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This section provides an overview of materials I have developed since 2014, initially as a graduate student and now as a researcher in South Korea. These resources are built upon the referenced papers and are shared online to support students and researchers in grasping core concepts and key fundamentals of influential work in deep learning and computer vision. Topics covered include object detection, image classification, instance and semantic segmentation, open-set recognition, reinforcement learning, activity recognition, generative adversarial networks, and more.
Please remember to cite the corresponding references when using this material.
For comments or questions, feel free to contact me at afuentes@jbnu.ac.kr.
More materials will be added soon...
2025
2025
20250110_DETRS_YOLO.pdfDETRs Beat YOLOs on Real-time Object Detection
DETRs Beat YOLOs on Real-time Object Detection
Transformers in Object Detection
2024
2024
20241108_Contrastive-Localized-language-Image.pdfContrastive Localized
Language-Image Pre-Training
Contrastive Localized
Language-Image Pre-Training
Foundation Models
20240906_Paper seminar-Large vision models.pdfWhen Do We Not Need Larger Vision Models?
When Do We Not Need Larger Vision Models?
Foundation Models
20240710_BioClip.pdfBIOCLIP: A Vision Foundation Model for the Tree of Life
BIOCLIP: A Vision Foundation Model for the Tree of Life
Foundation Models
20240405_General Object Foundation.pdfGeneral Object Foundation Model for Images and Videos at Scale
General Object Foundation Model for Images and Videos at Scale
Foundation Models
20240105_YOLOv7.pdfYOLOv7: Trainable bag-of-freebies sets new state-of-theart for real-time object detectors
YOLOv7: Trainable bag-of-freebies sets new state-of-theart for real-time object detectors
Object Detection
2023
2023
20231012_ContinualLearning.pdfVisual Perception via Learning in An Open World
Visual Perception via Learning in An Open World
Continual Learning
20230727_OneFormer.pdfOneFormer: One Transformer to Rule Universal Image Segmentation
OneFormer: One Transformer to Rule Universal Image Segmentation
Large Scale Vision Models - Instance Segmentation
20230519_InternImage.pdfInternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Large Scale Vision Models
20230317_Symbolic Discovery of Optimization Algoritms.pdfAuto Machine Learning
Auto Machine Learning
Learning to Learn
2022
2022
20221229_Detecting Twenty-tousand classes using image-level supervision.pdfDetecting twenty-thousand classes using image-level supervision
Detecting twenty-thousand classes using image-level supervision
Object detection
20220923_Continual Test-Time Domain Adaptation.pdfContinual Test-Time Domain Adaptation
Continual Test-Time Domain Adaptation
Domain Adaptation
20220708_Towards open world object detection.pdfTowards Open World Object Detection
Towards Open World Object Detection
Open World Object Detection
20220512_CoCa.pdfCoCa: Contrastive Captioners are Image-Text Foundation Models
CoCa: Contrastive Captioners are Image-Text Foundation Models
Foundation Model
20220401_You look only one representation.pdfYou Only Learn One Representation: Unified Network for Multiple Tasks
You Only Learn One Representation: Unified Network for Multiple Tasks
Multi-task learning
20220211_FlorenceFM.pdfFlorence: A New Foundation Model for Computer Vision
Florence: A New Foundation Model for Computer Vision
Foundation Models
2021
2021
20211119_Dynamic-head.pdfDynamic Head: Unifying Object Detection Heads with Attentions
Dynamic Head: Unifying Object Detection Heads with Attentions
Object detection
20210917_End-to-end semi-supervised object detection.pdfEnd-to-End Semi-Supervised Object Detection with Soft Teacher
End-to-End Semi-Supervised Object Detection with Soft Teacher
Object detection
20210715_An Image is worth 16_16 Words_Tranformers for Image Recognition at Scale.pdfVision Transformers: An Image is worth 16x16 Words: Transformers for Image Recognition at Scale
Vision Transformers: An Image is worth 16x16 Words: Transformers for Image Recognition at Scale
Image classification
20210514_Transformers.pdfTransformers: Attention is All You Need
Transformers: Attention is All You Need
Natural Language Processing
20210326_YOLOv4.pdfYOLO v4: Optimal Speed and Accuracy of Object Detection
YOLO v4: Optimal Speed and Accuracy of Object Detection
Object detection
20210205_UniversalDomainAdaptation.pdfUniversal Domain Adaptation
Universal Domain Adaptation
Open set domain adaptation
20210108_Large-Scale Object Detection.pdfLarge-Scale Object Detection in the Wild for Imbalanced Multi-Labels
Large-Scale Object Detection in the Wild for Imbalanced Multi-Labels
Object detection
2020
2020
20201120_Rethinking Pre-training.pdfRethinking Pre-training and Self-training
Rethinking Pre-training and Self-training
Image classification
20200811_MultipleObjg_Tracking.pdfMultiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
Object tracking
20200811_CSPNet.pdfCSPNet: A new backbone that can enhance learning capabilities of CNN
CSPNet: A new backbone that can enhance learning capabilities of CNN
Feature extractor
