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best computer vision papers 2020

By December 5, 2020No Comments

I am a senior computer science student with a passion for computer vision. “Training” is running the lottery and seeing which weights are high-valued. Linkedin. Sometimes it is worthwhile to backtrack a bit and take a different turn. Paper Deadlines for the Major Computer Vision Meetings. 23-28 August; Glasgow, United Kingdom; Computer Vision – ECCV 2020. The former perform tasks such as converting line drawings to fully rendered images, and the latter excels at replacing entities, such as turning horses into zebras or apples into oranges. This was a bold move, as CNNs were considered too heavy to be trained on such a large scale problem. Reason #2: Only once in a while we get to see a paper with a fresh new take on the limitations of CNNs and their interpretability. Pix2Pix and CycleGAN are the two seminal works on conditional generative models. Reason #2: Science moves in baby steps. June 2, 2020 -- Important notice to all authors: the paper submission deadline has been extended by 48 hours. Further Reading: So far, MobileNet v2 and v3 have been released, providing new enhancements to accuracy and size. 2017. International Journal of Computer Vision (IJCV) details the science and engineering of this rapidly growing field. It will be held March 1-5, 2020 at The Westin Snowmass Resort in Snowmass village, Colorado. Reason #1: Most tips are easily applicable. A topic I believe deserves more attention is class and sample weights. Research papers are a good way to learn about these subjects. Top Conferences for Image Processing & Computer Vision. “Mobilenets: Efficient convolutional neural networks for mobile vision applications.” arXiv preprint arXiv:1704.04861 (2017). Support cvpapers: Other Computer Science Paper Indexes. In the SELU paper, the authors propose a unifying approach: an activation that self-normalizes its outputs. Access to Virtual Platform. 2019. It drastically reduced the size of the Transformer by improving the algorithm. “Image-to-image translation with conditional adversarial networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. In this paper, the authors found that classifying all 33x33 patches of an image and then averaging their class predictions achieves near state-of-the-art results on ImageNet. With these twelve papers and their further readings, I believe you already have plenty of reading material to look at. Vaswani, Ashish, et al. Computer vision … After reading this paper, I realized how underutilized our millions of parameters are. Zhu, Jun-Yan, et al. Google+. While generation might not be your thing, reading about multi-network setups might be inspiring for a number of problems. In combination, both views provide the ultimate set of techniques for efficient training and inference. Model efficiency... 2. The area has far-reaching applications, being usually divided by input type: text, audio, image, video, or graph; or by problem formulation: supervised, unsupervised, and reinforcement learning. Vergleich 2020 von COMPUTER BILD: Jetzt die besten Produkte von TOP-Marken im Test oder Vergleich entdecken! This surely isn’t an exhaustive list of great papers. In parallel, other authors have devised many techniques to further reduce the model size, such as the SqueezeNet, and to downsize regular models with minimal accuracy loss. Keeping up with everything is a massive endeavor and usually ends up being a frustrating attempt. The proposed soft Barrier Penalty is differentiable and can impose very large … 1129 Papers; 25 Volumes; 2018 ECCV 2018. Further Reading: If you want to dive into the history and usage of the most popular activation functions, I wrote a guide on activation functions here on Medium. In this spirit, I present some reading suggestions to keep you updated on the latest and classic breakthroughs in AI and Data Science. This last one achieved super-human performance, solving the challenge. Using virtual reality (VR) in healthcare – A panoramic view, Smart sensors in modern logistics: Overcoming supply chain disruptions, Why and how to choose the right machine vision system, How to deal with seven common Macbook problems. Nowadays, ImageNet is mainly used for Transfer Learning and to validate low-parameter models, such as: Howard, Andrew G., et al. Many times, what you need is not a fancy new model, just a couple of new tricks. Kitaev, Nikita, Łukasz Kaiser, and Anselm Levskaya. 2018. Such models are ideal for low-resources devices and to speed-up real-time applications, such as object recognition on mobile phones. this comprehensive state-of-the-art review. Single Headed Attention RNN: Stop Thinking With Your Head, “Simple baselines for human pose estimation and tracking.”