DAQE: Enhancing the Quality of Compressed Images by Finding the Secret of Defocus

Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality. Unfortunately, the existing quality enhancement approaches for compressed images neglect the inherent characteristic of defocus, resulting in inferior performance. This paper finds that in compressed images, the significantly defocused regions are with better compression quality and two regions with different defocus values possess diverse texture patterns. These findings motivate our defocus-aware quality enhancement (DAQE) approach. Specifically, we propose a novel dynamic region-based deep learning architecture of the DAQE approach, which considers the region-wise defocus difference of compressed images in two aspects. (1) The DAQE approach employs fewer computational resources to enhance the quality of significantly defocused regions, while more resources on enhancing the quality of other regions; (2) The DAQE approach learns to separately enhance diverse texture patterns for the regions with different defocus values, such that texture-wise one-on-one enhancement can be achieved. Extensive experiments validate the superiority of our DAQE approach in terms of quality enhancement and resource-saving, compared with other state-of-the-art approaches

Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks

Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, existing synonym substitution based adversarial attacks are effective but not efficient to be incorporated into practical text adversarial training. Gradient-based attacks, which are very efficient for images, are hard to be implemented for synonym substitution based text attacks due to the lexical, grammatical and semantic constraints and the discrete text input space. Thereby, we propose a fast text adversarial attack method called Fast Gradient Projection Method (FGPM) based on synonym substitution, which is about 20 times faster than existing text attack methods and could achieve similar attack performance. We then incorporate FGPM with adversarial training and propose a text defense method called Adversarial Training with FGPM enhanced by Logit pairing (ATFL). Experiments show that ATFL could significantly improve the model robustness and block the transferability of adversarial examples.Comment: Accepted by AAAI 2021, code is available at https://github.com/JHL-HUST/FGP

Evaluation Index System for Railway Hub Logistics Base System Layout Planning, Taking Hefei Railway Hub in China as an Example

The construction of a railway hub logistics base system has many influencing factors and a high construction cost. There is an extremely important social and economic significance to the evaluation of its planning scheme. This study has obtained practical experience from a large number of existing railway hub planning schemes in China, using the analytic hierarchy process. Then, macro- and micro-level layout planning principles were analyzed. Moreover, 16 evaluation indicators were established at the macro and micro levels. The analytic hierarchy process and a comprehensive evaluation index method were used to deal with all indicators and give the score of the planning scheme. Lastly, Hefei railway hub in China was taken as an example to test the theory above

ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning

Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. The significant challenges for deep learning-based image fusion algorithms are the lack of a definitive ground truth as well as the corresponding distance measurement, with current manually given loss functions constrain the flexibility of model and generalizability for unified fusion tasks. To overcome these limitations, we introduce a unified image fusion framework based on meta-learning, named ReFusion, which provides a learning paradigm that obtains the optimal fusion loss for various fusion tasks based on reconstructing the source images. Compared to existing methods, ReFusion employs a parameterized loss function, dynamically adjusted by the training framework according to the specific scenario and task. ReFusion is constituted by three components: a fusion module, a loss proposal module, and a source reconstruction module. To ensure the fusion module maximally preserves the information from the source images, enabling the reconstruction of the source images from the fused image, we adopt a meta-learning strategy to train the loss proposal module using reconstruction loss. The update of the fusion module relies on the fusion loss proposed by the loss proposal module. The alternating updates of the three modules mutually facilitate each other, aiming to propose an appropriate fusion loss for different tasks and yield satisfactory fusion results. Extensive experiments demonstrate that ReFusion is capable of adapting to various tasks, including infrared-visible, medical, multi-focus, and multi-exposure image fusion. The code will be released

Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching

Correlation based stereo matching has achieved outstanding performance, which pursues cost volume between two feature maps. Unfortunately, current methods with a fixed model do not work uniformly well across various datasets, greatly limiting their real-world applicability. To tackle this issue, this paper proposes a new perspective to dynamically calculate correlation for robust stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios. Specifically, a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation. Additionally, we improve the traditional non-parametric warping with learnable parameters, such that the position-specific weights can be learned. We show that by empowering the recurrent network with the UGAC module, stereo matching can be exploited more robustly and effectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury datasets when employing the same fixed model over these datasets without any retraining procedure. To target real-time applications, we further design a lightweight model based on UGAC, which also outperforms other methods over KITTI benchmarks with only 0.6 M parameters.Comment: Accepted by ICCV202

VersaT2I: Improving Text-to-Image Models with Versatile Reward

Recent text-to-image (T2I) models have benefited from large-scale and high-quality data, demonstrating impressive performance. However, these T2I models still struggle to produce images that are aesthetically pleasing, geometrically accurate, faithful to text, and of good low-level quality. We present VersaT2I, a versatile training framework that can boost the performance with multiple rewards of any T2I model. We decompose the quality of the image into several aspects such as aesthetics, text-image alignment, geometry, low-level quality, etc. Then, for every quality aspect, we select high-quality images in this aspect generated by the model as the training set to finetune the T2I model using the Low-Rank Adaptation (LoRA). Furthermore, we introduce a gating function to combine multiple quality aspects, which can avoid conflicts between different quality aspects. Our method is easy to extend and does not require any manual annotation, reinforcement learning, or model architecture changes. Extensive experiments demonstrate that VersaT2I outperforms the baseline methods across various quality criteria

Farm-waste-derived recyclable photothermal evaporator

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tian, Y., Liu, X., Li, J., Deng, Y., DeGiorgis, J. A., Zhou, S., Caratenuto, A., Minus, M. L., Wan, Y., Xiao, G., & Zheng, Y. Farm-waste-derived recyclable photothermal evaporator. Cell Reports Physical Science, 2(9), (2021): 100549, https://doi.org/10.1016./j.xcrp.2021.100549Interfacial solar steam generation is emerging as a promising technique for efficient desalination. Although increasing efforts have been made, challenges exist for achieving a balance among a plethora of performance indicators—for example, rapid evaporation, durability, low-cost deployment, and salt rejection. Here, we demonstrate that carbonized manure can convert 98% of sunlight into heat, and the strong capillarity of porous carbon fibers networks pumps sufficient water to evaporation interfaces. Salt diffusion within microchannels enables quick salt drainage to the bulk seawater to prevent salt accumulation. With these advantages, this biomass-derived evaporator is demonstrated to feature a high evaporation rate of 2.81 kg m−2 h−1 under 1 sun with broad robustness to acidity and alkalinity. These advantages, together with facial deployment, offer an approach for converting farm waste to energy with high efficiency and easy implementation, which is particularly well suited for developing regions.This project is supported by the National Science Foundation through grant no. CBET-1941743. This project is based upon work supported in part by the National Science Foundation under EPSCoR Cooperative Agreement no. OIA-1655221

Characteristics of slamming pressure and force for trimaran hull

In this paper, the characteristics of the impact pressure and force of a trimaran section was studied by Computational Fluid Dynamics (CFD). The time domain features of the slamming pressure or force showed a strong correlation with the penetration depth regardless of the specific ways of water entry. The effects of velocity and acceleration on the impact pressure and force were analyzed. It was found that the initial impact of the main hull and the wet-deck slamming were predominantly affected by the entry velocities, whilst the acceleration had almost no effect for initial impact. The impact velocity presented a quadratic relation with slamming pressure/forces, and the relation between acceleration and wet-deck slamming pressure/force was linear. These were consistent with the patterns implied by analytical models such as the Wagner or MLM (Modified Logvinovich model) theories