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computer vision papers 2020

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computer vision papers 2020

The experiments demonstrate its effectiveness compared to the existing state-of-the-art techniques. CVPR-2020 Paper-Statistics. ... As mentioned in part 1— the most important thing:) — I went through all the titles of NeurIPS 2020 papers (more than 1900!) Time: Mondays, Wednesdays noon - 1:20 pm: Location: Margaret ... Additional readings will be assigned from relevant papers. I can’t overstate that. 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. downsize regular models with minimal accuracy loss. The algorithm takes an RGB-D image as an input and generates a Layered Depth Image (LDI) with color and depth inpainted in the parts that were occluded in the input image: First, a trivial LDI is initialized with a single layer everywhere. After it, other competitions took over the researchers’ attention. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. Single Headed Attention RNN: Stop Thinking With Your Head, “Simple baselines for human pose estimation and tracking.”. The official repository of the paper on GitHub received over 2000 stars, making it one of the highest-trending papers in … Most of us use Batch Normalization layers and the ReLU or ELU activation functions. The introduced Transformer-based approach to image classification includes the following steps: splitting images into fixed-size patches; adding position embeddings to the resulting sequence of vectors; feeding the patches to a standard Transformer encoder; adding an extra learnable ‘classification token’ to the sequence. Finally, we show that they improve over existing set-learning architectures in a series of experiments with images, graphs, and point-clouds. Understanding the low-parameter networks is crucial to make your own models less expensive to train and use. The PyTorch implementation of this research, together with the pre-trained models, is available on. Prior to this paper, language models relied extensively on Recurrent Neural Networks (RNN) to perform sequence-to-sequence tasks. 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. When trained on large datasets of 14M–300M images, Vision Transformer approaches or beats state-of-the-art CNN-based models on image recognition tasks. Your email address will not be published. November 16-20, 2020 Paper deadline: Call for papers. the EfficientDet models are up to 3× to 8× faster on GPU/CPU than previous detectors. Further Reading: Since these are late 2019 and 2020, there isn’t much to link. To help you stay well prepared for 2020, we have summarized the latest trends across different research areas, including natural language processing, conversational AI, computer vision, and reinforcement learning. The paper received the Best Paper Award at ECCV 2020, one of the key conferences in computer vision. Nowadays, ImageNet is mainly used for Transfer Learning and to validate low-parameter models, such as: Howard, Andrew G., et al. “Mobilenets: Efficient convolutional neural networks for mobile vision applications.” arXiv preprint arXiv:1704.04861 (2017). “Going back in time” is rolling-back to the initial untrained network and rerunning the lottery. You can build a project to detect certain types of shapes. To decompose the image into depth, albedo, illumination, and viewpoint without direct supervision for these factors, they suggest starting by assuming objects to be symmetric. The approach is based on evaluating the discriminator and training the generator only using augmented images. Date: 2020/06/15 8:30AM-5:00PM The high accuracy and efficiency of the EfficientDet detectors may enable their application for real-world tasks, including self-driving cars and robotics. Reason #2: As for the Bag-of-Features paper, this sheds some light on how limited our current understanding of CNNs is. A very fascinating, informative blog, thank you for all the information and topics you have to offer. The authors released the code implementation of the suggested approach to 3D photo inpainting on, Examples of the resulting 3D photos in a wide range of everyday scenes can be viewed, Introducing a novel autoencoder architecture, called. November 16-20, 2020 Advances and challenges in face and gesture based security systems (ACFGSS 2020) Paper deadline: Extension Call for papers. 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. In this work, we present a tuning-free PnP proximal algorithm, which can automatically determine the internal parameters including the penalty parameter, the denoising strength and the terminal time. The update operator of RAFT is recurrent and lightweight, while the recent approaches are mostly limited to a fixed number of iterations. Thanks to their efficient pre-training and high performance, Transformers may substitute convolutional networks in many computer vision applications, including navigation, automatic inspection, and visual surveillance. If you like these research summaries, you might be also interested in the following articles: We’ll let you know when we release more summary articles like this one. We designed two autoencoders: one based on a MLP encoder, and another based on a StyleGAN generator, which we call StyleALAE. Therefore, models using SELU activations are simpler and need fewer operations. The research team presents a new learning-based approach to generating a 3D photo from a single RGB-D image. 