Anh Nguyen

Michael A. Alcorn, Anh Nguyen

In many multi-agent spatiotemporal systems, agents operate under the influence of shared, unobserved variables (e.g., the play a team is executing in a game of basketball). As a result, the trajectories of the agents are often statistically dependent at any given time step; however, almost universally, multi-agent models implicitly assume the agents' trajectories are statistically independent at each time step. In this paper, we introduce baller2vec++, a multi-entity Transformer that can effectively model coordinated agents. Specifically, baller2vec++ applies a specially designed self-attention mask to a mixture of location and "look-ahead" trajectory sequences to learn the distributions of statistically dependent agent trajectories. We show that, unlike baller2vec (baller2vec++'s predecessor), baller2vec++ can learn to emulate the behavior of perfectly coordinated agents in a simulated toy dataset. Additionally, when modeling the trajectories of professional basketball players, baller2vec++ outperforms baller2vec by a wide margin.

Anh Nguyen, Thanh-Toan Do, Ian Reid et al.

We address the problem of jointly learning vision and language to understand the object in a fine-grained manner. The key idea of our approach is the use of object descriptions to provide the detailed understanding of an object. Based on this idea, we propose two new architectures to solve two related problems: object captioning and natural language-based object retrieval. The goal of the object captioning task is to simultaneously detect the object and generate its associated description, while in the object retrieval task, the goal is to localize an object given an input query. We demonstrate that both problems can be solved effectively using hybrid end-to-end CNN-LSTM networks. The experimental results on our new challenging dataset show that our methods outperform recent methods by a fair margin, while providing a detailed understanding of the object and having fast inference time. The source code will be made available.