. ReddIt. Computer Vision News (magazine dedicated to the algorithm community) Tweet. Reason #1: Most of us have nowhere near the resources the big tech companies have. Reason #1: While most of us know AlexNet’s historical importance, not everyone knows which of the techniques we use today were already present before the boom. Major topics include image processing, detection and recognition, geometry-based … Reason #2: Common knowledge is that bigger models are stronger models. You have entered an incorrect email address! Proceedings of the European conference on computer vision (ECCV). In my experience, most people stick to the defaults, which might not always be the best option. Models such as Self-Attention GAN demonstrate the usefulness of global-level reasoning a variety of tasks. Before we begin, I would like to apologize to the Audio and Reinforcement Learning communities for not adding these subjects to the list, as I have only limited experience with both. Reason #2: As for the Bag-of-Features paper, this sheds some light on how limited our current understanding of CNNs is. Share. Scaling the size of models is not the only avenue for improvement. “Single Headed Attention RNN: Stop Thinking With Your Head.” arXiv preprint arXiv:1911.11423 (2019). Computer Vision Project Idea – Contours are outlines or the boundaries of the shape. By. Most of us use Batch Normalization layers and the ReLU or ELU activation functions. “Bag of tricks for image classification with convolutional neural networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. The Best NLP/NLU Papers from the ICLR 2020 Conference Posted May 7, 2020. Reading a paper on purely dense networks is a bit of a refreshment. The Ranking of Top Journals for Computer Science and Electronics was prepared by Guide2Research, one of the leading portals for computer science research providing trusted data on scientific contributions since 2014. We solicit original research for publication in the main conference. Make learning your daily ritual. Alongside each suggestion, I listed some of the reasons I believe you should read (or re-read) the paper and added some further readings, in case you want to dive a bit deeper into a given subject. Reason #2: Big companies can quickly scale their research to a hundred GPUs. Computer Vision Conferences 2020/2021/2022 is for the researchers, scientists, scholars, engineers, academic, scientific and university practitioners to present research activities that might want to attend events, meetings, seminars, congresses, workshops, summit, and symposiums. The proposed formulation achieved significantly better state-of-the-art results and trains markedly faster than previous RNN models. There are many interesting papers on computer vision (CV) so I will list the ones I think has helped shape CV as we know it today. Save my name, email, and website in this browser for the next time I comment. New papers on Attention applications pop-up every month. Computer Vision Calls For Papers (CFP) for international conferences, workshops, meetings, seminars, events, journals and book chapters To everyone surprise, they won first place, with a ~15% Top-5 error rate, against ~26% of the second place, which used state-of-the-art image processing techniques. How is 3D Printing advancing the Biotech industry? For instance, at being a virtual assistant to artists. 324 Papers; 6 Volumes; 2016 ECCV 2016. Share. As we start 2020, it’s useful to take a step back and assess the research work we’ve done over the past year, and also to look forward to what sorts of problems we want to tackle in the upcoming years. This, in itself, is a rare but beautiful thing to be seen. CVPR Workshop on Computer Vision for Augmented and Virtual Reality, 2020 We present a simple, real-time approach for pupil tracking from live video on mobile devices. They use it for navigation through its environment (SLAM), detecting obstacles and specific events, like forest fires. For instance, at being a virtual assistant to artists. Past exam papers: Computer Vision. Moreover, they further explore this idea with VGG and ResNet-50 models, showing evidence that CNNs rely extensively on local information, with minimal global reasoning. 415 Papers; 8 … Reason #2: Adversarial approaches are the best examples of multi-network models. Reason #1: In the paper, the authors mostly deal with standard machine learning problems (tabular data). In most cases, we have no problem in identifying a friend in an old photograph taken years ago. The project is good to understand how to detect objects with different kinds of sh… Brendel, Wieland, and Matthias Bethge. In 2012, the authors proposed the use of GPUs to train a large Convolutional Neural Network (CNN) for the ImageNet challenge. July 27, 2020 -- Check out our blog post for this year's list of invited speakers! For example:with a round shape, you can detect all the coins present in the image. Image-to-image translation with conditional adversarial networks.”, “Unpaired image-to-image translation using cycle-consistent adversarial networks.”. 