69 benchmarks 1371 papers with code Tumor Segmentation. They introduce Vision Transformer (ViT), which is applied directly to sequences of image patches by analogy with tokens (words) in NLP. On Sintel (final pass), RAFT obtains an end-point-error of 2.855 pixels, a 30% error reduction from the best published result (4.098 pixels). Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results. An open question is how much. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Techniques and insights for applied deep learning (computer vision) from papers published at NeurIPS 2020. Details the science and engineering of the rapidly growing field of computer vision; Presents major technical advances of broad general interest 2017. The paper was accepted to CVPR 2020, the leading conference in computer vision. 12-18 October; Marseille, France; Computer Vision – ECCV 2008. This paper, on the opposite, argues that a simple model, using current best practices, can be surprisingly effective. ICCV 2015's Twenty One Hottest Research Papers This December in Santiago, Chile, the International Conference of Computer Vision 2015 is going to bring together the world's leading researchers in Computer Vision, Machine Learning, and Computer Graphics. The implementation code and demo are available on. However, when applied to GAN training, standard dataset augmentations tend to ‘leak’ into generated images (e.g., noisy augmentation leads to noisy results). The first results indicate that transformers achieve very promising results on image recognition tasks. However, these are often forgotten amid the major contributions. Further Reading: Weight initialization is an often overlooked topic. Similarly to Transformers in NLP, Vision Transformer is typically pre-trained on large datasets and fine-tuned to downstream tasks. So far, most papers have proposed new techniques to improve the state-of-the-art. CiteScore values are based on citation counts in a range of four years (e.g. “Unpaired image-to-image translation using cycle-consistent adversarial networks.” Proceedings of the IEEE international conference on computer vision. Reason #1: Nowadays, most of the novel architectures in the Natural-Language Processing (NLP) literature descend from the Transformer. It drastically reduced the size of the Transformer by improving the algorithm. The introduced approach consists of a pre-training stage, where both autoregressive and BERT objectives are explored, and a fine-tuning stage. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, called EfficientDet, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints. Please let me know if there are any other papers you believe should be on this list. TensorFlow implementation of iGPT by the OpenAI team is available, PyTorch implementation of the model is available, The researchers introduce a new deep network architecture for optical flow, called. StyleALAE can generate high-resolution (1024 × 1024) face and bedroom images of comparable quality to that of StyleGAN. While generation might not be your thing, reading about multi-network setups might be inspiring for a number of problems. Xiao, Bin, Haiping Wu, and Yichen Wei. Further Reading: Following the history of ImageNet champions, you can read the ZF Net, VGG, Inception-v1, and ResNet papers. Specific applications of GANs usually require images of a certain type that are not easily available in large numbers. Transformer / Attention models have attracted a lot of attention. One application of GANs that is not so well known (and you should check out) is semi-supervised learning. To avoid leaking, the NVIDIA researchers suggest evaluating the discriminator and training the generator only using augmented images. Additional Information The Computer Vision Laboratory is composed of three research groups working on the computer-based interpretation of 2D and 3D image data sets from conventional and non-conventional image sources. We demonstrate, through numerical and visual experiments, that the learned policy can customize different parameters for different states, and often more efficient and effective than existing handcrafted criteria. In my experience, using depth-wise convolutions can save you hundreds of dollars in cloud inference with almost no loss to accuracy. 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. Make learning your daily ritual. To implement the above optimizations, the autoencoder’s reciprocity is imposed in the latent space. The lottery analogy is seeing each weight as a “lottery ticket.” With a billion tickets, winning the prize is certain. CiteScore: 8.7 ℹ CiteScore: 2019: 8.7 CiteScore measures the average citations received per peer-reviewed document published in this title. Inspired by CVPR-2019-Paper-Statistics. Sometimes it is worthwhile to backtrack a bit and take a different turn. Edit: After writing this list, I compiled a second one with ten more AI papers read in 2020 and a third on GANs. By being “conditional,” these models allow users to have some degree of control over what is being generated by tweaking the inputs. First, raw images are resized to low resolution and reshaped