20200717_Transformers.pdfAttention is all you need
Attention is all you need
Spatio-temporal model (Fundament of Transformers)
20200521_ActorCondAttention.pdfActor Conditioned Attention Maps for Video Action Detection
Actor Conditioned Attention Maps for Video Action Detection
Video action detection
20200417_FixNet.pdfFixing the train-test resolution discrepancy
Fixing the train-test resolution discrepancy
FixEfficientNet
20200225_CBNet.pdfCBNet: A Novel Composite Backbone Network Architecture for Object Detection
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
CBNet (Object detection)
20200121_Class_Balanced.pdfClass-Balanced Loss Based on Effective Number of Samples
Class-Balanced Loss Based on Effective Number of Samples
Class imbalance
2019
2019
20191207_EfficnetNet.pdfEfficientDet: Scalable and Efficient Object Detection
EfficientDet: Scalable and Efficient Object Detection
EfficientDet (Object detection)
20191026_Bottom-up Object Detection.pdfBottom-up Object Detection by Grouping Extreme and Center Points
Bottom-up Object Detection by Grouping Extreme and Center Points
CornerNet
20190809_OpenSet.pdfC2AE: Class Conditioned Auto-Encoder for Open-set Recognition
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
Open-set recognition
20190723_Cascade_RCNN.pdfCascade R-CNN: High Quality Object Detection and Instance Segmentation
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
Cascade R-CNN (Object detection and instance segmentation)
20190712_Libra_RCNN.pdfLibra R-CNN: Towards Balanced Learning for Object Detection
Libra R-CNN: Towards Balanced Learning for Object Detection
Object detection
20190615_Hierarchical.pdfHierarchical Relational Networks for Group Activity Recognition and Retrieval
Hierarchical Relational Networks for Group Activity Recognition and Retrieval
Activity recognition
20190518_TridentNet.pdfScale-Aware Trident Networks for Object Detection
Scale-Aware Trident Networks for Object Detection
TridentNet (Object detection)
20190413_Sniper.pdfSNIPER: Efficient Multi-Scale Training
SNIPER: Efficient Multi-Scale Training
SniperNet (Object detection)
2090316_FeatureSelectiveAnchor.pdfFeature Selective Anchor-Free Module for Single-Shot Object Detection
Feature Selective Anchor-Free Module for Single-Shot Object Detection
Object detection
20190215_ReinforcementLearning.pdfAn Introduction to Deep Reinforcement Learning
An Introduction to Deep Reinforcement Learning
Reinforcement Learning
20190108_DeformableCNN.pdfDeformable Convolutional Networks
Deformable Convolutional Networks
Optimization of feature extractors
2018
2018
20181208_PELEENet.pdfPelee: A Real-Time Object Detection System on Mobile Devices
Pelee: A Real-Time Object Detection System on Mobile Devices
PeleeNet (Object detection)
20181110_ActivityRecognition.pdfA Review on Activity Recognition
A Review on Activity Recognition
Action recognition
20181010_MegDet.pdfMegDet: A large Mini-Batch Object Detector
MegDet: A large Mini-Batch Object Detector
Object detection
20180908_AttnGAN.pdfAttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN (Generative Adversarial Networks)
From Text-to-Image
20180814_Feature_Gen_Nets.pdfFeature Generating Networks for Zero-Shot Learning
Feature Generating Networks for Zero-Shot Learning
Zero-Shot Learning
20180724_MobileNetV2.pdfMobileNetV2: Inverted Residuals and Linear Bottlenecks
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Object detection
20180709_Taskonomy.pdfTaskonomy: Disentangling Task Transfer Learning
Taskonomy: Disentangling Task Transfer Learning
Transfer Learning
20180622_Analyzing_Filters_Toward_Efficient_ConvNet.pdfAnalyzing Filters Toward Efficient ConvNet
Analyzing Filters Toward Efficient ConvNet
Understanding ConvNets
20180421_NASNet.pdfLearning Transferable Architectures for Scalable Image Recognition
Learning Transferable Architectures for Scalable Image Recognition
NASNet (Image Recognition)
20180309_FractalNet.pdfFractalNet: Ultra-Deep Neural Networks Without Residuals
FractalNet: Ultra-Deep Neural Networks Without Residuals
FractalNet (Image Recognition)
20180123_SSD.pdfSingle-Shot Refinement Neural Network for Object Detection
Single-Shot Refinement Neural Network for Object Detection
SSR (Object detection)
20180105_FocalLoss.pdfFocal Loss for Dense Object Detection
Focal Loss for Dense Object Detection
RetinaNet (Object detection)
2017
2017
20171121_Understanding Deep Learning.pdfUnderstanding Deep Learning Requires Re-thinking Generalization
Understanding Deep Learning Requires Re-thinking Generalization
Generalization
20170818_ Residual Attention.pdfResidual Attention Network for Image Classification
Residual Attention Network for Image Classification
Image classification
20170801_DenseNet.pdfDensely Connected Convolutional Networks
Densely Connected Convolutional Networks
DenseNet
20170709_MobileNets.pdfMobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Image classification and object detection
20170527_Human-Object.pdfDetecting and Recognizing Human-Objects Interactions
Detecting and Recognizing Human-Objects Interactions
Human-Object Interaction
20170318_YOLO9000.pdfYOLO9000: Better, Faster, Stronger
YOLO9000: Better, Faster, Stronger
