{"list":"other","total":89,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"id":"1704.04861","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","created":"2017-04-17","cited_by_count":3845},{"id":"1512.02325","title":"SSD: Single Shot MultiBox Detector","created":"2015-12-08","cited_by_count":3130},{"id":"1804.02767","title":"YOLOv3: An Incremental Improvement","created":"2018-04-08","cited_by_count":2659},{"id":"1612.08242","title":"YOLO9000: Better, Faster, Stronger","created":"2016-12-25","cited_by_count":1431},{"id":"1805.04687","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning","created":"2018-05-12","cited_by_count":1288},{"id":"1703.07402","title":"Simple Online and Realtime Tracking with a Deep Association Metric","created":"2017-03-21","cited_by_count":910},{"id":"1602.00763","title":"Simple Online and Realtime Tracking","created":"2016-02-02","cited_by_count":868},{"id":"2102.02808","title":"Multi-Stage Progressive Image Restoration","created":"2021-02-04","cited_by_count":816},{"id":"2001.05566","title":"Image Segmentation Using Deep Learning: A Survey","created":"2020-01-15","cited_by_count":660},{"id":"1704.04760","title":"In-Datacenter Performance Analysis of a Tensor Processing Unit","created":"2017-04-16","cited_by_count":618},{"id":"1709.01686","title":"BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks","created":"2017-09-06","cited_by_count":596},{"id":"1603.00831","title":"MOT16: A Benchmark for Multi-Object Tracking","created":"2016-03-02","cited_by_count":532},{"id":"1807.05511","title":"Object Detection with Deep Learning: A Review","created":"2018-07-15","cited_by_count":528},{"id":"2004.01888","title":"FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking","created":"2020-04-04","cited_by_count":490},{"id":"1708.05866","title":"A Brief Survey of Deep Reinforcement Learning","created":"2017-08-19","cited_by_count":483},{"id":"2004.01177","title":"Tracking Objects as Points","created":"2020-04-02","cited_by_count":483},{"id":"1909.11875","title":"Federated Learning in Mobile Edge Networks: A Comprehensive Survey","created":"2019-09-26","cited_by_count":408},{"id":"1809.02165","title":"Deep Learning for Generic Object Detection: A Survey","created":"2018-09-06","cited_by_count":392},{"id":"1512.07108","title":"Recent Advances in Convolutional Neural Networks","created":"2015-12-22","cited_by_count":360},{"id":"2003.09003","title":"MOT20: A benchmark for multi object tracking in crowded scenes","created":"2020-03-19","cited_by_count":357},{"id":"1909.12605","title":"Towards Real-Time Multi-Object Tracking","created":"2019-09-27","cited_by_count":311},{"id":"1911.07559","title":"FFA-Net: Feature Fusion Attention Network for Single Image Dehazing","created":"2019-11-18","cited_by_count":307},{"id":"1905.10083","title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","created":"2019-05-24","cited_by_count":228},{"id":"1902.03604","title":"MOTS: Multi-Object Tracking and Segmentation","created":"2019-02-10","cited_by_count":223},{"id":"1804.00518","title":"The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking","created":"2018-03-26","cited_by_count":208},{"id":"1409.7618","title":"Multiple Object Tracking: A Literature Review","created":"2014-09-26","cited_by_count":166},{"id":"2005.10356","title":"TAO: A Large-Scale Benchmark for Tracking Any Object","created":"2020-05-20","cited_by_count":147},{"id":"1504.01942","title":"MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking","created":"2015-04-08","cited_by_count":133},{"id":"1907.08349","title":"Convergence of Edge Computing and Deep Learning: A Comprehensive Survey","created":"2019-07-19","cited_by_count":120},{"id":"1907.12740","title":"Deep Learning in Video Multi-Object Tracking: A Survey","created":"2019-07-18","cited_by_count":107},{"id":"1511.04136","title":"UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking","created":"2015-11-13","cited_by_count":98},{"id":"1909.00560","title":"Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence","created":"2019-09-02","cited_by_count":76},{"id":"1810.10090","title":"NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision","created":"2018-10-23","cited_by_count":68},{"id":"2003.03423","title":"Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider","created":"2020-03-06","cited_by_count":66},{"id":"1701.00299","title":"Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution","created":"2017-01-02","cited_by_count":59},{"id":"2104.13400","title":"FrameExit: Conditional Early Exiting for Efficient Video Recognition","created":"2021-04-27","cited_by_count":59},{"id":"2012.10557","title":"Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers","created":"2020-12-19","cited_by_count":58},{"id":"1712.01670","title":"DeepCache: Principled Cache for Mobile Deep Vision","created":"2017-12-01","cited_by_count":57},{"id":"1801.03493","title":"Focus: Querying Large Video Datasets with Low Latency and Low Cost","created":"2018-01-10","cited_by_count":46},{"id":"1805.01046","title":"BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics","created":"2018-05-02","cited_by_count":41},{"id":"2102.10423","title":"An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks","created":"2021-02-20","cited_by_count":39},{"id":"1905.13536","title":"Scaling Video Analytics on Constrained Edge Nodes","created":"2019-05-24","cited_by_count":36},{"id":"1802.03835","title":"Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms","created":"2018-02-11","cited_by_count":35},{"id":"2201.07705","title":"GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge","created":"2022-01-19","cited_by_count":32},{"id":"2111.15157","title":"MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark","created":"2021-11-30","cited_by_count":29},{"id":"2104.12233","title":"The 