Collection of papers, datasets, code and other resources for object detection and tracking using deep learning
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I use DavidRM Journal for managing my research data for its excellent hierarchical organization, cross-linking and tagging capabilities.
I make available a Journal entry export file that contains tagged and categorized collection of papers, articles, tutorials, code and notes about computer vision and deep learning that I have collected over the last few years.
This is what the topic cloud looks like:
It needs Jounal 8 and can be imported using following steps:
- Import my user preferences using File -> Import -> Import User Preferences
- Import research data using File -> Import -> Sync from The Journal Export File
Note that my user preferences must be imported before the research data for the tagged topics to work correctly.
(optional) My global options file is also provided for those interested in a dark theme and can be imported using File -> Import -> Import Global Options
Updated: 2023-11-22
- Scalable Object Detection Using Deep Neural Networks [cvpr14] [pdf] [notes]
- Selective Search for Object Recognition [ijcv2013] [pdf] [notes]
- Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks [tpami17] [pdf] [notes]
- RFCN - Object Detection via Region-based Fully Convolutional Networks [nips16] [Microsoft Research] [pdf] [notes]
- Mask R-CNN [iccv17] [Facebook AI Research] [pdf] [notes] [arxiv] [code (keras)] [code (tensorflow)]
- SNIPER Efficient Multi-Scale Training [ax1812/nips18] [pdf] [notes] [code]
- You Only Look Once Unified, Real-Time Object Detection [ax1605] [pdf] [notes]
- YOLO9000 Better, Faster, Stronger [ax1612] [pdf] [notes]
- YOLOv3 An Incremental Improvement [ax1804] [pdf] [notes]
- YOLOv4 Optimal Speed and Accuracy of Object Detection [ax2004] [pdf] [notes] [code]
- SSD Single Shot MultiBox Detector [ax1612/eccv16] [pdf] [notes]
- DSSD Deconvolutional Single Shot Detector [ax1701] [pdf] [notes]
- Feature Pyramid Networks for Object Detection [ax1704] [pdf] [notes]
- Focal Loss for Dense Object Detection [ax180207/iccv17] [pdf] [notes]
- FoveaBox: Beyond Anchor-based Object Detector [ax1904] [pdf] [notes] [code]
- CornerNet: Detecting Objects as Paired Keypoints [ax1903/ijcv19] [pdf] [notes] [code]
- FCOS Fully Convolutional One-Stage Object Detection [ax1908/iccv19] [pdf] [notes] [code] [code/FCOS_PLUS] [code/VoVNet] [code/HRNet] [code/NAS]
- Feature Selective Anchor-Free Module for Single-Shot Object Detection [ax1903/cvpr19] [pdf] [notes] [code]
- Bottom-up object detection by grouping extreme and center points [ax1901] [pdf] [notes] [code]
- Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection [ax1912/cvpr20] [pdf] [notes] [code]
- End-to-end object detection with Transformers [ax200528] [pdf] [notes] [code]
- Objects as Points [ax1904] [pdf] [notes] [code]
- RepPoints Point Set Representation for Object Detection [iccv19] [pdf] [notes] [code]
- OverFeat Integrated Recognition, Localization and Detection using Convolutional Networks [ax1402/iclr14] [pdf] [notes]
- LSDA Large scale detection through adaptation [ax1411/nips14] [pdf] [notes]
- Acquisition of Localization Confidence for Accurate Object Detection [ax1807/eccv18] [pdf] [notes] [code]
- EfficientDet: Scalable and Efficient Object Detection [cvpr20] [pdf]
- Generalized Intersection over Union A Metric and A Loss for Bounding Box Regression [ax1902/cvpr19] [pdf] [notes] [code] [project]
- Object Detection from Video Tubelets with Convolutional Neural Networks [cvpr16] [pdf] [notes]
- Object Detection in Videos with Tubelet Proposal Networks [ax1704/cvpr17] [pdf] [notes]
- Deep Feature Flow for Video Recognition [cvpr17] [Microsoft Research] [pdf] [arxiv] [code]
- Flow-Guided Feature Aggregation for Video Object Detection [ax1708/iccv17] [pdf] [notes]
- Towards High Performance Video Object Detection [ax1711] [Microsoft] [pdf] [notes]
- Online Video Object Detection using Association LSTM [iccv17] [pdf] [notes]
- Context Matters Refining Object Detection in Video with Recurrent Neural Networks [bmvc16] [pdf] [notes]
-
MOTS Multi-Object Tracking and Segmentation [cvpr19] [pdf] [notes] [code] [project/data]