2018. This paper, on the opposite, argues that a simple model, using current best practices, can be surprisingly effective. Wait until next year for these. Reason #3: The paper is math-heavy and uses a computationally derived proof. Further Reading: If interested in the Pose Estimation topic, you might consider reading this comprehensive state-of-the-art review. Here are the official Tensorflow 2 docs on the matter, Python Alone Won’t Get You a Data Science Job. found that if you train a big network, prune all low-valued weights, rollback the pruned network, and train again, you will get a better performing network. Facebook. 778 Papers; 16 Volumes; Computer Vision – ECCV 2018 Workshops. If you break an image into jigsaw-like pieces, scramble them, and show them to a kid, it won’t be able to recognize the original object; a CNN might. In contrast, the Transformer model is based solely on Attention layers, which are CNNs that capture the relevance of any sequence element to each other. Reason #1: Nowadays, most of the novel architectures in the Natural-Language Processing (NLP) literature descend from the Transformer. “Stop Thinking with Your Head,” and “Reformer” are two other good examples of this. Top Journals for Image Processing & Computer Vision. The military applications include the detection of enemy soldiers or vehicles, missile guidance, and creating battlefield awareness about a combat scene to reduce complexity and to fuse information from multiple sensors for supporting strategic decisions. We, normal folks, can’t. It helps detect tumors, arteriosclerosis, or other malign changes and measure organ dimensions, blood flow, etc. Curious to know more about computer vision? Understanding the Transformer is key to understanding most later models in NLP. As for the MobileNet discussion, elegance matters. Learn computer vision, machine learning, and image processing with OpenCV, CUDA, Caffe examples and tutorials written in C and Python. June 12, 2020 -- NeurIPS 2020 will be held entirely online. 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The International Conference on Learning Representations (ICLR) took place last week, and I had a pleasure to participate in it. While we all want to try the shiny and complicated novel architectures, a baseline model might be way faster to code and, yet, achieve similar results. See the chart above for more or the full listing for even more meetings. Edit: After writing this list, I compiled a second one with ten more AI papers read in 2020 and a third on GANs. Please let me know if there are any other papers you believe should be on this list. Klambauer, Günter, et al. This counts as a reason on its own. 16-385 Computer Vision, Spring 2020. Best Computer Vision Research Papers 2020 1. Artificial Intelligence is one of the most rapidly growing fields in science and is one of the most sought skills of the past few years, commonly labeled as Data Science. “Self-normalizing neural networks.” Advances in neural information processing systems. The COVID-19 pandemic has imposed unprecedented changes in our personal and professional lives. All levels of autonomy, ranging from semi-autonomous to fully autonomous vehicles such as submersibles, land-based robots, cars, trucks, UAVs, use computer vision-based systems to support drivers/pilots in various situations. Computer vision is notoriously tricky and challenging. San Diego, California, United States About Blog This blog is for programmers, hackers, engineers, scientists, students and self-starters who are interested in Computer Vision and Machine Learning. downsize regular models with minimal accuracy loss. Medical image processing is one most common application, where the data is extracted from images, such as microscopy images, X-ray images, angiography images, ultrasonic images, and tomography images, for the medical diagnosis of patients. Share your own research papers with us to be added to this list. I am new to research gate but feel this will be a good place to discuss project Ideas. It aims to build autonomous systems that can perform or even surpass the tasks associated with the human visual system, but what makes it extremely difficult to build such a system is because the human visual system is too good and sophisticated for many tasks in comparison with a computer vision system. COMPUTER VISION 23-28 August 2020. Continuing on the theoretical papers, Frankle et al. Reason #1: GAN papers are usually focused on the sheer quality of the generated results and place no emphasis on artistic control. The core idea behind MobileNet and other low-parameter models is to decompose expensive operations into a set of smaller (and faster) operations. Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild, by Shangzhe Wu, Christian... 