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 …

Understanding human actions in visual data is tied to advances in complementary research
areas including object recognition, human dynamics, domain adaptation and semantic …

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 …

We evaluate whether features extracted from the activation of a deep convolutional network
trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re …

With the success of new computational architectures for visual processing, such as
convolutional neural networks (CNN) and access to image databases with millions of …

In this paper we study the role of context in existing state-of-the-art detection and
segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 …

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 …

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 …

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 …

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 …