-
Towards Real-Time Multi-Object Tracking [ax1909] [pdf] [notes]
-
A Simple Baseline for Multi-Object Tracking [ax2004] [pdf] [notes] [code]
-
Integrated Object Detection and Tracking with Tracklet-Conditioned Detection [ax1811] [pdf] [notes]
-
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism [ax1708/iccv17] [pdf] [arxiv] [notes]
-
Online multi-object tracking with dual matching attention networks [ax1902/eccv18] [pdf] [arxiv] [notes] [code]
-
FAMNet Joint Learning of Feature, Affinity and Multi-Dimensional Assignment for Online Multiple Object Tracking [iccv19] [pdf] [notes]
-
Exploit the Connectivity: Multi-Object Tracking with TrackletNet [ax1811/mm19] [pdf] [notes]
-
Tracking without bells and whistles [ax1903/iccv19] [pdf] [notes] [code] [pytorch]
- Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies [ax1704/iccv17] [Stanford] [pdf] [notes] [arxiv] [project],
- Multi-object Tracking with Neural Gating Using Bilinear LSTM [eccv18] [pdf] [notes]
- Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking [cvpr19] [pdf] [notes] [code]
- Unsupervised Person Re-identification by Deep Learning Tracklet Association [ax1809/eccv18] [pdf] [notes]
- Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers [ax1809/cvpr19] [pdf] [arxiv] [notes] [code]
- Simple Unsupervised Multi-Object Tracking [ax2006] [pdf] [notes]
- Learning to Track: Online Multi-object Tracking by Decision Making [iccv15] [Stanford] [pdf] [notes] [code (matlab)] [project]
- Collaborative Deep Reinforcement Learning for Multi-Object Tracking [eccv18] [pdf] [notes]
- Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor [iccv15] [NEC Labs] [pdf] [author] [notes]
- Deep Network Flow for Multi-Object Tracking [cvpr17] [NEC Labs] [pdf] [supplementary] [notes]
- Learning a Neural Solver for Multiple Object Tracking [ax1912/cvpr20] [pdf] [notes] [code]
- A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects [ax1607] [highest MT on MOT2015] [University of Freiburg, Germany] [pdf] [arxiv] [author] [notes]
- Simple Online and Realtime Tracking [icip16] [pdf] [notes] [code]
- High-Speed Tracking-by-Detection Without Using Image Information [avss17] [pdf] [notes] [code]
- Deep Reinforcement Learning for Visual Object Tracking in Videos [ax1704] [USC-Santa Barbara, Samsung Research] [pdf] [arxiv] [author] [notes]
- Visual Tracking by Reinforced Decision Making [ax1702] [Seoul National University, Chung-Ang University] [pdf] [arxiv] [author] [notes]
- Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning [cvpr17] [Seoul National University] [pdf] [supplementary] [project] [notes] [code]
- End-to-end Active Object Tracking via Reinforcement Learning [ax1705] [Peking University, Tencent AI Lab] [pdf] [arxiv]
- Fully-Convolutional Siamese Networks for Object Tracking [eccv16] [pdf] [project] [notes]
- High Performance Visual Tracking with Siamese Region Proposal Network [cvpr18] [pdf] [author] [notes]
- Siam R-CNN Visual Tracking by Re-Detection [cvpr20] [pdf] [notes] [project] [code]
- ATOM Accurate Tracking by Overlap Maximization [cvpr19] [pdf] [notes] [code]
- DiMP Learning Discriminative Model Prediction for Tracking [iccv19] [pdf] [notes] [code]
- D3S – A Discriminative Single Shot Segmentation Tracker [cvpr20] [pdf] [notes] [code]
- Decoupled Neural Interfaces using Synthetic Gradients [ax1608] [pdf] [notes]
- Understanding Synthetic Gradients and Decoupled Neural Interfaces [ax1703] [pdf] [notes]
- Video Frame Interpolation via Adaptive Convolution [cvpr17 / iccv17] [pdf (cvpr17)] [pdf (iccv17)] [ppt]
- beta-VAE Learning Basic Visual Concepts with a Constrained Variational Framework [iclr17] [pdf] [notes]
- Disentangling by Factorising [ax1806] [pdf] [notes]
- IDOT
- UA-DETRAC Benchmark Suite
- GRAM Road-Traffic Monitoring
- Ko-PER Intersection Dataset
- TRANCOS
- Urban Tracker
- DARPA VIVID / PETS 2005 [Non stationary camera]
- KIT-AKS [No ground truth]
- CBCL StreetScenes Challenge Framework [No top down viewpoint]
- MOT 2015 [mostly street level viewpoint]
- MOT 2016 [mostly street level viewpoint]
- MOT 2017 [mostly street level viewpoint]
- MOT 2020 [mostly top down viewpoint]