3. Reason #2: High are the odds you are unaware of most approaches. Regular articles present major technical advances of broad general interest. Here are the official Tensorflow 2 docs on the matter. If you enjoyed reading this list, you might enjoy its continuations: Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Our method extends a state-of-the-art face mesh detector with two new components: a tiny neural network that predicts positions of the pupils in 2D, and a displacement-based estimation of the pupil blend shape coefficients. Feel free to download. Computer vision is notoriously tricky and challenging. As for the lottery hypothesis, the following is an easy to read review: Isola, Phillip, et al. This paper gives a comprehensive summary of several models size vs accuracy. Reason #3: The CycleGAN paper, in particular, demonstrates how an effective loss function can work wonders at solving some difficult problems. After it, other competitions took over the researchers’ attention. If you could go back in time and buy only the winning tickets, you would maximize your profits. Further Reading: I highly recommend reading the BERT and SAGAN paper. CTRL + SPACE for auto-complete. This list would not be complete without some GAN papers. “Attention is all you need.” Advances in neural information processing systems. Pinterest. “Bag of tricks for image classification with convolutional neural networks.”, this paper on class weights for unbalanced datasets, “Approximating cnns with bag-of-local-features models works surprisingly well on imagenet.”, “The lottery ticket hypothesis: Finding sparse, trainable neural networks.”. See our blog post for more information. Humans can recognize faces under all variations in terms of illumination, viewpoint, or expression. which might not always be the best option. 8-16 October; Amsterdam, The Netherlands; Computer Vision – ECCV 2016. Prior to this paper, language models relied extensively on Recurrent Neural Networks (RNN) to perform sequence-to-sequence tasks. Check it out :). Models such as GPT-2 and BERT are at the forefront of innovation. Twitter. “Reformer: The Efficient Transformer.” arXiv preprint arXiv:2001.04451 (2020). The Top Conferences Ranking for Computer Science & Electronics was prepared by Guide2Research, one of the leading portals for computer science research providing trusted data on scientific contributions since 2014. Survey articles offer critical reviews of the state of the art and/or tutorial presentations of pertinent topics. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. In manufacturing, computer vision is heavily used to find defects and measure the position and orientation of products to be picked up by a robot arm. Transformer / Attention models have attracted a lot of attention. While the literature on MobileNets addresses more efficient models, the research on NLP addresses more efficient training. Are you looking for GSEB 10th 2019, 2020 and old Years Question Papers? Although most papers I listed deal with image and text, many of their concepts are fairly input agnostic and provide insight far beyond vision and language tasks. Time: Mondays, Wednesdays noon - 1:20 pm: Location: Margaret Morrison A14: Instructor: Ioannis (Yannis) Gkioulekas: Teaching Assistants: Anand Bhoraskar, Prakhar Kulshreshtha: Course Description. Reason #1: While many believe that CNNs “see,” this paper shows evidence that they might be way dumber than we would dare to bet our money. One application of GANs that is not so well known (and you should check out) is semi-supervised learning. Further Reading: Related in its findings, the adversarial attacks literature also shows other striking limitations of CNNs. Consider reading this paper on class weights for unbalanced datasets. In the end, you will get a better performing network. The lottery analogy is seeing each weight as a “lottery ticket.” With a billion tickets, winning the prize is certain. Understanding the low-parameter networks is crucial to make your own models less expensive to train and use. However, these are often forgotten amid the major contributions. “Going back in time” is rolling-back to the initial untrained network and rerunning the lottery. Elegance matters. Both mentioned papers criticize the architecture, providing computationally efficient alternatives to the Attention module. WELCOME TO ECCV2020 . 2017. Reading about efficiency is the best way to ensure you are efficiently using your current resources. Further Reading: Following the history of ImageNet champions, you can read the ZF Net, VGG, Inception-v1, and ResNet papers. Reason #2: If you have to deal with tabular data, this is one of the most up-to-date approaches to the topic within the Neural Networks literature. How much more could be reduced by using the lottery technique? 