Visual relationship detection aims to capture interactions between pairs of objects in images. Relationships between objects and humans represent a particularly important subset of this problem, with implications for challenges such as understanding human behavior, and identifying affordances, amongst others. In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotations (nearly 10K categories) than the previous released datasets. This large label space better reflects the reality of human-object interactions, but gives rise to a long-tail distribution problem, which in turn demands a zero-shot approach to labels appearing only in the test set. This is the first time this issue has been addressed. We propose a webly-supervised approach to these problems and demonstrate that the proposed model provides a strong baseline on our HCVRD dataset.

Because the sensor captures human accelerations continuously Inertial wearable sensors have been vastly utilized for Human while the subject performs different activities in freeliving Activity Recognition (HAR). A major challenge with situations, 'start' and'end' of activities are unknown the trained HAR models is that the performance of the classifier a priori. A typical segmentation with a window of size w is highly sensitive to the context of the sensor and engineered on 3-axis accelerometer data forms 3 channels of input data, features (Rokni and Ghasemzadeh 2017).

A major barrier to the personalized Human Activity Recognition using wearable sensors is that the performance of the recognition model drops significantly upon adoption of the system by new users or changes in physical/behavioral status of users. Therefore, the model needs to be retrained by collecting new labeled data in the new context. In this study, we develop a transfer learning framework using convolutional neural networks to build a personalized activity recognition model with minimal user supervision.

A major barrier to the personalized Human Activity Recognition using wearable sensors is that the performance of the recognition model drops significantly upon adoption of the system by new users or changes in physical/behavioral status of users. Therefore, the model needs to be retrained by collecting new labeled data in the new context. In this study, we develop a transfer learning framework using convolutional neural networks to build a personalized activity recognition model with minimal user supervision.

While multimodal conversation agents are gaining importance in several domains such as retail, travel etc., deep learning research in this area has been limited primarily due to the lack of availability of large-scale, open chatlogs. To overcome this bottleneck, in this paper we introduce the task of multimodal, domain-aware conversations, and propose the MMD benchmark dataset. This dataset was gathered by working in close coordination with large number of domain experts in the retail domain. These experts suggested various conversations flows and dialog states which are typically seen in multimodal conversations in the fashion domain. Keeping these flows and states in mind, we created a dataset consisting of over 150K conversation sessions between shoppers and sales agents, with the help of in-house annotators using a semi-automated manually intense iterative process. With this dataset, we propose 5 new sub-tasks for multimodal conversations along with their evaluation methodology. We also propose two multimodal neural models in the encode-attend-decode paradigm and demonstrate their performance on two of the sub-tasks, namely text response generation and best image response selection. These experiments serve to establish baseline performance and open new research directions for each of these sub-tasks. Further, for each of the sub-tasks, we present a 'per-state evaluation' of 9 most significant dialog states, which would enable more focused research into understanding the challenges and complexities involved in each of these states.

Because the sensor captures human accelerations continuously Inertial wearable sensors have been vastly utilized for Human while the subject performs different activities in freeliving Activity Recognition (HAR). A major challenge with situations, 'start' and'end' of activities are unknown the trained HAR models is that the performance of the classifier a priori. A typical segmentation with a window of size w is highly sensitive to the context of the sensor and engineered on 3-axis accelerometer data forms 3 channels of input data, features (Rokni and Ghasemzadeh 2017).

Because the sensor captures human accelerations continuously Inertial wearable sensors have been vastly utilized for Human while the subject performs different activities in freeliving Activity Recognition (HAR). A major challenge with situations, 'start' and'end' of activities are unknown the trained HAR models is that the performance of the classifier a priori. A typical segmentation with a window of size w is highly sensitive to the context of the sensor and engineered on 3-axis accelerometer data forms 3 channels of input data, features (Rokni and Ghasemzadeh 2017).

Because the sensor captures human accelerations continuously Inertial wearable sensors have been vastly utilized for Human while the subject performs different activities in freeliving Activity Recognition (HAR). A major challenge with situations, 'start' and'end' of activities are unknown the trained HAR models is that the performance of the classifier a priori. A typical segmentation with a window of size w is highly sensitive to the context of the sensor and engineered on 3-axis accelerometer data forms 3 channels of input data, features (Rokni and Ghasemzadeh 2017).

A major barrier to the personalized Human Activity Recognition using wearable sensors is that the performance of the recognition model drops significantly upon adoption of the system by new users or changes in physical/behavioral status of users. Therefore, the model needs to be retrained by collecting new labeled data in the new context. In this study, we develop a transfer learning framework using convolutional neural networks to build a personalized activity recognition model with minimal user supervision.

A major barrier to the personalized Human Activity Recognition using wearable sensors is that the performance of the recognition model drops significantly upon adoption of the system by new users or changes in physical/behavioral status of users. Therefore, the model needs to be retrained by collecting new labeled data in the new context. In this study, we develop a transfer learning framework using convolutional neural networks to build a personalized activity recognition model with minimal user supervision.