5th AI City Challenge","created":"2021-04-25","cited_by_count":28},{"id":"1908.07985","title":"MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors","created":"2019-08-21","cited_by_count":27},{"id":"2003.11753","title":"Real-time 3D Deep Multi-Camera Tracking","created":"2020-03-26","cited_by_count":23},{"id":"2112.09852","title":"LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision","created":"2021-12-18","cited_by_count":20},{"id":"1806.04226","title":"Physical Representation-based Predicate Optimization for a Visual Analytics Database","created":"2018-06-11","cited_by_count":19},{"id":"2004.14619","title":"The 4th AI City Challenge","created":"2020-04-30","cited_by_count":18},{"id":"1706.03178","title":"Serverless Computing: Current Trends and Open Problems","created":"2017-06-10","cited_by_count":17},{"id":"2102.01887","title":"Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines","created":"2021-02-03","cited_by_count":16},{"id":"2010.10754","title":"ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles","created":"2020-10-21","cited_by_count":15},{"id":"1808.05283","title":"All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey","created":"2018-08-15","cited_by_count":14},{"id":"2007.13005","title":"Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics","created":"2020-07-25","cited_by_count":11},{"id":"2205.10635","title":"SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments","created":"2022-05-21","cited_by_count":11},{"id":"1906.02888","title":"The server is dead, long live the server: Rise of Serverless Computing, Overview of Current State and Future Trends in Research and Industry","created":"2019-06-07","cited_by_count":10},{"id":"2106.15315","title":"Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics","created":"2021-06-21","cited_by_count":10},{"id":"1911.00119","title":"ALERT: Accurate Learning for Energy and Timeliness","created":"2019-10-31","cited_by_count":9},{"id":"2001.01043","title":"SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning","created":"2020-01-04","cited_by_count":9},{"id":"2011.12025","title":"SegBlocks: Block-Based Dynamic Resolution Networks for Real-Time Segmentation","created":"2020-11-24","cited_by_count":9},{"id":"2102.03012","title":"A Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning","created":"2021-02-05","cited_by_count":9},{"id":"2105.06524","title":"CrossRoI: Cross-camera Region of Interest Optimization for Efficient Real Time Video Analytics at Scale","created":"2021-05-13","cited_by_count":9},{"id":"2108.09376","title":"BlockCopy: High-Resolution Video Processing with Block-Sparse Feature Propagation and Online Policies","created":"2021-08-20","cited_by_count":9},{"id":"2205.13770","title":"LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality","created":"2022-05-27","cited_by_count":9},{"id":"2102.08481","title":"THIA: Accelerating Video Analytics using Early Inference and Fine-Grained Query Planning","created":"2021-02-16","cited_by_count":8},{"id":"2207.00172","title":"Turbo: Opportunistic Enhancement for Edge Video Analytics","created":"2022-06-29","cited_by_count":7},{"id":"2207.09616","title":"Towards Transmission-Friendly and Robust CNN Models over Cloud and Device","created":"2022-07-20","cited_by_count":7},{"id":"2001.04049","title":"FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile","created":"2020-01-13","cited_by_count":6},{"id":"2101.09752","title":"AQuA: Analytical Quality Assessment for Optimizing Video Analytics Systems","created":"2021-01-24","cited_by_count":6},{"id":"2105.01803","title":"DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge","created":"2021-05-05","cited_by_count":6},{"id":"2207.00588","title":"CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics","created":"2022-07-02","cited_by_count":6},{"id":"1904.12342","title":"Video Analytics with Zero-streaming Cameras","created":"2019-04-28","cited_by_count":5},{"id":"2112.02439","title":"Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing","created":"2021-12-04","cited_by_count":5},{"id":"1411.7935","title":"Multiple object tracking with context awareness","created":"2014-11-24","cited_by_count":4},{"id":"2010.13737","title":"Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints","created":"2020-10-26","cited_by_count":4},{"id":"2107.10404","title":"DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers","created":"2021-07-22","cited_by_count":4},{"id":"1902.05577","title":"A Scalable Platform for Distributed Object Tracking across a Many-camera Network","created":"2019-02-14","cited_by_count":3},{"id":"2203.14481","title":"DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation","created":"2022-03-28","cited_by_count":3},{"id":"2208.12644","title":"Why is the video analytics accuracy fluctuating, and what can we do about it?","created":"2022-08-23","cited_by_count":3},{"id":"1909.02068","title":"ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients","created":"2019-08-28","cited_by_count":2},{"id":"2211.03102","title":"EdgeVision: Towards Collaborative Video Analytics on Distributed Edges for Performance Maximization","created":"2022-11-06","cited_by_count":2},{"id":"2210.01402","title":"Streaming Video Analytics On The Edge With Asynchronous Cloud Support","created":"2022-10-04","cited_by_count":1},{"id":"2210.08648","title":"AttTrack: Online Deep Attention Transfer for Multi-object Tracking","created":"2022-10-16","cited_by_count":1}],"next_cursor":null}