Weakly Aligned Feature Fusion for Multimodal Object Detection

Self-Attentive Feature-level Fusion for Multimodal Emotion Detection

Multimodal emotion recognition is the task of detecting emotions present in user-generated multimedia content. Such resources contain complementary information in multiple modalities. A stiff challenge often faced is the complexity associated with feature-level fusion of these heterogeneous modes. In this paper, we propose a new feature-level fusion method based on self-attention mechanism. We ...

Multi Sensor Fusion for Object Detection Using Generalized Feature Models

This paper presents a multi sensor tracking system and introduces the use of new generalized feature models. To detect and recognize objects as selfcontained parts of the real world with two or more sensors of the same or of several types requires on the one hand fusion methods suitable for combining the data coming from the set of sensors in an optimal manner. This is realized by a sensor fusi...

Feature-Level based Video Fusion for Object Detection

Fusion of three-dimensional data from multiple sensors gained momentum, especially in applications pertaining to surveillance, when promising results were obtained in moving object detection. Several approaches to video fusion of visual and infrared data have been proposed in recent literature. They mainly comprise of pixel based methodologies. Surveillance is a major application of video fusio...

Iuliia Malysheva LARGE - SCALE MULTIMODAL SENSOR FUSION AND OBJECT DETECTION

Object-centered Feature Selection for Weakly-Unsupervised Object Categorization

We describe a novel approach of spatio-temporal mapping of local image features, to reduce the number of input data for further object categorization. The main focus of our work is the selection of good features to learn, by achieving a precise mapping of image features either related to static objects or to background. This can be done by initial camera motion estimation, subsequent structure ...