into a 1D sequence. In addition, RAFT has strong cross-dataset generalization as well as high efficiency in inference time, training speed, and parameter count. The authors apply Transformer architecture to predict pixels instead of language tokens. In particular, they introduce an autoencoder, called Adversarial Latent Autoencoder (ALAE), that can generate images with quality comparable to state-of-the-art GANs while also learning a less entangled representation. Improving model performance under extreme lighting conditions and for extreme poses. The parameters are optimized with a reinforcement learning (RL) algorithm, where a high reward is given if the policy leads to faster convergence and better restoration accuracy. Code is available on https://github.com/google/automl/tree/master/efficientdet. Best Computer Vision Paper This surely isn’t an exhaustive list of great papers. Nowadays, we get to see models with over a billion parameters. GSEB 10th Exam Paper [2007-2020] All Years PDF by Chetan Lakhera GSEB 10th Exam Paper Download For Maths, Science & etc subjects From the Year 2007 to 2018, 2019, 2020 March and July Question Papers Solution PDF in Gujarati, English and Hindi Medium. That case is relevant when learning with sets of images, sets of point-clouds, or sets of graphs. How much more could be reduced by using the lottery technique? Domain adaptation for computer vision is the area of research, ... Paper submission due: Nov 02, 2020 Revision submission due: January 31, 2021 Final decision: March 31, 2021. So in this article, I have coalesced and created a list of Open-Source Computer Vision projects based on the various applications of computer vision. This, in itself, is a rare but beautiful thing to be seen. The proposed formulation achieved significantly better state-of-the-art results and trains markedly faster than previous RNN models. 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. Such compound operations are often orders-of-magnitude faster and use substantially fewer parameters. Here are the official Tensorflow 2 docs on the matter, Python Alone Won’t Get You a Data Science Job. The code implementation of this research paper. Brendel, Wieland, and Matthias Bethge. She "translates" arcane technical concepts into actionable business advice for executives and designs lovable products people actually want to use. The suggested approach enables images to be generated and manipulated with a high level of visual detail, and thus may have numerous applications in real estate, marketing, advertising, etc. 2020-2021 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics CVPR 2020 Acceptance rate (2016~2020) The total number of papers is increasing every year and this year has increased significantly! The extensive numerical and visual experiments demonstrate the effectiveness of the suggested approach on compressed sensing MRI and phase retrieval problems. Finally, the representations are learned by these objectives with linear probes or fine-tuning. The experiments demonstrate that these object detectors consistently achieve higher accuracy with far fewer parameters and multiply-adds (FLOPs). Reason #3: While the transformer model has mostly been restricted to NLP, the proposed Attention mechanism has far-reaching applications. Keeping up with everything is a massive endeavor and usually ends up being a frustrating attempt. The core technical novelty of the suggested approach lies in creating a completed Layered Depth Image representation using context-aware color and depth inpainting. The PnP algorithm introduced in this paper is tuning-free and can automatically determine internal parameters, including the penalty parameter, the denoising strength, and the terminal time. The paper is trending in the AI research community, as evident from the. 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 #2: Big companies can quickly scale their research to a hundred GPUs. 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. Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. The experiments demonstrate that RAFT achieves state-of-the-art performance on both Sintel and KITTI datasets. November 16-20, 2020 Computer Vision for Automatic Human Health Monitoring Paper deadline: Call for papers. “Self-normalizing neural networks.” Advances in neural information processing systems. We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. In practice, this renders batch normalization layers obsolete. 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. We propose a method for converting a single RGB-D input image into a 3D photo – a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. 8-14 September; Munich, Germany; Computer Vision – ECCV 2018. Achieving a new record Fréchet inception distance (FID) of 2.42 on CIFAR-10, compared to the previous state of the art of 5.59. Mariya is the co-author of Applied AI: A Handbook For Business Leaders and former CTO at Metamaven. Share. In particular, with single-model and single-scale, our EfficientDet-D7 achieves state-of-the-art 52.2 AP on COCO test-dev with 52M parameters and 325B FLOPs, being 4×–9× smaller and using 13×–42× fewer FLOPs than previous detectors. “Single Headed Attention RNN: Stop Thinking With Your Head.” arXiv preprint arXiv:1911.11423 (2019). Pix2Pix and CycleGAN are the two seminal works on conditional generative models. The introduced approach allows a significant reduction in the number of training images, which lowers the barrier for using GANs in many applied fields. 