Dalian University of Technology

About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.

Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset

Abstract: Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. First, existing synthesized rain datasets have only limited realism, in terms of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images, which makes the current evaluation less objective. The core challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways. First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images. Using this method, we construct a large-scale dataset of ∼29.5K rain/rain-free image pairs that covers a wide range of natural rain scenes. Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.

A Nonmetal Plasmonic Z-Scheme Photocatalyst with UV- to NIR-Driven Photocatalytic Protons Reduction.

Abstract: Ultrabroad-spectrum absorption and highly efficient generation of available charge carriers are two essential requirements for promising semiconductor-based photocatalysts, towards achieving the ultimate goal of solar-to-fuel conversion. Here, a fascinating nonmetal plasmonic Z-scheme photocatalyst with the W18 O49 /g-C3 N4 heterostructure is reported, which can effectively harvest photon energies spanning from the UV to the nearinfrared region and simultaneously possesses improved charge-carrier dynamics to boost the generation of long-lived active electrons for the photocatalytic reduction of protons into H2 . By combining with theoretical simulations, a unique synergistic photocatalysis effect between the semiconductive Z-scheme charge-carrier separation and metal-like localized-surface-plasmon-resonance-induced "hot electrons" injection process is demonstrated within this binary heterostructure.

Pose-Invariant Embedding for Deep Person Re-Identification

Abstract: Pedestrian misalignment, which mainly arises from detector errors and pose variations, is a critical problem for a robust person re-identification (re-ID) system. With poor alignment, the feature learning and matching process might be largely compromised. To address this problem, this paper introduces pose-invariant embedding (PIE) as a pedestrian descriptor. First, in order to align pedestrians to a standard pose, the PoseBox structure is introduced, which is generated through pose estimation followed by affine transformations. Second, to reduce the impact of pose estimation errors and information loss during the PoseBox construction, we design a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input. The proposed PIE descriptor is thus defined as the fully connected layer of the PBF network for the retrieval task. Experiments are conducted on the Market-1501, CUHK03-NP, and DukeMTMC-reID datasets. We show that PoseBox alone yields decent re-ID accuracy and that when integrated in the PBF network, the learned PIE descriptor produces competitive performance compared with state-of-the-art approaches.

A lysosome-targetable and two-photon fluorescent probe for monitoring endogenous and exogenous nitric oxide in living cells.

Abstract: A lysosome-specific and two-photon fluorescent probe, Lyso-NINO, demonstrates high selectivity and sensitivity toward NO, lower cytotoxicity, and perfect lysosomal localization. With the aid of Lyso-NINO, the first capture of NO within lysosomes of macrophage cells has been achieved using both two-photon fluorescence microscopy and flow cytometry.