Given that our experimental results show that current gait recognition approaches designed under data collected in controlled scenarios are inappropriate for real surveillance scenarios, we propose a novel gait recognition method, called RealGait.
Therefore, virality prediction from dance challenges is of great commercial value and has a wide range of applications, such as smart recommendation and popularity promotion.
Video-based person re-identification (Re-ID) which aims to associate people across non-overlapping cameras using surveillance video is a challenging task.
Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications, whereas the data of rare fine-grained categories is very limited.
Furthermore, a novel framework based on convolutional variational autoencoder and deep Koopman embedding is proposed to approximate the Koopman operators, which is used as dynamical features from the linearized embedding space for cross-view gait recognition.
Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Ranked #3 on Event Extraction on ACE2005
ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence.
Video-based person re-identification (Re-ID) is an important computer vision task.
In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach.
Group activity recognition plays a fundamental role in a variety of applications, e. g. sports video analysis and intelligent surveillance.
Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively.
We argue that one of the diffculties in this problem is the severe misalignment in face images or feature vectors with different poses.