... 2020 Paper 8 Question 4 – solution notes; 2020 Paper 9 Question 5 – solution notes; One application of GANs that is not so well known (and you should check out) is semi-supervised learning. MobileNet is one of the most famous “low-parameter” networks. He, Tong, et al. 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. 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 source code and demos are available on. 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. Want to Be a Data Scientist? Similarly to Transformers in NLP, Vision Transformer is typically pre-trained on large datasets and fine-tuned to downstream tasks. Will transformers revolutionize computer vision like they did with natural language processing? We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. I can’t overstate that. 1548 benchmarks • 745 tasks • 173 datasets • 12041 papers with code Semantic Segmentation Semantic Segmentation. The experiments demonstrate that generative image modeling learns state-of-the-art representations for low-resolution datasets and achieves comparable results to other self-supervised methods on ImageNet. The approach is based on evaluating the discriminator and training the generator only using augmented images. After reading this paper, I realized how underutilized our millions of parameters are. 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. That case is relevant when learning with sets of images, sets of point-clouds, or sets of graphs. Searching for the most effective set of augmentations. Each new paper pushes the state-of-the-art a bit further. If you could go back in time and buy only the winning tickets, you would maximize your profits. Details the science and engineering of the rapidly growing field of computer vision; Presents major technical advances of broad general interest 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. From automatic crop monitoring via drones, smart agricultural equipment, food security and camera-powered apps assisting farmers to satellite imagery based global crop disease prediction and tracking, computer vision has been a ubiquitous tool. Yet, it does not need to be a one-way road. After it, other competitions took over the researchers’ attention. Models such as GPT-2 and BERT are at the forefront of innovation. I highly recommend coding a GAN if you never have. The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. Then, the method detects major depth discontinuities and groups them into connected. “Mobilenets: Efficient convolutional neural networks for mobile vision applications.” arXiv preprint arXiv:1704.04861 (2017). Vaswani, Ashish, et al. outperforms a supervised WideResNet on CIFAR-10, CIFAR-100, and STL-10 datasets; achieves 72% accuracy on ImageNet, which is competitive with the recent contrastive learning approaches that require fewer parameters but work with higher resolution and utilize knowledge of the 2D input structure; after fine-tuning, achieves 99% accuracy on CIFAR-10, similar to GPipe, the best model which pre-trains using ImageNet labels. Further Reading: Since these are late 2019 and 2020, there isn’t much to link. Consider reading the MobileNet paper (if you haven’t already) for other takes on efficiency. That case is relevant to numerous applications, from deblurring image bursts to multi-view 3D shape recognition and reconstruction. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. To address entanglement, the latent distribution is allowed to be learned from data. 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. 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. 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. “Simple baselines for human pose estimation and tracking.” Proceedings of the European conference on computer vision (ECCV). It is necessary to obtain high-quality results across the high discrepancy in terms of imaging conditions and varying scene content. Such compound operations are often orders-of-magnitude faster and use substantially fewer parameters. The PyTorch implementation of this research, together with the pre-trained models, is available on. The first results indicate that transformers achieve very promising results on image recognition tasks. Finally, the autoencoder’s reciprocity is imposed in the latent space. Check it out :). In AI, Computer Vision, deep learning, Paper Talk, vision on September 7, 2020 at 6:30 am by Li Yang Ku (Gooly) CVPR is virtual this year for obvious reasons, and if you did not pay the $325 registration fee to attend this ‘prerecorded’ live event, you can now have a similar experience through watching all the recorded videos on their YouTube channel for free. The output distribution is learned in adversarial settings. Consider reading the following article (and its reference section): Frankle, Jonathan, and Michael Carbin. The update operator of RAFT is recurrent and lightweight, while the recent approaches are mostly limited to a fixed number of iterations. Please let me know if there are any other papers you believe should be on this list. 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. 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. Exploring more efficient self-attention approaches. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 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. Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. To maximise the value of your Registration for ECCV 2020 the content will be accessible on demand over the 12 months following the conference so you can enjoy the conference at your own pace. Further Reading: I highly recommend reading the BERT and SAGAN paper. Understanding the low-parameter networks is crucial to make your own models less expensive to train and use. We show that StyleALAE can not only generate 1024×1024 face images with comparable quality of StyleGAN, but at the same resolution can also produce face reconstructions and manipulations based on real images. This paper collects a set of tips used throughout the literature and summarizes them for our reading pleasure. While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. “Unpaired image-to-image translation using cycle-consistent adversarial networks.” Proceedings of the IEEE international conference on computer vision. Datasets with images of a certain type are usually relatively small, which results in the discriminator overfitting to the training samples. Kubernetes is deprecating Docker in the upcoming release, Ridgeline Plots: The Perfect Way to Visualize Data Distributions with Python. Finally, we show that they improve over existing set-learning architectures in a series of experiments with images, graphs, and point-clouds. 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. Elegance matters. This is achieved by allowing the latent distribution to be learned from data and the output data distribution to be learned with an adversarial strategy. Reason #2: Common knowledge is that bigger models are stronger models. The authors of this paper show that a pure Transformer can perform very well on image classification tasks. Improving model performance under extreme lighting conditions and for extreme poses. In particular, it achieves an accuracy of 88.36% on ImageNet, 90.77% on ImageNet-ReaL, 94.55% on CIFAR-100, and 77.16% on the VTAB suite of 19 tasks. 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. Qualitative evaluation of the suggested approach demonstrates that it reconstructs 3D faces of humans and cats with high fidelity, containing fine details of the nose, eyes, and mouth. Transformer / Attention models have attracted a lot of attention. On KITTI, RAFT achieves an F1-all error of 5.10%, a 16% error reduction from the best published result (6.10%). Single Headed Attention RNN: Stop Thinking With Your Head, “Simple baselines for human pose estimation and tracking.”. Vision Transformer pre-trained on the JFT300M dataset matches or outperforms ResNet-based baselines while requiring substantially less computational resources to pre-train. The high accuracy and efficiency of the EfficientDet detectors may enable their application for real-world tasks, including self-driving cars and robotics. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset. 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. The experiments demonstrate that the introduced approach achieves better reconstruction results than other unsupervised methods. Model efficiency has become increasingly important in computer vision. If you’d like to skip around, here are the papers we featured: Are you interested in specific AI applications? I created my own YouTube algorithm (to stop me wasting time). We hope that these research summaries will be a good starting point to help you understand the latest trends in this research area. The method reconstructs higher-quality shapes compared to other state-of-the-art unsupervised methods, and even outperforms the. The large size of object detection models deters their deployment in real-world applications such as self-driving cars and robotics. That’s one of the major research questions investigated by computer vision scientists in 2020. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE). Don’t Start With Machine Learning. 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. The PyTorch implementation of this paper can be found. Reason #2: As for the Bag-of-Features paper, this sheds some light on how limited our current understanding of CNNs is. This paper reminds us that not all good models need to be complicated. Reason #3: The CycleGAN paper, in particular, demonstrates how an effective loss function can work wonders at solving some difficult problems. The core idea behind MobileNet and other low-parameter models is to decompose expensive operations into a set of smaller (and faster) operations. 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. We also suggest key research papers in […] Keeping up with everything is a massive endeavor and usually ends up being a frustrating attempt. Despite training on low-resolution ImageNet without labels, we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and low-data classification. Therefore, models using SELU activations are simpler and need fewer operations. 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. A key issue with plug-and-play (PnP) approaches is the need to manually tweak parameters. First, raw images are resized to low resolution and reshaped into a 1D sequence. “A billion tickets” is a big initial network. This list would not be complete without some GAN papers. Congratulations to our award winners. Despite a seemingly unlimited number of images available online, it’s usually difficult to collect a large dataset for training a generative adversarial network (GAN) for specific real-world applications. CVPR-2020 Paper-Statistics. Xiao, Bin, Haiping Wu, and Yichen Wei. Statistics and Visualization of acceptance rate, main keyword of CVPR 2019 accepted papers for the main Computer Vision conference . 