27 accepted papers
Cross-subject emotion recognition aims to improve a model's generalization to previously unseen subjects. Existing methods are mainly built upon domain generalization or data augmentation, but suffer from two major limitations: 1) heavy dependence on modality-specific feature designs—almost exclusiv…
Person re-identification (Re-ID) under extremely low-light conditions suffers from severe image degradation, which significantly impairs the extraction of identity-discriminative features. Existing methods struggle to recover semantic information that is obscured under poor illumination. To better u
Fatigue is a critical factor contributing to accidents in industries such as safety monitoring and engineering construction. Fatigue exhibits dynamic complexity and non-stationary characteristics, so there are many intermediate states of short-term variation between alert and fatigue. Capturing and…
Due to the successful development of deep image generation technology, forgery detection plays a more important role in social and economic security. Racial bias has not been explored thoroughly in the deep forgery detection field. In the paper, we first contribute a dedicated dataset called the Fai…
Adversarial attacks involve adding perturbations to the source image to cause misclassification by the target model, which demonstrates the potential of attacking face recognition models. Existing adversarial face image generation methods still can’t achieve satisfactory performance because of low t…
Spatiotemporal social behavior analysis is a technique that studies the social behavior patterns of objects and estimates their risks based on their trajectories. In social public scenarios such as train stations, hidden following behavior has become one of the most challenging issues due to its pro…
Video anomaly detection (VAD) is an essential but challenging task. Existing prevalent methods focus on analyzing the reconstruction or prediction difference between normal and abnormal patterns through multiple deep features, e.g., optic flow. However, these approaches independently use deep featur…
Given clean labels, Graph Neural Networks (GNNs) have shown promising abilities for graph anomaly detection. However, real-world graphs are inevitably noisy labeled, which drastically degrades the performance of GNNs. To alleviate it, some studies follow the local consistency (a.k.a homophily) assum…
Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anom…
The anonymity of online networks makes tackling fraud increasingly costly. Thanks to the superiority of graph representation learning, graph-based fraud detection has made significant progress in recent years. However, upgrading fraudulent strategies produces more advanced and difficult scams. One c…
Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation and limited generalizability, this paper focuses on building a unified framework for multiple classes. Under such a cha…
Deep learning networks with deeper layers become a trend for their good performance but lacks the potential for real-time mobile deployment. Another challenge for paired training networks is the limited generalization capacity caused by the sample bias. To overcome these two challenges, we propose a…
Location prediction is of great importance in location-based applications for the construction of the smart city. To our knowledge, existing models for location prediction focus on the users' preference on POIs from the perspective of the human side. However, modeling users' interests from the histo…
Person re-identification aims to associate images captured by non-overlapping cameras. It is a challenging task because images are often in different conditions such as background clutter, illumination variation, viewpoint changes and different camera settings. Viewpoint changes and pose variations…
Most of current best performing Acoustic Scene Classification (ASC) systems utilize Mel scale spectrograms with Convolutional Neural Networks (CNNs). Mel scale is a common way to suit frequency warping of human ears, with strict decreasing frequency resolution on low to high frequency range. However…
Video transmission from satellites to terrestrial devices usually requires a large amount of channel resources due to the huge amount of satellite video data. Subject to limited transmission bandwidth in space environment, the video encoder for video satellite calls for higher coding efficiency. In…
The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme proposed for moving objects so far has achieved high efficiency, it does not take full advantages of local information and highly relies on the accuracy…
In this paper, we present a conceptually novel framework for vehicle pose estimation from given RGB images. Our approach extends Mask R-CNN by adding two branches for coarse viewpoint estimation and keypoint detection, in parallel with the existing branches for mask segmentation and 2D object detect…
We present Edge-aware Context Encoder (E-CE): an image inpainting model which takes scene structure and context into account. Unlike previous CE which predicts the missing regions using context from entire image, E-CE learns to recover the texture according to edge structures, attempting to avoid co…
Face Super Resolution(FSR) is to infer High Resolution(HR) facial images from given Low Resolution(LR) ones with the assistance of LR and HR training pairs. Among existing methods, local patch based methods are superior in visual and objective quality than global based methods. These local patch bas…
NHK has proposed a famous 3D audio system: 22.2 multi-channel system, but its loudspeakers are too many and are troublesome to put in home. Ando and Wang has proposed two simplification methods to reduce its channel number, but only 3D sound field at the central listening point can be recovered well…
Metric learning is an important issue in person re-identification, and Mahalanobis-distance based metric learning methods prevail in this field. All of these approaches can be considered as equivalently projecting all samples to a new metric space and calculating the Euclidean distance there. Howeve…
In this paper we address the problem of automatic clothing parsing in surveillance video with the information from user-generated tags such as “jeans” and “T-shirt”. Although clothing parsing has achieved great success in fashion clothing, it is quite challenging to parse clothing in practical surve…
Action recognition from video is a prominent research area in computer vision, with far-reaching applications. Current state-of-the-art action recognition methods is Fisher Vector (FV) coding model based on spatio-temporal local features. Though high dimensional local features have more representati…
This paper proposes a general multichannel system reproduction method. Firstly, relative to original multichannel system, a general global model is build up by guaranteeing sound pressure and the direction of particle velocity at the receiving point constant, and making the square error of particle…
In dictionary-learning-based face hallucination, the testing image is represented as a linear combination of the training samples, and how to obtain the optimal coefficients is the primary issue. Sparse representation (SR) has ever been widely used in face hallucination, however, due to the fact tha…
Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or…