Studies involving colourising images has been garnering researchers' keen attention over time, assisted by significant advances in various Machine Learning techniques and compute power availability. Traditionally, colourising images have been an intricate task that gave a substantial degree of freedom during the assignment of chromatic information. In our proposed method, we attempt to colourise images using Vision Transformer - Inception - Generative Adversarial Network (ViT-I-GAN), which has an Inception-v3 fusion embedding in the generator. For a stable and robust network, we have used Vision Transformer (ViT) as the discriminator. We trained the model on the Unsplash and the COCO dataset for demonstrating the improvement made by the Inception-v3 embedding. We have compared the results between ViT-GANs with and without Inception-v3 embedding.
Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learnin…
Implicit generative models, which do not return likelihood values, such as generative adversarial networks and diffusion models, have become prevalent in recent years. While it is true that these models have shown remarkable results, evaluating their performance is challenging. This issue is of vital importance to push research forward and identify meaningful gains from random noise. Currently, heuristic metrics such as the Inception score (IS) and Frechet Inception Distance (FID) are the most common evaluation metrics, but what they measure is not entirely clear. Additionally, there are quest…
National University of Singapore（新加坡国立大学）；Tencent PCG（腾讯平台与内容事业群）
Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper, we surprisingly find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead, learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability …
It is abundantly clear that time dependent data is a vital source of information in the world. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. This work attempts to ease the frustration by proposing a new …
Texas A&M University（德克萨斯农工大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks. In this paper, we present the first preliminary study on introducing the NAS algorithm to generative adversarial networks (GANs), dubbed AutoGAN. The marriage of NAS and GANs faces its unique challenges. We define the search space for the generator architectural variations and use an RNN controller to guide the search, with parameter sharing and dynamic-resetting to accelerate the process. Inception score is adopted as the reward, and a multi-level search strategy i…
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this problem, we propose a new SSL pipeline, consisting of first un/self-supervised pre-training, followed by supervised fine-tuning, and finally semi-supervised fine-tuning. At the semi-supervised fine-tuning stage, we adopt an exponential moving average (EMA)-Teacher framework instead of the popular FixMatch, since the former is more stable and delivers higher accuracy for semi-supervised vision transformers. In additi…
Department of Statistics（统计系）；London School of Economics（伦敦政治经济学院）
The main idea of this paper is to explore the possibilities of generating samples from the neural networks, mostly focusing on the colorization of the grey-scale images. I will compare the existing methods for colorization and explore the possibilities of using new generative modeling to the task of colorization. The contributions of this paper are to compare the existing structures with similar generating structures(Decoders) and to apply the novel structures including Conditional VAE(CVAE), Conditional Wasserstein GAN with Gradient Penalty(CWGAN-GP), CWGAN-GP with L1 reconstruction loss, Adv…
In this work, a real-time hand gesture recognition system-based human-computer interface (HCI) is presented. The system consists of six stages: (1) hand detection, (2) gesture segmentation, (3) use of five pre-trained convolutional neural network models (CNN) and vision transformer (ViT), (4) building an interactive human-machine interface (HMI), (5) development of a gesture-controlled virtual mouse, (6) use of Kalman filter to estimate the hand position, based on that the smoothness of the motion of pointer is improved. In our work, five pre-trained CNN (VGG16, VGG19, ResNet50, ResNet101, and…
Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Harvard Medical School（哈佛医学院）
A critical factor that influences the success of an in-vitro fertilization (IVF) procedure is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories. Network architectures and hyper-parameters affect the efficiency of CNNs for any given task. Here, …
University of Texas at Austin（德克萨斯大学奥斯汀分校）；UC Santa Barbara（加州大学圣塔芭芭拉分校）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
The recent explosive interest on transformers has suggested their potential to become powerful "universal" models for computer vision tasks, such as classification, detection, and segmentation. While those attempts mainly study the discriminative models, we explore transformers on some more notoriously difficult vision tasks, e.g., generative adversarial networks (GANs). Our goal is to conduct the first pilot study in building a GAN completely free of convolutions, using only pure transformer-based architectures. Our vanilla GAN architecture, dubbed TransGAN, consists of a memory-friendly tran…
In this paper, we conduct a study on the state-of-the-art methods for text-to-image synthesis and propose a framework to evaluate these methods. We consider syntheses where an image contains a single or multiple objects. Our study outlines several issues in the current evaluation pipeline: (i) for image quality assessment, a commonly used metric, e.g., Inception Score (IS), is often either miscalibrated for the single-object case or misused for the multi-object case; (ii) for text relevance and object accuracy assessment, there is an overfitting phenomenon in the existing R-precision (RP) and …
HP-GAN: Harnessing pretrained networks for GAN improvement with FakeTwins and discriminator consistency
School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea（电气电子工程学院，延世大学，首尔，大韩民国）；Robotics Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea（机器人研究所，韩国科学技术院，首尔，大韩民国）；Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea（口腔和颌面放射科，延世大学牙科学院，首尔，大韩民国）
Dartmouth College（达特茅斯学院）；UTSouthwestern Medical Center（德克萨斯大学西南医学中心）；Manchester Metropolitan University（曼彻斯特城市大学）
Rapid growth in the development of medical imaging analysis technology has been propelled by the great interest in improving computer-aided diagnosis and detection (CAD) systems for three popular image visualization tasks: classification, segmentation, and Region of Interest (ROI) detection. However, a limited number of datasets with ground truth annotations are available for developing segmentation and ROI detection of lesions, as expert annotations are laborious and expensive. Detecting the ROI is vital to locate lesions accurately. In this paper, we propose the use of two deep object detect…
An Investigation of Visual Foundation Models Robustness
Centre for Secure Information Technologies (CSIT)（安全信息科技中心）；Queen’s University Belfast（贝尔法斯特女王大学）；Department of Information Engineering and Computer Science（信息工程与计算机科学系）；University of Trento（特伦托大学）
