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Found 7 papers, 6 papers with code
High-Level Parallelism and Nested Features for Dynamic Inference Cost and Top-Down Attention
Our proposed topology also comes with a built-in top-down attention mechanism, which allows processing to be directly influenced by either enhancing or inhibiting category-specific high-level features, drawing parallels to the selective attention mechanism observed in human cognition.
Generative Adversarial Graph Convolutional Networks for Human Action Synthesis
Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning).
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A Symbolic Temporal Pooling method for Video-based Person Re-Identification
In video-based person re-identification, both the spatial and temporal features are known to provide orthogonal cues to effective representations.
The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices
Over the last decades, the world has been witnessing growing threats to the security in urban spaces, which has augmented the relevance given to visual surveillance solutions able to detect, track and identify persons of interest in crowds.
An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion.
A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-output Classification Problems
This paper describes one objective function for learning semantically coherent feature embeddings in multi-output classification problems, i. e., when the response variables have dimension higher than one.
Person Re-identification: Implicitly Defining the Receptive Fields of Deep Learning Classification Frameworks
The \emph{receptive fields} of deep learning classification models determine the regions of the input data that have the most significance for providing correct decisions.