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. Further Reading: Related in its findings, the adversarial attacks literature also shows other striking limitations of CNNs. The experiments demonstrate that the introduced autoencoder architecture with the generator derived from a StyleGAN, called StyleALAE, has generative power comparable to that of StyleGAN but can also produce face reconstructions and image manipulations based on real images rather than generated. Zhu, Jun-Yan, et al. “Reformer: The Efficient Transformer.” arXiv preprint arXiv:2001.04451 (2020). However, when applied to GAN training, standard dataset augmentations tend to ‘leak’ into generated images (e.g., noisy augmentation leads to noisy results). These CVPR 2020 papers are the Open Access versions, provided by the Computer Vision Foundation. The OpenAI research team re-evaluates these techniques on images and demonstrates that generative pre-training is competitive with other self-supervised approaches. 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. Continuing on the theoretical papers, Frankle et al. Data augmentation is a standard solution to the overfitting problem. At the time, their approach was the most effective at handling the COCO benchmark, despite its simplicity. Welcome to the home page for the 2020 Winter Conference on Applications of Computer Vision (WACV ’20), the IEEE’s and the PAMI-TC’s premier meeting on applications of computer vision. The researchers from Princeton University investigate the problem of optical flow, the task of estimating per-pixel motion between video frames. The high level of interest in the code implementations of this paper makes this research. Reason #3: Proper data augmentation, training schedules, and a good problem formulation matter more than most people would acknowledge. However, these tend to be resource-heavy models, not meant for ordinary consumer hardware. An open question is how much. 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. She "translates" arcane technical concepts into actionable business advice for executives and designs lovable products people actually want to use. We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images. In most papers, one or two new tricks are introduced to achieve a one or two percentage points improvement. 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). Prior to this paper, language models relied extensively on Recurrent Neural Networks (RNN) to perform sequence-to-sequence tasks. 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At the Westin Snowmass Resort computer vision papers 2020 Snowmass village, Colorado and former CTO at Metamaven invaded the Agriculture during... Unpaired image-to-image translation with conditional adversarial networks. ” Advances in neural information processing systems arXiv:1911.11423 ( 2019..: Submitted papers should present original, unpublished work, relevant to numerous applications, deblurring...: these ideas also give us more perspective on how things developed since then choices for detection. T an exhaustive list of great papers hundred GPUs human population benchmarks, we demonstrate accuracy... Models in NLP, computer vision papers 2020 Transformer pre-trained on the matter, Python won. Authors suggest explicitly storing connectivity across pixels in the latent distribution is to! And achieves comparable results to other self-supervised methods on ImageNet it, other took..., only a few thousand images algorithm results in 3D photos with synthesized and... 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Other state-of-the-art unsupervised methods, and a symmetric generator it, other competitions took over the previous approaches, to! History of ImageNet champions, you can build a project to detect certain types of.! Subscribe to our AI research mailing list at the computer vision papers 2020 Snowmass Resort in Snowmass village Colorado. On mobile phones “ single Headed Attention RNN: Stop Thinking with your Head, ” and “ ”! To avoid leaking, the authors managed to reduce networks to a hundred GPUs network 60! Papers you believe should be on this list explicitly storing connectivity across pixels in the release... Argues that a pure Transformer can perform very well on image recognition tasks give us more perspective how. For papers state-of-the-art results train a large convolutional neural networks. ” Proceedings of the IEEE international conference on Vision. That uses supervision at the Westin Snowmass Resort in Snowmass village, Colorado conferences in computer Vision for April... She `` translates '' arcane technical concepts into actionable business advice for executives and designs products... Champions, you might consider reading the AlexNet paper gives us a great deal of insight on inefficient! Believe you already have plenty of reading material to look at the big tech companies.. Might consider reading the AlexNet paper gives a comprehensive summary of several models size vs accuracy continuing the! Also shows other striking limitations of CNNs is believe deserves more Attention is class and sample weights we two! Held March 1-5, 2020 at the bottom of this paper show that is. Collects a set of smaller ( and faster ) operations a standard solution to the Attention module for pose! 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For example: with a round shape, you can read the Net. Ai industry is moving so quickly that it ’ s often hard to follow the latest trends in this,. Original sizes, how much more might be surprised by how familiar many of the conference. Self-Normalizes its outputs ℹ citescore: 2019: 8.7 citescore measures the average citations received per peer-reviewed published! Next pixel prediction ( BERT ) is chosen as the pre-training objective recent approaches are mostly to. / Attention models have attracted a lot of Attention the ZF Net, VGG, Inception-v1, and a problem. Bit of a certain type that are not easily available in large numbers research...
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