RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores residual and illumination with Generative Adversarial Networks for shadow removal. Combined with the coarse shadow-removal image, the estimated negative residual images and inverse illumination maps can be used to generate indirect shadow-removal images to refine the coarse shadow-removal result to the fine shadow-free image in a coarse-to-fine fashion. Three discriminators are designed to distinguish whether the predicted negative residual images, shadow-removal images, and the inverse illumination maps are real or fake jointly compared with the corresponding ground-truth information. To our best knowledge, we are the first one to explore residual and illumination for shadow removal. We evaluate our proposed method on two benchmark datasets, i.e., SRD and ISTD, and the extensive experiments demonstrate that our proposed method achieves the superior performance to state-of-the-arts, although we have no particular shadow-aware components designed in our generators.Comment: The paper was accepted to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI'2020

Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space

Diverse human motion prediction aims at predicting multiple possible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sample outcomes from the distribution. While different results can be obtained, they are usually the most likely ones which are not diverse enough. Recent work explicitly learns multiple modes of the conditional distribution via a deterministic network, which however can only cover a fixed number of modes within a limited range. In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model. Our method works by generating an auxiliary space and smartly making randomly sampling from the auxiliary space equivalent to the diverse sampling from the target distribution. We propose a simple yet effective network architecture that implements this novel sampling strategy, which incorporates a Gumbel-Softmax coefficient matrix sampling method and an aggressive diversity promoting hinge loss function. Extensive experiments demonstrate that our method significantly improves both the diversity and accuracy of the samplings compared with previous state-of-the-art sampling approaches. Code and pre-trained models are available at https://github.com/Droliven/diverse_sampling.Comment: Paper and Supp of our work accepted by ACM MM 202

Multiple-Crop Human Mesh Recovery with Contrastive Learning and Camera Consistency in A Single Image

We tackle the problem of single-image Human Mesh Recovery (HMR). Previous approaches are mostly based on a single crop. In this paper, we shift the single-crop HMR to a novel multiple-crop HMR paradigm. Cropping a human from image multiple times by shifting and scaling the original bounding box is feasible in practice, easy to implement, and incurs neglectable cost, but immediately enriches available visual details. With multiple crops as input, we manage to leverage the relation among these crops to extract discriminative features and reduce camera ambiguity. Specifically, (1) we incorporate a contrastive learning scheme to enhance the similarity between features extracted from crops of the same human. (2) We also propose a crop-aware fusion scheme to fuse the features of multiple crops for regressing the target mesh. (3) We compute local cameras for all the input crops and build a camera-consistency loss between the local cameras, which reward us with less ambiguous cameras. Based on the above innovations, our proposed method outperforms previous approaches as demonstrated by the extensive experiments

System Dynamical Simulation of Risk Perception for Enterprise Decision-Maker in Communication of Chemical Incident Risks

PresentationSystem Dynamical Simulation of Risk Perception for Enterprise Decision-Maker in Communication of Chemical Incident Risk

An intelligent video fire detection approach based on object detection technology

PresentationFire that is one of the most serious accidents in chemical factories, may lead to considerable product losses, equipment damages and casualties. With the rapid development of computer vision technology, intelligent fire detection has been proposed and applied in various scenarios. This paper presents a new intelligent video fire detection approach based on object detection technology using convolutional neural networks (CNN). First, a CNN model is trained for the fire detection task which is framed as a regression problem to predict bounding boxes and associated probabilities. In the application phase, videos from surveillance cameras are detected frame by frame. Once fire appears in the current frame, the model will output the coordinates of the fire region. Simultaneously, the frame where the fire region is localized will be immediately sent to safety supervisors as a fire alarm. This will help detect fire at the early stage, prevent fire spreading and improve the emergency response

Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection

Without human annotations, a typical Unsupervised Video Anomaly Detection (UVAD) method needs to train two models that generate pseudo labels for each other. In previous work, the two models are closely entangled with each other, and it is not known how to upgrade their method without modifying their training framework significantly. Second, previous work usually adopts fixed thresholding to obtain pseudo labels, however the user-specified threshold is not reliable which inevitably introduces errors into the training process. To alleviate these two problems, we propose a novel interleaved framework that alternately trains a One-Class Classification (OCC) model and a Weakly-Supervised (WS) model for UVAD. The OCC or WS models in our method can be easily replaced with other OCC or WS models, which facilitates our method to upgrade with the most recent developments in both fields. For handling the fixed thresholding problem, we break through the conventional cognitive boundary and propose a weighted OCC model that can be trained on both normal and abnormal data. We also propose an adaptive mechanism for automatically finding the optimal threshold for the WS model in a loose to strict manner. Experiments demonstrate that the proposed UVAD method outperforms previous approaches

The effect of biaxial strain on the thermoelectric properties of two-dimensional layered MoSe2

Two-dimensional layered MoSe2 demonstrates exceptional potential for thermoelectric conversion and waste heat recovery applications, owing to its superior electrical transport properties derived from structural plasticity and the high electronegativity of Se atoms. This study systematically examines the effects of biaxial strain on the thermal transport and thermoelectric properties of MoSe2 through first-principles calculations. The results reveal that tensile strain induces a monotonic reduction in lattice thermal conductivity through significant decreases in both phonon lifetime and group velocity. Regarding thermoelectric performance, while tensile strain enhances both p-type and n-type MoSe2, the n-type variant shows more pronounced improvement. The thermoelectric figure of merit increases from 0.066 to 0.191 under 2% tensile strain at 300 K, reaching 1.061 under 4% strain at 800 K. These findings not only confirm the excellent thermoelectric properties of n-type MoSe2 but also reveal the important role of strain engineering in modulating the thermoelectric properties, suggesting that n-type MoSe2 has great potential as a thermoelectric material

SARS-CoV-2 ORF10 antagonizes STING-dependent interferon activation and autophagy.

A characteristic feature of COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, is the dysregulated immune response with impaired type I and III interferon (IFN) expression and an overwhelming inflammatory cytokine storm. RIG-I-like receptors (RLRs) and cGAS-STING signaling pathways are responsible for sensing viral infection and inducing IFN production to combat invading viruses. Multiple proteins of SARS-CoV-2 have been reported to modulate the RLR signaling pathways to achieve immune evasion. Although SARS-CoV-2 infection also activates the cGAS-STING signaling by stimulating micronuclei formation during the process of syncytia, whether SARS-CoV-2 modulates the cGAS-STING pathway requires further investigation. Here, we screened 29 SARS-CoV-2-encoded viral proteins to explore the viral proteins that affect the cGAS-STING signaling pathway and found that SARS-CoV-2 open reading frame 10 (ORF10) targets STING to antagonize IFN activation. Overexpression of ORF10 inhibits cGAS-STING-induced interferon regulatory factor 3 phosphorylation, translocation, and subsequent IFN induction. Mechanistically, ORF10 interacts with STING, attenuates the STING-TBK1 association, and impairs STING oligomerization and aggregation and STING-mediated autophagy; ORF10 also prevents the endoplasmic reticulum (ER)-to-Golgi trafficking of STING by anchoring STING in the ER. Taken together, these findings suggest that SARS-CoV-2 ORF10 impairs the cGAS-STING signaling by blocking the translocation of STING and the interaction between STING and TBK1 to antagonize innate antiviral immunity