Traditional object detection methods are built on handcrafted features and shallow trainable architectures. •. RGB-D Salient Object Detection Video object detection is the task of detecting objects from a video as opposed to images. • ShuhanChen/PGAR_ECCV20 UNSUPERVISED DOMAIN ADAPTATION, 17 Jul 2019 {fyang,hengfan,pchu}@temple.edu, erik.blasch@us.af.mil, hling@cs.stonybrook.edu RGB-D Salient Object Detection • fkthi/OpenTrafficMonitoringPlus •. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century’s time (from the 1990s to 2019). ICCV 2019 The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios. on HKU-IS, RGB SALIENT OBJECT DETECTION ROBUST OBJECT DETECTION Abstract: This paper deals with the field of computer vision, mainly for the application of deep learning in object detection task. • Gabriel-Macias/robust_frcnn Image Source. •. Image-to-Image Translation 2, the high intrinsic similarities between the target objectand thebackgroundmakeCODfarmore challenging than the traditional salient object detection [1,5,17,25,62– 66,68] or generic object detection [4,79]. papers with code, tasks/Screenshot_2019-11-27_at_16.01.13_syDIRNY.png, Attentive Feedback Network for Boundary-Aware Salient Object Detection, Uncertainty Inspired RGB-D Saliency Detection, Cascaded Partial Decoder for Fast and Accurate Salient Object Detection, A Simple Pooling-Based Design for Real-Time Salient Object Detection, RGB Salient Object Detection on SIP 3/75. Get the latest machine learning methods with code. •. Browse State-of-the-Art Methods Trends About RC2020 Log In/Register; Get the weekly digest × Get the latest machine learning methods with code. NeurIPS 2016 • facebookresearch/detectron • In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. •. Soon, it was implemented in OpenCV and face detection became synonymous with Viola and Jones algorithm.Every few years a new idea comes along that forces people to pause and take note. • weijun88/LDF Ranked #3 on Object Detection from Scratch with Deep Supervision Abstract: In this paper, we propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. •. As shown in Fig. A Benchmark for the: Ranked #4 on ROBUST OBJECT DETECTION, ICCV 2019 ( Image credit: Attentive Feedback Network for Boundary-Aware Salient Object Detection ), 7 Sep 2020 II. on Cityscapes-to-Foggy Cityscapes, REGION PROPOSAL View Object Detection Research Papers on Academia.edu for free. Facial detection is one form of it, which can be utilized as a security measure to let only certain people into a highly classified area of a government building, for example. Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets. There are so many, in fact, that compiling any comprehensive review of the state of the art is beyond the scope of any reasonable length paper. Object Detection with Deep Learning: A Review Zhong-Qiu Zhao, Member, IEEE, Peng Zheng, Shou-tao Xu, and Xindong Wu, Fellow, IEEE Abstract—Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. SALIENT OBJECT DETECTION, ECCV 2020 R-FCN: Object Detection via Region-based Fully Convolutional Networks. Every object has its own class such as all circles are round, which are used while recognizing the objects. The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. Ranked #1 on 8 Object Detection Models are architectures used to perform the task of object detection. INSTANCE SEGMENTATION Benchmark Homepage: https://github.com/bethgelab/robust-detection-benchmark, mPC [AP]: Mean Performance under Corruption [measured in AP], rPC [%]: Relative Performance under Corruption [measured in %]. Get the latest machine learning methods with code. •. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. SALIENT OBJECT DETECTION •. INSTANCE SEGMENTATION ICCV 2019 • VITA-Group/UAV-NDFT • Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Object-Detection-paper. PREPROCESSING It is the lowest level of abstraction. SALIENT OBJECT DETECTION, CVPR 2020 •. REGION PROPOSAL To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose. Coco: val 2017; Pascal VOC: test 2007; Cityscapes: val; ( Image credit: Benchmarking Robustness in Object Detection ), CVPR 2018 Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter. • bethgelab/imagecorruptions. Detective: An Attentive Recurrent Model for Sparse Object Detection Model/Code API Access Call/Text an Expert Apr 25, 2020 Amine Kechaou, Manuel Martinez, Monica Haurilet, Rainer Stiefelhagen. If requested an unrestricted category can be added later. Object detection is still an active research area. • JingZhang617/UCNet Benchmarking Robustness in Object Detection, Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming, Domain Adaptive Faster R-CNN for Object Detection in the Wild, Image-to-Image Translation Get the latest machine learning methods with code. Clustered Object Detection in Aerial Images Fan Yang1 Heng Fan1 Peng Chu1 Erik Blasch2 Haibin Ling3,1∗ 1Department of Computer and Information Sciences, Temple University, Philadelphia, USA 2Air Force Research Lab, USA 3Department Computer Science, Stony Brook University, Stony Brook, NY, USA. Robustness enhancing methods all models have to use a standard ResNet50 backbone performance. 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