- MOTS: Multi-Object Tracking and Segmentation [MOT and KITTI]
- CVPR 2019 [mostly street level viewpoint]
- PETS 2009 [No vehicles]
- PETS 2017 [Low density] [mostly pedestrians]
- DukeMTMC [multi camera] [static background] [pedestrians] [above-street level viewpoint] [website not working]
- KITTI Tracking Dataset [No top down viewpoint] [non stationary camera]
- The WILDTRACK Seven-Camera HD Dataset [pedestrian detection and tracking]
- 3D Traffic Scene Understanding from Movable Platforms [intersection traffic] [stereo setup] [moving camera]
- LOST : Longterm Observation of Scenes with Tracks [top down and street level viewpoint] [no ground truth]
- JTA [top down and street level viewpoint] [synthetic/GTA 5] [pedestrian] [3D annotations]
- PathTrack: Fast Trajectory Annotation with Path Supervision [top down and street level viewpoint] [iccv17] [pedestrian]
- CityFlow [pole mounted] [intersections] [vehicles] [re-id] [cvpr19]
- JackRabbot Dataset [RGBD] [head-on][indoor/outdoor][stanford]
- TAO: A Large-Scale Benchmark for Tracking Any Object [eccv20] [code]
- Edinburgh office monitoring video dataset [indoors][long term][mostly static people]
- Waymo Open Dataset [outdoors][vehicles]
- Stanford Drone Dataset
- UAVDT - The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking [uav] [intersections/highways] [vehicles] [eccv18]
- VisDrone
- MNIST-MOT / MNIST-Sprites [script generated] [cvpr19]
- TUB Multi-Object and Multi-Camera Tracking Dataset [avss16]
- Virtual KITTI [arxiv] [cvpr16] [link seems broken]
- Cell Tracking Challenge [nature methods/2017]
- CTMC: Cell Tracking with Mitosis Detection Dataset Challenge [cvprw20] [MOT]
- TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild [eccv18]
- LaSOT: Large-scale Single Object Tracking [cvpr19]
- Need for speed: A benchmark for higher frame rate object tracking [iccv17]
- Long-term Tracking in the Wild A Benchmark [eccv18]
- UAV123: A benchmark and simulator for UAV tracking [eccv16] [project]
- Sim4CV A Photo-Realistic Simulator for Computer Vision Applications [ijcv18]
- CDTB: A Color and Depth Visual Object Tracking and Benchmark [iccv19] [RGBD]
- Temple Color 128 - Color Tracking Benchmark [tip15]
- YouTube-8M
- AVA: A Video Dataset of Atomic Visual Action
- VIRAT Video Dataset
- Kinetics Action Recognition Dataset
- PASCAL Visual Object Classes
- A Large-Scale Dataset for Vehicle Re-Identification in the Wild [cvpr19]
- Object Detection-based annotations for some frames of the VIRAT dataset
- MIO-TCD: A new benchmark dataset for vehicle classification and localization [tip18]
- Tiny ImageNet
- Wildlife Image and Localization Dataset (species and bounding box labels) [wacv18]
- Stanford Dogs Dataset [cvpr11]
- Oxford-IIIT Pet Dataset [cvpr12]
- Caltech-UCSD Birds 200 [rough segmentation] [attributes]
- Gold Standard Snapshot Serengeti Bounding Box Coordinates
- COCO - Common Objects in Context
- Open Images
- ADE20K [cvpr17]
- SYNTHIA [cvpr16]
- UC Berkeley Computer Vision Group - Contour Detection and Image Segmentation
- DAVIS: Densely Annotated VIdeo Segmentation
- Mapillary Vistas Dataset [street scenes] [semi-free]
- BDD100K [street scenes] [autonomous driving]
- ApolloScape [street scenes] [autonomous driving]
- Cityscapes [street scenes] [instance-level]
- YouTube-VOS [iccv19]
- ImageNet Large Scale Visual Recognition Competition 2012
- Animals with Attributes 2
- CompCars Dataset
- ObjectNet [only test set]
- Gluon CV Toolkit [mxnet] [pytorch]
- OpenMMLab Computer Vision Foundation [pytorch]
- Globally-optimal greedy algorithms for tracking a variable number of objects [cvpr11] [matlab] [author]
- Continuous Energy Minimization for Multitarget Tracking [cvpr11 / iccv11 / tpami 2014] [matlab]
- Discrete-Continuous Energy Minimization for Multi-Target Tracking [cvpr12] [matlab] [project]
- The way they move: Tracking multiple targets with similar appearance [iccv13] [matlab]
- 3D Traffic Scene Understanding from Movable Platforms [2d_tracking] [pami14/kit13/iccv13/nips11] [c++/matlab]
- Multiple target tracking based on undirected hierarchical relation hypergraph [cvpr14] [C++] [author]
- Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning [cvpr14] [matlab] (project)