Object detection
20170216_Unsupervised GANet.pdfUnsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Fundament of GAN
20160119_SpeedAccuracy.pdfSpeed/accuracy trade-offs for modern convolutional object detectors
Speed/accuracy trade-offs for modern convolutional object detectors
Tradeoff between accuracy and speed of deep nets for object detection
2016
2016
20161224_SSD.pdfSSD: Single Shot MultiBox Detector
SSD: Single Shot MultiBox Detector
SSD (Object detection)
20161119_ProNet2.pdfProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks
ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks
Object detection
20161022_Learning_deep features.pdfLearning Deep Features for Discriminative Localization
Learning Deep Features for Discriminative Localization
Object localization
20160902_Identiy mapping.pdfIdentity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Identity in residual networks
20160720_T-CNN.pdfT-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Object detection in videos
20160423_Faster R-CNN.pdfFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Object detection
20160305_PedestrianDetectionAidedByDeepLearningSemanticTasks.pdfPedestrian Detection aided by Deep Learning Semantic Tasks
Pedestrian Detection aided by Deep Learning Semantic Tasks
Pedestrian detection
20160115_RecurrentCNN.pdfRecurrent Convolutional Neural Networks for Objects Recognition
Recurrent Convolutional Neural Networks for Objects Recognition
RCNN (Object detection)
20150904_FLowNet.pdfFlowNet: Learning Optical Flow with Convolutional Networks
FlowNet: Learning Optical Flow with Convolutional Networks
Optical flow
2015
2015
Traditional hand-crafted-based methods for pedestrian detection, motion estimation, optical flow.
Traditional hand-crafted-based methods for pedestrian detection, motion estimation, optical flow.
20151107_Stereo Vision CNN.pdfComputing the Stereo Matching Cost with a Convolutional Neural Network
Computing the Stereo Matching Cost with a Convolutional Neural Network
Stereo vision CNN
20150805_Dense Stereo for Pedestrian Detection.pdfThe Benefits of Dense Stereo for Pedestrian Detection
The Benefits of Dense Stereo for Pedestrian Detection
Dense stereo vision
20150713_Moving objects detection and Credal.pdfMoving Objects Detection and Credal Boosting Based Recognition in Urban Environmets
Moving Objects Detection and Credal Boosting Based Recognition in Urban Environmets
Moving objects detection
20150625_Will the pedestrian cross .pdfWill the Pedestrian Cross? A study on Pedestrian Path Prediction
Will the Pedestrian Cross? A study on Pedestrian Path Prediction
Pedestrian detection
20150506_Unsupervised flow-based motion segmentation.pdfUnsupervised flow-based motion analysis for an autonomous moving system
Unsupervised flow-based motion analysis for an autonomous moving system
Motion analysis
20150418_Locating objects in car-driving sequences.pdfLocating moving objects in car-driving sequences
Locating moving objects in car-driving sequences
Moving objects detection
20150403_Stixel motion estimation.pdfStixels motion estimation without optical flow computation
Stixels motion estimation without optical flow computation
Motion estimation
20150207_Probabilistic models.pdfPedestrian detection from traffic scenes based on probabilistic models of the contour fragments
Pedestrian detection from traffic scenes based on probabilistic models of the contour fragments
Pedestrian detection
20152001_3D Optical flow.pdfFeature- and Depth-Supported Modified Total Variation Optical Flow for 3D Motion Field Estimation in Real Scenes
Feature- and Depth-Supported Modified Total Variation Optical Flow for 3D Motion Field Estimation in Real Scenes
Optical flow
2014
2014
Traditional hand-crafted-based methods for pedestrian detection, motion estimation, optical flow.
Traditional hand-crafted-based methods for pedestrian detection, motion estimation, optical flow.
20141101_Moving Pedestrian Detection Based on Motion Segmentation.pdfMoving Pedestrian Detection Based on Motion Segmentation
Moving Pedestrian Detection Based on Motion Segmentation
Motion segmentation
20141128_Motion segmentation.pdfMotion Segmentation Using Optical Flow for Pedestrian Detection from Moving Vehicle
Motion Segmentation Using Optical Flow for Pedestrian Detection from Moving Vehicle
Pedestrian detection
20140927_PedestrianDetection.pdfTowards a Real-Time Pedestrian Detection based on a deformable template model
Towards a Real-Time Pedestrian Detection based on a deformable template model
Pedestrian detection
20140814_New features pedestrian.pdfNew features and insights for pedestrian detection
New features and insights for pedestrian detection
Pedestrian detections
20140707_Pedestrian Detection.pdfPedestrian Detection: An Evaluation of the State of the Art
Pedestrian Detection: An Evaluation of the State of the Art
Pedestrian detection
20140505_Study Relationships HG and CV.pdfStudying Relationships Between Human Gaze, Description, and Computer Vision
Studying Relationships Between Human Gaze, Description, and Computer Vision
Human and computer vision
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