Multimodal sentiment analysis: a survey of methods, trends, and challenges
Sentiment analysis has come long way since it was introduced as a natural language
processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying …
processing task nearly 20 years ago. Sentiment analysis aims to extract the underlying …
Going deeper into action recognition: A survey
Understanding human actions in visual data is tied to advances in complementary research
areas including object recognition, human dynamics, domain adaptation and semantic …
areas including object recognition, human dynamics, domain adaptation and semantic …
Action genome: Actions as compositions of spatio-temporal scene graphs
Action recognition has typically treated actions and activities as monolithic events that occur
in videos. However, there is evidence from Cognitive Science and Neuroscience that people …
in videos. However, there is evidence from Cognitive Science and Neuroscience that people …
Long-term feature banks for detailed video understanding
To understand the world, we humans constantly need to relate the present to the past, and
put events in context. In this paper, we enable existing video models to do the same. We …
put events in context. In this paper, we enable existing video models to do the same. We …
A survey of multimodal sentiment analysis
Sentiment analysis aims to automatically uncover the underlying attitude that we hold
towards an entity. The aggregation of these sentiment over a population represents opinion …
towards an entity. The aggregation of these sentiment over a population represents opinion …
A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility map**
This study introduces four heterogeneous ensemble-learning techniques, that is, stacking,
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …
Multiple instance learning: A survey of problem characteristics and applications
Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …
Semantics for robotic map**, perception and interaction: A survey
For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …
require a deeper understanding of the world in which they operate. In robotics and related …
Object detectors emerge in deep scene cnns
With the success of new computational architectures for visual processing, such as
convolutional neural networks (CNN) and access to image databases with millions of …
convolutional neural networks (CNN) and access to image databases with millions of …
CNN features off-the-shelf: an astounding baseline for recognition
Recent results indicate that the generic descriptors extracted from the convolutional neural
networks are very powerful. This paper adds to the mounting evidence that this is indeed the …
networks are very powerful. This paper adds to the mounting evidence that this is indeed the …