- Learning to Track: Online Multi-Object Tracking by Decision Making [iccv15] [matlab]
- Joint Tracking and Segmentation of Multiple Targets [cvpr15] [matlab]
- Multiple Hypothesis Tracking Revisited [iccv15] [highest MT on MOT2015 among open source trackers] [matlab]
- Combined Image- and World-Space Tracking in Traffic Scenes [icra 2017] [c++]
- Online Multi-Target Tracking with Recurrent Neural Networks [aaai17] [lua/torch7]
- Real-Time Multiple Object Tracking - A Study on the Importance of Speed [ax1710/masters thesis] [c++]
- Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking [icra18] [matlab]
- Online Multi-Object Tracking with Dual Matching Attention Network [eccv18] [matlab/tensorflow]
- TrackR-CNN - Multi-Object Tracking and Segmentation [cvpr19] [tensorflow] [project]
- Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking [cvpr19] [tensorflow]
- Robust Multi-Modality Multi-Object Tracking [iccv19] [pytorch]
- Towards Real-Time Multi-Object Tracking / Joint Detection and Embedding [ax1909] [pytorch] [CMU]
- Deep Affinity Network for Multiple Object Tracking [tpami19] [pytorch]
- Tracking without bells and whistles [iccv19] [pytorch]
- Lifted Disjoint Paths with Application in Multiple Object Tracking [icml20] [matlab] [mot15#1,mot16 #3,mot17#2]
- Learning a Neural Solver for Multiple Object Tracking [cvpr20] [pytorch] [mot15#2]
- Tracking Objects as Points [ax2004] [pytorch]
- Quasi-Dense Similarity Learning for Multiple Object Tracking [ax2006] [pytorch]
- DEFT: Detection Embeddings for Tracking [ax2102] [pytorch]
- How To Train Your Deep Multi-Object Tracker [ax1906/cvpr20] [pytorch] [traktor/gitlab]
- Track To Detect and Segment: An Online Multi-Object Tracker [cvpr21] [pytorch] [project]
- MOTR: End-to-End Multiple-Object Tracking with Transformer [ax2202] [pytorch]
- Simple Online and Realtime Tracking [icip 2016] [python]
- Deep SORT : Simple Online Realtime Tracking with a Deep Association Metric [icip17] [python]
- High-Speed Tracking-by-Detection Without Using Image Information [avss17] [python]
- A simple baseline for one-shot multi-object tracking [ax2004] [pytorch] [winner of mot15,16,17,20]
- SiamMOT: Siamese Multi-Object Tracking [ax2105] [pytorch]
- Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers [cvpr19] [python/c++/pytorch]
- Torchreid: Deep learning person re-identification in PyTorch [ax1910] [pytorch]
- SMOT: Single-Shot Multi Object Tracking [ax2010] [pytorch] [gluon-cv]
- FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [ax2004] [pytorch] [microsoft] [BDD100K] [face tracking]
- Rethinking the competition between detection and ReID in Multi-Object Tracking [ax2010] [pytorch]
- Joint Object Detection and Multi-Object Tracking with Graph Neural Networks [ax2006/ icra21] [pytorch]
- Baxter Algorithms / Viterbi Tracking [tmi14] [matlab]
- Deepcell: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning [biorxiv1910] [tensorflow]
- 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics [iros20/eccvw20] [pytorch]
- GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning [iros20/eccvw20] [pytorch]
- HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking [cvpr20] [python]
- A collection of common tracking algorithms (2003-2012) [c++/matlab]
- SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask [pytorch]
- In Defense of Color-based Model-free Tracking [cvpr15] [c++]
- Hierarchical Convolutional Features for Visual Tracking [iccv15] [matlab]
- Visual Tracking with Fully Convolutional Networks [iccv15] [matlab]
- Hierarchical Convolutional Features for Visual Tracking [iccv15] [matlab]
- DeepTracking: Seeing Beyond Seeing Using Recurrent Neural Networks [aaai16] [torch 7]
- Learning Multi-Domain Convolutional Neural Networks for Visual Tracking [cvpr16] [vot2015 winner] [matlab/matconvnet] [pytorch]
- Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking [eccv 2016] [matlab]
- Fully-Convolutional Siamese Networks for Object Tracking [eccvw 2016] [matlab/matconvnet] [project] [pytorch] [pytorch (only training)]
- DCFNet: Discriminant Correlation Filters Network for Visual Tracking [ax1704] [matlab/matconvnet] [pytorch]
