VARGAN: Variance Enforcing Network Enhanced GAN

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suffer from unstable training process, and mode collapse. In this paper, we introduce a new GAN architecture called variance enforcing GAN (VARGAN), which incorporates a third network to introduce diversity in the generated samples. The third network measures the diversity of the generated samples, which is used to penalize the generator's loss for low diversity samples. The network is trained on the available training data and undesired distributions with limited modality. On a set of synthetic and real-world image data, VARGAN generates a more diverse set of samples compared to the recent state-of-the-art models. High diversity and low computational complexity, as well as fast convergence, make VARGAN a promising model to alleviate mode collapse.

CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels

Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated errors, labels carried over from previous errors and absence of input imagery along with class labels). In this work, we create a new semantic segmentation benchmark dataset, which we call CalCROP21, for the diverse crops in the Central Valley region of California at 10m spatial resolution using a Google Earth Engine based robust image processing pipeline and a novel attention based spatio-temporal semantic segmentation algorithm STATT. STATT uses re-sampled (interpolated) CDL labels for training, but is able to generate a better prediction than CDL by leveraging spatial and temporal patterns in Sentinel2 multi-spectral image series to effectively capture phenologic differences amongst crops and uses attention to reduce the impact of clouds and other atmospheric disturbances. We also present a comprehensive evaluation to show that STATT has significantly better results when compared to the resampled CDL labels. We have released the dataset and the processing pipeline code for generating the benchmark dataset.

Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts

Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image. For example a caption might reflect ironically on the image, so neither the caption nor the image is a mere transcript of the other. Instead they combine -- via what has been called meaning multiplication -- to create a new meaning that has a more complex relation to the literal meanings of text and image. Here we introduce a multimodal dataset of 1299 Instagram post labeled for three orthogonal taxonomies: the authorial intent behind the image-caption pair, the contextual relationship between the literal meanings of the image and caption, and the semiotic relationship between the signified meanings of the image and caption. We build a baseline deep multimodal classifier to validate the taxonomy, showing that employing both text and image improves intent detection by 8% compared to using only image modality, demonstrating the commonality of non-intersective meaning multiplication. Our dataset offers an important resource for the study of the rich meanings that results from pairing text and image.

Semantic sentence similarity: size does not always matter

This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge. We produce synthetic and natural spoken versions of a well known semantic textual similarity database and show that our VGS model produces embeddings that correlate well with human semantic similarity judgements. Our results show that a model trained on a small image-caption database outperforms two models trained on much larger databases, indicating that database size is not all that matters. We also investigate the importance of having multiple captions per image and find that this is indeed helpful even if the total number of images is lower, suggesting that paraphrasing is a valuable learning signal. While the general trend in the field is to create ever larger datasets to train models on, our findings indicate other characteristics of the database can just as important important.

Improving Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator

Adversarial examples can deceive a deep neural network (DNN) by significantly altering its response with imperceptible perturbations, which poses new potential vulnerabilities as the growing ubiquity of DNNs. However, most of the existing adversarial examples cannot maintain the malicious functionality if we apply an affine transformation on the resultant examples, which is an important measurement to the robustness of adversarial attacks for the practical risks. To address this issue, we propose an affine-invariant adversarial attack which can consistently construct adversarial examples robust over a distribution of affine transformation. To further improve the efficiency, we propose to disentangle the affine transformation into rotations, translations, magnifications, and reformulate the transformation in polar space. Afterwards, we construct an affine-invariant gradient estimator by convolving the gradient at the original image with derived kernels, which can be integrated with any gradient-based attack methods. Extensive experiments on the ImageNet demonstrate that our method can consistently produce more robust adversarial examples under significant affine transformations, and as a byproduct, improve the transferability of adversarial examples compared with the alternative state-of-the-art methods.

