Artificial Intelligence Generated Content(AIGC), known for its superior visual results, represents a promising mitigation method for high-cost advertising applications. Numerous approaches have been developed to manipulate generated content under different conditions. However, a crucial limitation lies in the accurate description of products in advertising applications. Applying previous methods directly may lead to considerable distortion and deformation of advertised products, primarily due to oversimplified content control conditions. Hence, in this work, we propose a patch-enhanced mask encoder approach to ensure accurate product descriptions while preserving diverse backgrounds. Our approach consists of three components Patch Flexible Visibility, Mask Encoder Prompt Adapter and an image Foundation Model. Patch Flexible Visibility is used for generating a more reasonable background image. Mask Encoder Prompt Adapter enables region-controlled fusion. We also conduct an analysis of the structure and operational mechanisms of the Generation Module. Experimental results show our method can achieve the highest visual results and FID scores compared with other methods.
Example-guided image synthesis has been recently attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplary image serves to provide style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the previous models are designed on datasets with specific and roughly aligned objects. In this paper, we tackle a more challenging and general task, where the exemplar is an arbitrary scene image that is semantically unaligned to the given label map. To this end, we first propose a new Masked S…
IIT Delhi（德里印度理工学院）；Flipkart Internet Pvt. Ltd.（Flipkart互联网私人有限公司）；TIFR（塔塔基础研究院）
The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse. Auto-Encoders (AE) with regularized latent space provide an alternative framework for generative models, albeit their performance levels have not reached that of GANs. In this work, we hypothesise that the dimensionality of the AE model's latent space has a critical effect on the quality of generated data. Under the assumption that nature generates data by sampling from a "true" generative latent space follo…
Ankara University（安卡拉大学）；Hacettepe University（哈杰泰佩大学）
Facial image inpainting is a challenging problem as it requires generating new pixels that include semantic information for masked key components in a face, e.g., eyes and nose. Recently, remarkable methods have been proposed in this field. Most of these approaches use encoder-decoder architectures and have different limitations such as allowing unique results for a given image and a particular mask. Alternatively, some optimization-based approaches generate promising results using different masks with generator networks. However, these approaches are computationally more expensive. In this pa…
DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology
University of Glasgow（格拉斯哥大学）；Dotphoton；Fraunhofer HHI（弗劳恩霍夫海因里希·赫兹研究所）；Aignostics；Institute of Pathology, LMU Munich（慕尼黑大学病理学研究所）；UC Berkeley（加州大学伯克利分校）；TU Berlin（柏林工业大学）
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction
College of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；College of Electronic Information and Electrical Engineering, Shanghai Jiaotong University（上海交通大学电子信息与电气工程学院）；College of Information, Shanghai Ocean University（上海海洋大学信息学院）；College of Computer, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Huazhong University of Science and Technology（华中科技大学）
Image anomaly detection consists in detecting images or image portions that are visually different from the majority of the samples in a dataset. The task is of practical importance for various real-life applications like biomedical image analysis, visual inspection in industrial production, banking, traffic management, etc. Most of the current deep learning approaches rely on image reconstruction: the input image is projected in some latent space and then reconstructed, assuming that the network (mostly trained on normal data) will not be able to reconstruct the anomalous portions. However, t…
Novelty detection is the task of recognizing samples that do not belong to the distribution of the target class. During training, the novelty class is absent, preventing the use of traditional classification approaches. Deep autoencoders have been widely used as a base of many unsupervised novelty detection methods. In particular, context autoencoders have been successful in the novelty detection task because of the more effective representations they learn by reconstructing original images from randomly masked images. However, a significant drawback of context autoencoders is that random mask…
CoVisco: Codec-Native Vision Encoder with Native Token Compression for Unified Image-Video Understanding
ERNIE Team, Baidu Inc.（百度公司ERNIE团队）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）
The University of Sydney（悉尼大学）；Shandong University（山东大学）
Latest federated learning (FL) methods started to focus on how to use unlabeled data in clients for training due to users' privacy concerns, high labeling costs, or lack of expertise. However, current Federated Semi-Supervised/Self-Supervised Learning (FSSL) approaches fail to learn large-scale images because of the limited computing resources of local clients. In this paper, we introduce a new framework FedMAE, which stands for Federated Masked AutoEncoder, to address the problem of how to utilize unlabeled large-scale images for FL. Specifically, FedMAE can pre-train one-block Masked AutoEnc…
Nanjing University of Science and Technology（南京理工大学）；Microsoft Research（微软研究院）
Pre-training and fine-tuning, e.g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks. Inspired by the success of BERT, we propose MAsked Sequence to Sequence pre-training (MASS) for the encoder-decoder based language generation tasks. MASS adopts the encoder-decoder framework to reconstruct a sentence fragment given the remaining part of the sentence: its encoder takes a sentence with randomly masked fragment (several consecutive tokens) as input, and its decoder tries to predict…