- End-to-end representation learning for Correlation Filter based tracking [cvpr17] [matlab/matconvnet] [tensorflow/inference_only] [project]
- Dual Deep Network for Visual Tracking [tip1704] [caffe]
- SiameseX: A simplified PyTorch implementation of Siamese networks for tracking: SiamFC, SiamRPN, SiamRPN++, SiamVGG, SiamDW, SiamRPN-VGG [pytorch]
- RATM: Recurrent Attentive Tracking Model [cvprw17] [python]
- ROLO : Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking [iscas 2017] [tensorfow]
- ECO: Efficient Convolution Operators for Tracking [cvpr17] [matlab] [python/cuda] [pytorch]
- Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning [cvpr17] [tensorflow]
- Detect to Track and Track to Detect [iccv17] [matlab]
- Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers [eccv18] [pytorch]
- Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking [cvpr18] [matlab]
- High Performance Visual Tracking with Siamese Region Proposal Network [cvpr18] [pytorch/195] [pytorch/313] [pytorch/no_train/104] [pytorch/177]
- Distractor-aware Siamese Networks for Visual Object Tracking [eccv18] [vot18 winner] [pytorch]
- VITAL: VIsual Tracking via Adversarial Learning [cvpr18] [matlab] [pytorch] [project]
- Fast Online Object Tracking and Segmentation: A Unifying Approach (SiamMask) [cvpr19] [pytorch] [project]
- PyTracking: A general python framework for training and running visual object trackers, based on PyTorch [ECO/ATOM/DiMP/PrDiMP] [cvpr17/cvpr19/iccv19/cvpr20] [pytorch]
- Unsupervised Deep Tracking [cvpr19] [matlab/matconvnet] [pytorch]
- Deeper and Wider Siamese Networks for Real-Time Visual Tracking [cvpr19] [pytorch]
- GradNet: Gradient-Guided Network for Visual Object Tracking [iccv19] [tensorflow]
- `Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-term Tracking [iccv19] [tensorflow]
- Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking [iccv19] [matlab]
- Learning the Model Update for Siamese Trackers [iccv19] [pytorch]
- SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking [cvpr19] [pytorch] [inference-only]
- Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking [iccv19] [matlab]
- Siam R-CNN: Visual Tracking by Re-Detection [cvpr20] [tensorflow]
- D3S - Discriminative Single Shot Segmentation Tracker [cvpr20] [pytorch/pytracking]
- Discriminative and Robust Online Learning for Siamese Visual Tracking [aaai20] [pytorch/pysot]
- Siamese Box Adaptive Network for Visual Tracking [cvpr20] [pytorch/pysot]
- Ocean: Object-aware Anchor-free Tracking [ax2010] [pytorch]
- BioTracker An Open-Source Computer Vision Framework for Visual Animal Tracking[opencv/c++]
- Tracktor: Image‐based automated tracking of animal movement and behaviour[opencv/c++]
- MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology[matlab]
- idtracker.ai: Tracking all individuals in large collectives of unmarked animals [tensorflow] [project]
- Flow-Guided Feature Aggregation for Video Object Detection [nips16 / iccv17] [mxnet]
- T-CNN: Tubelets with Convolution Neural Networks [cvpr16] [python]
- TPN: Tubelet Proposal Network [cvpr17] [python]
- Deep Feature Flow for Video Recognition [cvpr17] [mxnet]
- Mobile Video Object Detection with Temporally-Aware Feature Maps [cvpr18] [Google] [tensorflow]
- Tensorflow object detection API [tensorflow]
- Detectron2 [pytorch]
- Detectron [pytorch]
- Open MMLab Detection Toolbox with PyTorch [pytorch]
- SimpleDet [mxnet]
- MCG : Multiscale Combinatorial Grouping - Object Proposals and Segmentation (project) [tpami16/cvpr14] [python]
- COB : Convolutional Oriented Boundaries (project) [tpami18/eccv16] [matlab/caffe]
- Feature Pyramid Networks for Object Detection [caffe/python]
- RFCN (author) [caffe/matlab]
- RFCN-tensorflow [tensorflow]
- PVANet: Lightweight Deep Neural Networks for Real-time Object Detection [intel] [emdnn16(nips16)]
- Mask R-CNN [tensorflow] [keras]
- Light-head R-CNN [cvpr18] [tensorflow]
- Evolving Boxes for Fast Vehicle Detection [icme18] [caffe/python]
- Cascade R-CNN (cvpr18) [detectron] [caffe]
- A MultiPath Network for Object Detection [torch] [bmvc16] [facebook]
- SNIPER: Efficient Multi-Scale Training/An Analysis of Scale Invariance in Object Detection-SNIP [nips18/cvpr18] [mxnet]