Decoupled Contrastive Learning

Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented ``views'' of the same image as positive to be pulled closer, and all other images negative to be pushed further apart. However, behind the impressive success of CL-based techniques, their formulation often relies on heavy-computation settings, including large sample batches, extensive training epochs, etc. We are thus motivated to tackle these issues and aim at establishing a simple, efficient, and yet competitive baseline of contrastive learning. Specifically, we identify, from theoretical and empirical studies, a noticeable negative-positive-coupling (NPC) effect in the widely used cross-entropy (InfoNCE) loss, leading to unsuitable learning efficiency with respect to the batch size. Indeed the phenomenon tends to be neglected in that optimizing infoNCE loss with a small-size batch is effective in solving easier SSL tasks. By properly addressing the NPC effect, we reach a decoupled contrastive learning (DCL) objective function, significantly improving SSL efficiency. DCL can achieve competitive performance, requiring neither large batches in SimCLR, momentum encoding in MoCo, or large epochs. We demonstrate the usefulness of DCL in various benchmarks, while manifesting its robustness being much less sensitive to suboptimal hyperparameters. Notably, our approach achieves $66.9\%$ ImageNet top-1 accuracy using batch size 256 within 200 epochs pre-training, outperforming its baseline SimCLR by $5.1\%$. With further optimized hyperparameters, DCL can improve the accuracy to $68.2\%$. We believe DCL provides a valuable baseline for future contrastive learning-based SSL studies.

Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review

The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classification leading to COVID-19 detection. The reviews of recent papers indicate that the highest classification accuracy of 99.80% is obtained when InstaCovNet-19 DL method is applied to an X-ray dataset of 361 COVID-19 patients, 362 pneumonia patients and 365 normal people. Furthermore, it can be seen that the best classification accuracy of 99.054% can be achieved when EDL_COVID DL method is applied to a CT image dataset of 7500 samples where COVID-19 patients, lung tumor patients and normal people are equal in number. Moreover, we illustrate the potential DL techniques in drug or vaccine discovery in combating the coronavirus. Finally, we address a number of problems, concerns and future research directions relevant to DL applications for COVID-19.

Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis

Deep person generation has attracted extensive research attention due to its wide applications in virtual agents, video conferencing, online shopping and art/movie production. With the advancement of deep learning, visual appearances (face, pose, cloth) of a person image can be easily generated or manipulated on demand. In this survey, we first summarize the scope of person generation, and then systematically review recent progress and technical trends in deep person generation, covering three major tasks: talking-head generation (face), pose-guided person generation (pose) and garment-oriented person generation (cloth). More than two hundred papers are covered for a thorough overview, and the milestone works are highlighted to witness the major technical breakthrough. Based on these fundamental tasks, a number of applications are investigated, e.g., virtual fitting, digital human, generative data augmentation. We hope this survey could shed some light on the future prospects of deep person generation, and provide a helpful foundation for full applications towards digital human.

Learning Coded Apertures for Time-Division Multiplexing Light Field Display

Conventional stereoscopic displays suffer from vergence-accommodation conflict which cause visual fatigue. Integral imaging-based (II) displays resolves this problem by directly projecting light field sub-views into the eye using microlens arrays. However, II-based light field displays has inherent trade-off between angular and spatial resolutions. In this paper, we propose a novel display concept called coded time-division light field display (C-TDM-LFD), which projects encoded light field sub-views to the viewers' eyes, offering correct cues for vergence-accommodation reflex. By jointly optimizing display inputs and pattern of coded apertures, our pipeline can render high resolution refocused images from sparse light field sub-views with minimal aliasing effects. By simulating light transport and image formation with Fourier optics, we can learn display inputs and coded aperture patterns via deep learning in an end-to-end fashion. To our knowledge, we are among the first to optimize the light field display pipeline with deep learning. We verify our concept with objective image quality metrics (PSNR, SSIM) and optics software simulation, and perform extensive studies on the various customizable design variables in our display pipeline. Experiments results show that our method can generate refocused images with higher quality both quantitatively and qualitatively compared to baseline display designs.

Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting

Both generalized and incremental few-shot learning have to deal with three major challenges: learning novel classes from only few samples per class, preventing catastrophic forgetting of base classes, and classifier calibration across novel and base classes. In this work we propose a three-stage framework that allows to explicitly and effectively address these challenges. While the first phase learns base classes with many samples, the second phase learns a calibrated classifier for novel classes from few samples while also preventing catastrophic forgetting. In the final phase, calibration is achieved across all classes. We evaluate the proposed framework on four challenging benchmark datasets for image and video few-shot classification and obtain state-of-the-art results for both generalized and incremental few shot learning.