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. 2018. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. Reason #1: Being simple is sometimes the most effective approach. For instance, at being a virtual assistant to artists. Consider reading the following article (and its reference section): Frankle, Jonathan, and Michael Carbin. The same result holds for equivariant networks and equivariant DSS networks. Beyond transformers in vision applications, we also noticed a continuous interest in learning 3D objects from images, generating realistic images using GANs and autoencoders, etc. Models such as Self-Attention GAN demonstrate the usefulness of global-level reasoning a variety of tasks. The high level of interest in the code implementations of this paper makes this research. The introduced tuning-free PnP proximal algorithm can be applied to different inverse imaging problems, including magnetic resonance imaging (MRI), computed tomography (CT), microscopy, and inverse scattering. Models such as GPT-2 and BERT are at the forefront of innovation. Reason #2: Adversarial approaches are the best examples of multi-network models. To deal with the resulting complexity of the topology and the difficulty of applying a global CNN to the problem, the research team breaks the problem into many local inpainting sub-problems that are solved iteratively. An even larger model trained on a mixture of ImageNet and web images is competitive with self-supervised benchmarks on ImageNet, achieving 72.0% top-1 accuracy on a linear probe of our features. This paper gives a comprehensive summary of several models size vs accuracy. “All You Need is a Good Init” is a seminal paper on the topic. We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network architecture for optical flow. Will transformers revolutionize computer vision like they did with natural language processing? We hope that these research summaries will be a good starting point to help you understand the latest trends in this research area. Model efficiency has become increasingly important in computer vision. Further Reading: While AI is growing fast, GANs are growing faster. 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. “Training” is running the lottery and seeing which weights are high-valued. 2017. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. Reconstructing more complex objects by extending the model to use either multiple canonical views or a different 3D representation, such as a mesh or a voxel map. This paper reminds us that not all good models need to be complicated. The implementation code and models are available on, When applying Transformer architecture to images, the authors follow as closely as possible the design of the original. Two Vision Group papers accepted to the one of the top conference of computer vision - Asian Conference on Computer Vision (ACCV) 2020 Visually Guided Sound Source Separation using Cascaded Opponent Filter Network ( Zhu L. and Rahtu E. The research team from NVIDIA Research, Stanford University, and Bar Ilan University introduces a principled approach to learning such sets, where they first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements and then show that networks that are composed of these layers are universal approximators of both invariant and equivariant functions. Follow her on Twitter at @thinkmariya to raise your AI IQ. First, we propose a weighted bi-directional feature pyramid network (BiFPN), which allows easy and fast multi-scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. These research papers are the Open Access versions, provided by the Computer Vision Foundation. Paper evaluation and submission: Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. 2020’s Top AI & Machine Learning Research Papers, GPT-3 & Beyond: 10 NLP Research Papers You Should Read, Key Dialog Datasets: Overview and Critique, Task-Oriented Dialog Agents: Recent Advances and Challenges. CFP: Computer Vision for All Seasons (deadline December 10, 2019) (pdf, 87 kB) CFP: Computer Vision in the Wild (deadline December 20, 2019) (pdf, 422 kB) Proposal for a Special Issue An extensive range of numerical and visual experiments demonstrate that the introduced tuning-free PnP algorithm: outperforms state-of-the-art techniques by a large margin on the linear inverse imaging problem, namely compressed sensing MRI (especially under the difficult settings); demonstrates state-of-the-art performance on the non-linear inverse imaging problem, namely phase retrieval, where it produces cleaner and clearer results than competing techniques; often reaches a level of performance comparable to the “oracle” parameters tuned via the inaccessible ground truth. Want to Be a Data Scientist? To address this problem, the Google Research team introduces two optimizations, namely (1) a weighted bi-directional feature pyramid network (BiFPN) for efficient multi-scale feature fusion and (2) a novel compound scaling method. In my experience, most people stick to the defaults, which might not always be the best option.

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