- SSD-Tensorflow [tensorflow]
- SSD-Tensorflow (tf.estimator) [tensorflow]
- SSD-Tensorflow (tf.slim) [tensorflow]
- SSD-Keras [keras]
- SSD-Pytorch [pytorch]
- Enhanced SSD with Feature Fusion and Visual Reasoning [nca18] [tensorflow]
- RefineDet - Single-Shot Refinement Neural Network for Object Detection [cvpr18] [caffe]
- 9.277.41 [pytorch]
- 31.857.212 [pytorch]
- 25.274.84 [pytorch] [nvidia]
- 22.869.302 [pytorch]
- Darknet: Convolutional Neural Networks [c/python]
- YOLO9000: Better, Faster, Stronger - Real-Time Object Detection. 9000 classes! [c/python]
- Darkflow [tensorflow]
- Pytorch Yolov2 [pytorch]
- Yolo-v3 and Yolo-v2 for Windows and Linux [c/python]
- YOLOv3 in PyTorch [pytorch]
- pytorch-yolo-v3 [pytorch] [no training] [tutorial]
- YOLOv3_TensorFlow [tensorflow]
- tensorflow-yolo-v3 [tensorflow slim]
- tensorflow-yolov3 [tensorflow slim]
- keras-yolov3 [keras]
- YOLOv4 [darknet - c/python] [tensorflow] [pytorch/711] [pytorch/ONNX/TensorRT/1.9k] [pytorch 3D]
- YOLOv5 [pytorch]
- YOLOX [pytorch] MegEngine [ax2107]
- FoveaBox: Beyond Anchor-based Object Detector [ax1904] [pytorch/mmdetection]
- Cornernet: Detecting objects as paired keypoints [ax1903/eccv18] [pytorch]
- FCOS: Fully Convolutional One-Stage Object Detection [iccv19] [pytorch] [VoVNet] [HRNet] [NAS] [FCOS_PLUS]
- Feature Selective Anchor-Free Module for Single-Shot Object Detection [cvpr19] [pytorch]
- CenterNet: Objects as Points [ax1904] [pytorch]
- Bottom-up Object Detection by Grouping Extreme and Center Points, [cvpr19] [pytorch]
- RepPoints Point Set Representation for Object Detection [iccv19] [pytorch] [microsoft]
- DE⫶TR: End-to-End Object Detection with Transformers [ax200528] [pytorch] [facebook]
- Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection [cvpr20] [pytorch]
- Relation Networks for Object Detection [cvpr18] [mxnet]
- DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling [iccv17(poster)] [theano]
- Multi-scale Location-aware Kernel Representation for Object Detection [cvpr18] [caffe/python]
- Holistically-Nested Edge Detection (HED) (iccv15) [caffe]
- Edge-Detection-using-Deep-Learning (HED) [tensorflow]
- Holistically-Nested Edge Detection (HED) in OpenCV [python/c++]
- Crisp Boundary Detection Using Pointwise Mutual Information (eccv14) [matlab]
- Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection [wacv20] tensorflow pytorch
- Real-time Scene Text Detection with Differentiable Binarization [pytorch] [aaai20]
- OpenMMLab's next-generation platform for general 3D object detection [pytorch]
- OpenPCDet Toolbox for LiDAR-based 3D Object Detection [pytorch]
- FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks (cvpr17) [caffe] [pytorch/nvidia]
- SPyNet: Spatial Pyramid Network for Optical Flow (cvpr17) [lua] [pytorch]
- Guided Optical Flow Learning (cvprw17) [caffe] [tensorflow]
- Fast Optical Flow using Dense Inverse Search (DIS) [eccv16] [C++]
- A Filter Formulation for Computing Real Time Optical Flow [ral16] [c++/cuda - matlab,python wrappers]
- PatchBatch - a Batch Augmented Loss for Optical Flow [cvpr16] [python/theano]
- Piecewise Rigid Scene Flow [iccv13/eccv14/ijcv15] [c++/matlab]
- DeepFlow v2 [iccv13] [c++/python/matlab], [project]
- An Evaluation of Data Costs for Optical Flow [gcpr13] [matlab]
- Fully Convolutional Instance-aware Semantic Segmentation [cvpr17] [coco16 winner] [mxnet]
- Instance-aware Semantic Segmentation via Multi-task Network Cascades [cvpr16] [caffe] [coco15 winner]
- DeepMask/SharpMask [nips15/eccv16] [facebook] [torch] [tensorflow] [pytorch/deepmask]
- Simultaneous Detection and Segmentation [eccv14] [matlab] [project]
- PANet [cvpr18] [pytorch]
- RetinaMask [arxviv1901] [pytorch]
- Mask Scoring R-CNN [cvpr19] [pytorch]
- DeepMAC [ax2104] [tensorflow]
- Swin Transformer [iccv21] [pytorch] [microsoft]
- Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch [pytorch] [facebook]
- PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle. [2019]
- Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation [cvpr18] [spotlight] [pytorch]
- Few-shot Segmentation Propagation with Guided Networks [ax1806] [pytorch] [incomplete]
