https://github.com/DequanWang/CoDeNet
공식 코드CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
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Novel Object Detection is a challenging task introduced by Fomenko et.al. in their paper "Learning to Discover and Detect Objects". The goal in this task is to measure mAP performance on known as well as novel classes, where the known classes correspond to the 80 COCO classes, and the novel classes are the remaining 1123 classes from LVIS dataset. Thus, during training the model can only be trained with annotations from COCO dataset, but during evaluation/inference it is expected to BOTH classify and detect objects belonging to ALL the classes in the LVIS dataset.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox Detector
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Mamba YOLO: A Simple Baseline for Object Detection with State Space Model
SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite Imagery
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Knowledge Distillation in YOLOX-ViT for Side-Scan Sonar Object Detection
Enhancing Novel Object Detection via Cooperative Foundational Models
Automatic Signboard Detection and Localization in Densely Populated Developing Cities
Scaling Novel Object Detection with Weakly Supervised Detection Transformers
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection
Mamba YOLO: A Simple Baseline for Object Detection with State Space Model
Learning to Detect and Retrieve Objects from Unlabeled Videos
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
DesCo: Learning Object Recognition with Rich Language Descriptions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.