evolved version of YOLO

Prediction-Guided Distillation for Dense Object Detection

Mutual Supervision for Dense Object Detection

Rethinking the Misalignment Problem in Dense Object Detection

Semi-supervised Learning for Dense Object Detection in Retail Scenes

SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

Localization Distillation for Dense Object Detection

Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

Robust and Efficient Post-Processing for Video Object Detection (REPP)

A Solution to Product detection in Densely Packed Scenes

BorderDet: Border Feature for Dense Object Detection

AutoAssign: Differentiable Label Assignment for Dense Object Detection

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

Learning Gaussian Maps for Dense Object Detection

Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection

Mixture Dense Regression for Object Detection and Human Pose Estimation

Soft Anchor-Point Object Detection

LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection

Precise Detection in Densely Packed Scenes

Focal Loss for Dense Object Detection

Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.