- Pytorch-segmentation-toolbox [DeeplabV3 and PSPNet] [pytorch]
- DeepLab [tensorflow]
- Auto-DeepLab [pytorch]
- DeepLab v3+ [pytorch]
- Deep Extreme Cut (DEXTR): From Extreme Points to Object Segmentation[cvpr18][project] [pytorch]
- FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation[ax1903][project] [pytorch]
- PraNet: Parallel Reverse Attention Network for Polyp Segmentation[miccai20]
- PHarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS[ax2101]
- Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation [cvpr20] [pytorch]
- Improving Semantic Segmentation via Video Prediction and Label Relaxation [cvpr19] [pytorch] [nvidia]
- PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation [accv18/cvprw18/eccvw18] [tensorflow]
- MaskTrackRCNN for video instance segmentation [iccv19] [pytorch/detectron]
- MaskTrackRCNN [iccv19] [pytorch/detectron]
- Video Instance Segmentation using Inter-Frame Communication Transformers [nips21] [pytorch/detectron]
- VNext: SeqFormer / IDOL [eccv22] [pytorch/detectron2]
- SeqFormer: Sequential Transformer for Video Instance Segmentation [eccv22] [pytorch/detectron2]
- VITA: Video Instance Segmentation via Object Token Association [nips22] [pytorch/detectron2]
- ViP-DeepLab [cvpr21]
- Self-Supervised Learning via Conditional Motion Propagation [cvpr19] [pytorch]
- A Neural Temporal Model for Human Motion Prediction [cvpr19] [tensorflow]
- Learning Trajectory Dependencies for Human Motion Prediction [iccv19] [pytorch]
- Structural-RNN: Deep Learning on Spatio-Temporal Graphs [cvpr15] [tensorflow]
- A Keras multi-input multi-output LSTM-based RNN for object trajectory forecasting [keras]
- Transformer Networks for Trajectory Forecasting [ax2003] [pytorch]
- Regularizing neural networks for future trajectory prediction via IRL framework [ietcv1907] [tensorflow]
- Peeking into the Future: Predicting Future Person Activities and Locations in Videos [cvpr19] [tensorflow]
- DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting [ax200526] [pytorch]
- MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Traffic [ax200405] [tensorflow]
- Human Trajectory Prediction in Socially Interacting Crowds Using a CNN-based Architecture [pytorch]
- A tool set for trajectory prediction, ready for pip install [icai19/wacv19] [pytorch]
- RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs [acmcscs19] [pytorch/tensorflow]
- The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction [cvpr20] [dummy]
- Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction [cvpr19] [tensorflow]
- Adversarial Loss for Human Trajectory Prediction [hEART19] [pytorch]
- Social GAN: SSocially Acceptable Trajectories with Generative Adversarial Networks [cvpr18] [pytorch]
- Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs [ax1912] [pytorch]
- Study of attention mechanisms for trajectory prediction in Deep Learning [msc thesis] [python]
- A python implementation of multi-model estimation algorithm for trajectory tracking and prediction, research project from BMW ABSOLUT self-driving bus project. [python]
- Prediciting Human Trajectories [theano]
- Implementation of Recurrent Neural Networks for future trajectory prediction of pedestrians [pytorch]
- β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework [iclr17] [deepmind] [tensorflow] [tensorflow] [pytorch]
- Disentangling by Factorising [ax1806] [pytorch]
- Learning Efficient Convolutional Networks Through Network Slimming [iccv17] [pytorch]
- LabelImg
- ByLabel: A Boundary Based Semi-Automatic Image Annotation Tool
- Bounding Box Editor and Exporter
- VGG Image Annotator
- Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos
- PixelAnnotationTool
- labelme : Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation)
- VATIC - Video Annotation Tool from Irvine, California) [ijcv12] [project]
- Computer Vision Annotation Tool (CVAT)
- Image labelling tool
- Labelbox [paid]
- RectLabel An image annotation tool to label images for bounding box object detection and segmentation. [paid]
- Onepanel: Production scale vision AI platform with fully integrated components for model building, automated labeling, data processing and model training pipelines. [docs]
