Papers Dense Object Detection
“Dense Object Detection” 태그가 달린 논문 37편 · 필터 해제
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+1Dense Object Detection Based on De-homogenized Queries
Dense object detection is widely used in automatic driving, video surveillance, and other fields. This paper focuses on the challenging task of dense object detection. Currently, detection methods based on greedy algorit…
Dense Object DetectionObjectobject-detectionObject DetectionHybrid Classification-Regression Adaptive Loss for Dense Object Detection
For object detection detectors, enhancing model performance hinges on the ability to simultaneously consider inconsistencies across tasks and focus on difficult-to-train samples. Achieving this necessitates incorporating…
ClassificationDense Object Detectionobject-detectionObject Detection+1Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data Volume
Existing pseudo label generation methods for point weakly supervised object detection are inadequate in low data volume and dense object detection tasks. We consider the generation of weakly supervised pseudo labels as t…
Dense Object Detectionobject-detectionObject DetectionPseudo Label+1Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement
DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-att…
2D Object DetectionComputational EfficiencyDense Object DetectionObject DetectionBridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection
Knowledge distillation (KD) has shown potential for learning compact models in dense object detection. However, the commonly used softmax-based distillation ignores the absolute classification scores for individual categ…
Binary ClassificationClassificationDense Object DetectionKnowledge Distillation+3CrossKD: Cross-Head Knowledge Distillation for Object Detection
Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imit…
Dense Object DetectionKnowledge DistillationModel CompressionObject+2Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguiti…
Dense Object DetectionObjectobject-detectionObject Detection+2Transferring dense object detection models to event-based data
Event-based image representations are fundamentally different to traditional dense images. This poses a challenge to apply current state-of-the-art models for object detection as they are designed for dense images. In th…
Dense Object DetectionObjectobject-detectionObject DetectionRevisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection
Average precision (AP) loss has recently shown promising performance on the dense object detection task. However,a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet be…
Dense Object Detectionobject-detectionObject DetectionUnitail: 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. Pursuing this goal, we introduce the United Retail Datasets (Unitail…
BenchmarkingDense Object DetectionOne-stage Anchor-free Oriented Object DetectionOptical Character Recognition (OCR)PP-YOLOE: An evolved version of YOLO
In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment. We optimize on the basis of the previous PP-YOLOv2, using anchor-free paradigm, more powe…
2D Object DetectionDense Object DetectionMulti-Object TrackingMultiple Object Tracking+3Prediction-Guided Distillation for Dense Object Detection
Real-world object detection models should be cheap and accurate. Knowledge distillation (KD) can boost the accuracy of a small, cheap detection model by leveraging useful information from a larger teacher model. However,…
Dense Object DetectionKnowledge DistillationObjectobject-detection+2Mutual Supervision for Dense Object Detection
The classification and regression head are both indispensable components to build up a dense object detector, which are usually supervised by the same training samples and thus expected to have consistency with each othe…
ClassificationDense Object DetectionObjectobject-detection+2Rethinking the Misalignment Problem in Dense Object Detection
Object detection aims to localize and classify the objects in a given image, and these two tasks are sensitive to different object regions. Therefore, some locations predict high-quality bounding boxes but low classifica…
Dense Object DetectionObjectobject-detectionObject Detection+1