8-14 September; Munich, Germany ; Computer Vision – ECCV 2018. If you have watched any webinar or online talks of computer science pioneer Andrew NG, you will notice that he always asks AI and ML enthusiasts to read research papers on emerging technologies. However, RNNs are awfully slow, as they are terrible to parallelize to multi-GPUs. Reason #3: While the transformer model has mostly been restricted to NLP, the proposed Attention mechanism has far-reaching applications. “Simple baselines for human pose estimation and tracking.” Proceedings of the European conference on computer vision (ECCV). Reading the AlexNet paper gives us a great deal of insight on how things developed since then. I can’t overstate that. A similar idea is given by the Focal loss paper, which considerably improves object detectors by just replacing their traditional losses for a better one. This paper collects a set of tips used throughout the literature and summarizes them for our reading pleasure. Reason #1: Being simple is sometimes the most effective approach. 2020 ECCV 2020. Print. Reason #3: These ideas also give us more perspective on how inefficient behemoth networks are. Best Paper Nomination arXiv code/models : PointRend: Image Segmentation as Rendering Alexander Kirillov, Yuxin Wu, Kaiming He, and Ross Girshick Computer Vision and Pattern Recognition (CVPR), 2020 (Oral) arXiv code/models : A Multigrid Method for Efficiently Training Video Models Chao-Yuan Wu, Ross Girshick, Kaiming He, … So far, most papers have proposed new techniques to improve the state-of-the-art. Get an update on which computer vision papers and researchers won awards. An open question is how much. Editorial - April 6, 2020. The ranking represents h-index, and Impact Score values gathered by November 10th 2020. “The lottery ticket hypothesis: Finding sparse, trainable neural networks.” arXiv preprint arXiv:1803.03635 (2018). The new deadline is Friday June 5, 2020 at 1pm PDT. Reason #2: The proposed network had 60 million parameters, complete insanity for 2012 standards. Deadline What is required Name Conference Date Location; Full Paper: CVPR 2021: June 19-25, 2021: Nashville, TN The deadlines below have passed. There seems to be no limit for us on how many faces we can store in our brains for future recognition. Yet, it does not need to be a one-way road. “Approximating cnns with bag-of-local-features models works surprisingly well on imagenet.” arXiv preprint arXiv:1904.00760 (2019). There seems no hope in building an autonomous system with such stellar performance. Therefore, models using SELU activations are simpler and need fewer operations. MobileNet is one of the most famous “low-parameter” networks. 50 research papers and resources in Computer Vision – Free Download. Take a look, “Imagenet classification with deep convolutional neural networks.”, “Mobilenets: Efficient convolutional neural networks for mobile vision applications.”. We also suggest key research papers in different areas that we think are representative of the latest advancements. Both perform the task of converting images from a domain A to a domain B and differ by leveraging paired and unpaired datasets. It publishes papers on research in areas of current interest to the readers, including but not limited to the following: Computer organizations and […] Reason #1: “Stop Thinking With Your Head” is a damn funny paper to read. “All You Need is a Good Init” is a seminal paper on the topic. Further Reading: While AI is growing fast, GANs are growing faster. As you are aware, we have decided to transform the face-to-face version of ECCV 2020 into an online event. Consider reading the MobileNet paper (if you haven’t already) for other takes on efficiency. This course provides a comprehensive introduction to computer vision. Topics of interest include all aspects of Pattern Recognition, not limited to the following detailed list: Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis 1. statistical, syntactic and structural pattern rec… Most data scientists deal primarily with images. Email. In practice, this renders batch normalization layers obsolete. Conditional models, such as these, provide an avenue for GANs to actually become useful in practice. Further Reading: Since these are late 2019 and 2020, there isn’t much to link. Further Reading: Weight initialization is an often overlooked topic. I highly recommend coding a GAN if you never have. You might be surprised by how familiar many of the concepts introduced in the paper are, such as dropout and ReLU. Though it was somewhat disappointing, computer vision has been offering several exciting applications in healthcare, manufacturing, defense, etc. However, I tried my best to select the most insightful and seminal works I have seen and read. The paper that introduced the Transformer Model. We remain committed to the ECCV series in 2020 and for this reason, we are committed to offer a fully … llll Wärmeleitpaste Test bzw. This paper, on the opposite, argues that a simple model, using current best practices, can be surprisingly effective. The former is a continuation of the Transformer model, and the latter is an application of the Attention mechanism to images in a GAN setup.

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