- Augmentor: Image augmentation library in Python for machine learning
- Albumentations: Fast image augmentation library and easy to use wrapper around other libraries
- imgaug: Image augmentation for machine learning experiments
- solt: Image Streaming over lightweight data transformations
- Imbalanced Dataset Sampler [pytorch]
- Iterable dataset resampling in PyTorch [pytorch]
- Awesome Public Datasets
- List of traffic surveillance datasets
- Machine learning datasets: A list of the biggest machine learning datasets from across the web
- Labeled Information Library of Alexandria: Biology and Conservation [other conservation data sets]
- THOTH: Data Sets & Images
- Google AI Datasets
- Google Cloud Storage public datasets
- Microsoft Research Open Data
- Earth Engine Data Catalog
- Registry of Open Data on AWS
- Kaggle Datasets
- CVonline: Image Databases
- Synthetic for Computer Vision: A list of synthetic dataset and tools for computer vision
- pgram machine learning datasets
- pgram vision datasets
- Visual Tracking Paper List
- List of deep learning based tracking papers
- List of single object trackers with results on OTB
- Collection of Correlation Filter based trackers with links to papers, codes, etc
- VOT2018 Trackers repository
- CUHK Datasets
- A Summary of CVPR19 Visual Tracking Papers
- Visual Trackers for Single Object
- List of multi object tracking papers
- A collection of Multiple Object Tracking (MOT) papers in recent years, with notes
- Papers with Code : Multiple Object Tracking
- Paper list and source code for multi-object-tracking
- Segmentation Papers and Code
- Segmentation.X : Papers and Benchmarks about semantic segmentation, instance segmentation, panoptic segmentation and video segmentation
- Instance Segmentation Papers with Code
- Awesome-Trajectory-Prediction
- Awesome Interaction-aware Behavior and Trajectory Prediction
- Human Trajectory Prediction Datasets
- Papers With Code : the latest in machine learning
- Awesome Deep Ecology
- List of Matlab frameworks, libraries and software
- Face Recognition
- A Month of Machine Learning Paper Summaries
- Awesome-model-compression-and-acceleration
- Model-Compression-Papers
- End-to-end object detection with Transformers
- Deep Learning for Object Detection: A Comprehensive Review
- Review of Deep Learning Algorithms for Object Detection
- A Simple Guide to the Versions of the Inception Network
- R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms
- A gentle guide to deep learning object detection
- The intuition behind RetinaNet
- YOLO—You only look once, real time object detection explained
- Understanding Feature Pyramid Networks for object detection (FPN)
- Fast object detection with SqueezeDet on Keras
- Region of interest pooling explained
- Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow
- Simple Understanding of Mask RCNN
- Learning to Segment
- Analyzing The Papers Behind Facebook's Computer Vision Approach
- Review: MNC — Multi-task Network Cascade, Winner in 2015 COCO Segmentation
- Review: FCIS — Winner in 2016 COCO Segmentation
- Review: InstanceFCN — Instance-Sensitive Score Maps
- Learning from imbalanced data
- Learning from Imbalanced Classes
- Handling imbalanced datasets in machine learning [medium]
- How to handle Class Imbalance Problem [medium]
- Dealing with Imbalanced Data [towardsdatascience]
- How to Handle Imbalanced Classes in Machine Learning [elitedatascience]
- 7 Techniques to Handle Imbalanced Data [kdnuggets]
- 10 Techniques to deal with Imbalanced Classes in Machine Learning [analyticsvidhya]
- Guide to Autoencoders
- Applied Deep Learning - Part 3: Autoencoders
- Denoising Autoencoders
- Stacked Denoising Autoencoders
- A Gentle Introduction to LSTM Autoencoders
- Variational Autoencoder in TensorFlow
- Variational Autoencoders with Tensorflow Probability Layers
- Facebook AI
- Google AI
- Google DeepMind
- Deep Learning Wizard
- Towards Data Science
- Jay Alammar : Visualizing machine learning one concept at a time
- Inside Machine Learning: Deep-dive articles about machine learning, cloud, and data. Curated by IBM
- colah's blog
- Jeremy Jordan
- Silicon Valley Data Science
- Illarion’s Notes