DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning

Large-scale text-to-image generation models with an exponential evolution can currently synthesize high-resolution, feature-rich, high-quality images based on text guidance. However, they are often overwhelmed by words of new concepts, styles, or object entities that always emerge. Although there are some recent attempts to use fine-tuning or prompt-tuning methods to teach the model a new concept as a new pseudo-word from a given reference image set, these methods are not only still difficult to synthesize diverse and high-quality images without distortion and artifacts, but also suffer from low controllability. To address these problems, we propose a DreamArtist method that employs a learning strategy of contrastive prompt-tuning, which introduces both positive and negative embeddings as pseudo-words and trains them jointly. The positive embedding aggressively learns characteristics in the reference image to drive the model diversified generation, while the negative embedding introspects in a self-supervised manner to rectify the mistakes and inadequacies from positive embedding in reverse. It learns not only what is correct but also what should be avoided. Extensive experiments on image quality and diversity analysis, controllability analysis, model learning analysis and task expansion have demonstrated that our model learns not only concept but also form, content and context. Pseudo-words of DreamArtist have similar properties as true words to generate high-quality images

Adversarially-Aware Robust Object Detector

Object detection, as a fundamental computer vision task, has achieved a remarkable progress with the emergence of deep neural networks. Nevertheless, few works explore the adversarial robustness of object detectors to resist adversarial attacks for practical applications in various real-world scenarios. Detectors have been greatly challenged by unnoticeable perturbation, with sharp performance drop on clean images and extremely poor performance on adversarial images. In this work, we empirically explore the model training for adversarial robustness in object detection, which greatly attributes to the conflict between learning clean images and adversarial images. To mitigate this issue, we propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. RobustDet also employs the Adversarial Image Discriminator (AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.Comment: ECCV2022 oral pape

Research on real-time scheduling algorithm of federated learning tasks based on energy optimization on NoC platform

Federal learning technology can realize global data sharing and reduce the risk of privacy disclosure under the premise of ensuring data security. Aiming at the problem of task assignment scheduling in federated learning process, this paper studies the problem of federated learning task scheduling on NoC multi-core platform under cloud computing architecture. Considering the limited computing resources of physical nodes, this paper describes the problem of optimal assignment and execution of tasks on network nodes as a mixed integer nonlinear programming problem. In order to improve the computational effi ciency, the original problem can be equitably converted into a mixed integer linear programming problem. Finally, the scheduling method is verifi ed by real application of task set, and the infl uence of parameter selection on scheduling scheme is studied

Masked Images Are Counterfactual Samples for Robust Fine-tuning

Deep learning models are challenged by the distribution shift between the training data and test data. Recently, the large models pre-trained on diverse data demonstrate unprecedented robustness to various distribution shifts. However, fine-tuning on these models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. Existing methods for tackling this trade-off do not explicitly address the OOD robustness problem. In this paper, based on causal analysis on the aforementioned problems, we propose a novel fine-tuning method, which use masked images as counterfactual samples that help improving the robustness of the fine-tuning model. Specifically, we mask either the semantics-related or semantics-unrelated patches of the images based on class activation map to break the spurious correlation, and refill the masked patches with patches from other images. The resulting counterfactual samples are used in feature-based distillation with the pre-trained model. Extensive experiments verify that regularizing the fine-tuning with the proposed masked images can achieve a better trade-off between ID and OOD performance, surpassing previous methods on the OOD performance. Our code will be publicly available.Comment: Accepted by CVPR 2023 (v2: improve the clarity

Road Network Guided Fine-Grained Urban Traffic Flow Inference

Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of traffic monitoring sensors for cost savings. In this work, we notice that traffic flow has a high correlation with road network, which was either completely ignored or simply treated as an external factor in previous works. To facilitate this problem, we propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that explicitly exploits the prior knowledge of road networks to fully learn the road-aware spatial distribution of fine-grained traffic flow. Specifically, a multi-directional 1D convolutional layer is first introduced to extract the semantic feature of the road network. Subsequently, we incorporate the road network feature and coarse-grained flow feature to regularize the short-range spatial distribution modeling of road-relative traffic flow. Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture. Benefiting from the road-aware inference mechanism, our method can generate high-quality fine-grained traffic flow maps. Extensive experiments on three real-world datasets show that the proposed RATFM outperforms state-of-the-art models under various scenarios

Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD matrices of previous time intervals can not be obtained immediately in online metro systems, and conventional methods only used limited information to forecast the future OD and DO ridership separately. In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership. Specifically, an OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Moreover, a Dual Information Transformer is introduced to propagate the mutual information among OD features and DO features for modeling the OD-DO causality and correlation. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously. Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction

Isolation and Identification of Pathogenic Fungi of Carambola Fruit and Study on Their Metabolites

In order to understand the types, biological characteristics, and metabolites types of pathogenic fungi in carambola, the typical diseased parts of carambola fruit were collected for fungal isolation by tissue isolation methods. The fungi were identified by morphological and molecular identification methods based on ITS sequencing analysis. The biological characteristics of pathogenic fungi were explored, and metabolites were isolated and purified using liquid chromatography. Their structures were established on the basis of 1H NMR and 13C NMR. The results were as follows: The four strains were identified as Fusarium equiseti, Neopestalotiopsis sp., Schizophyllum commune and Cladosporium cladosporioides. All four strains could cause disease in carambola fruit. The fermentation products of Fusarium equiseti were chemically investigated, leading to the discovery of four compounds, 3,7,9-trihydroxy-1-methyl-6H-dibenzo[b,d]pyran-6-on, 1,4,7-trihydroxy-3,9-dimethoxy-6H-benzo[c]chromen-6-one, lecanoric acid and 7-hydroxy-4-methyl-1(3H)-isobenzofuranone. The results of biological characteristics indicated that the optimal growth temperature for the four pathogenic fungi was 25~28 ℃, and they could all grow within the pH range of 4~10