655 research outputs found
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
the segmentation-based multi-scale analysis to locate tampered areas in digital
images. First, to deal with color input sliding windows of different scales, a
unified CNN architecture is designed. Then, we elaborately design the training
procedures of CNNs on sampled training patches. With a set of robust
multi-scale tampering detectors based on CNNs, complementary tampering
possibility maps can be generated. Last but not least, a segmentation-based
method is proposed to fuse the maps and generate the final decision map. By
exploiting the benefits of both the small-scale and large-scale analyses, the
segmentation-based multi-scale analysis can lead to a performance leap in
forgery localization of CNNs. Numerous experiments are conducted to demonstrate
the effectiveness and efficiency of our method.Comment: 7 pages, 6 figure
Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition
Facial expression data is characterized by a significant imbalance, with most
collected data showing happy or neutral expressions and fewer instances of fear
or disgust. This imbalance poses challenges to facial expression recognition
(FER) models, hindering their ability to fully understand various human
emotional states. Existing FER methods typically report overall accuracy on
highly imbalanced test sets but exhibit low performance in terms of the mean
accuracy across all expression classes. In this paper, our aim is to address
the imbalanced FER problem. Existing methods primarily focus on learning
knowledge of minor classes solely from minor-class samples. However, we propose
a novel approach to extract extra knowledge related to the minor classes from
both major and minor class samples. Our motivation stems from the belief that
FER resembles a distribution learning task, wherein a sample may contain
information about multiple classes. For instance, a sample from the major class
surprise might also contain useful features of the minor class fear. Inspired
by that, we propose a novel method that leverages re-balanced attention maps to
regularize the model, enabling it to extract transformation invariant
information about the minor classes from all training samples. Additionally, we
introduce re-balanced smooth labels to regulate the cross-entropy loss, guiding
the model to pay more attention to the minor classes by utilizing the extra
information regarding the label distribution of the imbalanced training data.
Extensive experiments on different datasets and backbones show that the two
proposed modules work together to regularize the model and achieve
state-of-the-art performance under the imbalanced FER task. Code is available
at https://github.com/zyh-uaiaaaa.Comment: Accepted by NeurIPS202
Analysis of Route Choice for Pedestrian Two-Stage Crossing at a Signalized Intersection
Studying pedestrians’ twice-crossing behavior is of great significance to enhance safety and efficiency for pedestrians at signalized intersections. However, limited attention has been paid to analyze and model pedestrians’ behavior patterns of twice crossing. The purpose of this paper is to determine pedestrians' route choices for twice crossing at a signalized intersection, focusing on the waiting position (to cross the street) and walking route. A goal-oriented and time-driven model was proposed to analyze pedestrians’ twice-crossing behavior at signalized intersections, where the two directions have different pedestrian signal timing. A video-recording method was used to collect field data in order to obtain pedestrian preferences in choosing a walking route. It was found that pedestrians in the two directions present different preferences toward walking route, in waiting position, directional change and route type. The results showed that the proposed model is effective in simulating pedestrian route-choice behavior of twice crossing. This research provides a theoretical basis for identifying pedestrian movement intention, optimizing signal timing, and improving pedestrian infrastructure at signalized intersections.
Analysis of Red-Light Violation Behavior of Pedestrian Two-Stage Crossing at a Signalized Intersection
Studying pedestrians’ twice-crossing behavior is of great significance to enhance safety and efficiency for pedestrians at signalized intersections. However, researchers have paid little attention to analyze and model pedestrians’ red-light running behavior on a two-stage crossing at signalized intersections. This paper focuses on analyzing the characteristics of pedestrian red-light violation behavior at the two stages, including the time distribution of violation behavior, the consistency of violation behavior, and the violation behavior in group. A goal-oriented and time-driven red-light violation behavior model was proposed for pedestrian two-stage crossing. A video-recording method was used to collect field data, and the results show that pedestrians in the two directions present different red-light violation behaviors in time selection and violation count, as well as, pedestrians in the two stages of a direction present different red-light violation behaviors in time selection. The main reasons leading to the phenomena were analyzed, regarding from people’s cognitive psychology and visual perception. The results also show that the proposed model is effective in simulating pedestrian red-light violation behavior of twice crossing. This research provides a theoretical basis for optimizing signal timing, improving pedestrian safety and developing user-friendly transportation system
Analysis of OAM Mode Purity in Phased Array Antenna
In this paper, the orbital angular momentum of different modes in electric field is decomposed, and the definition of purity of OAM mode in OAM antenna are proposed. Based on the purity theory, the purity of circular array is derived. And the effects of different parameters on the purity are analyzed. An intuitive and quantifiable dimension for comparing the OAM performance in phased array antenna is provided in this paper
TBFormer: Two-Branch Transformer for Image Forgery Localization
Image forgery localization aims to identify forged regions by capturing
subtle traces from high-quality discriminative features. In this paper, we
propose a Transformer-style network with two feature extraction branches for
image forgery localization, and it is named as Two-Branch Transformer
(TBFormer). Firstly, two feature extraction branches are elaborately designed,
taking advantage of the discriminative stacked Transformer layers, for both RGB
and noise domain features. Secondly, an Attention-aware Hierarchical-feature
Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from
two different domains. Although the two feature extraction branches have the
same architecture, their features have significant differences since they are
extracted from different domains. We adopt position attention to embed them
into a unified feature domain for hierarchical feature investigation. Finally,
a Transformer decoder is constructed for feature reconstruction to generate the
predicted mask. Extensive experiments on publicly available datasets
demonstrate the effectiveness of the proposed model.Comment: 5 pages, 3 figure
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