P
Peter Anderson
Researcher at Georgia Institute of Technology
Publications - 55
Citations - 11219
Peter Anderson is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Closed captioning & Question answering. The author has an hindex of 22, co-authored 55 publications receiving 7333 citations. Previous affiliations of Peter Anderson include University of New South Wales & Australian National University.
Papers
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Proceedings ArticleDOI
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
TL;DR: In this paper, a bottom-up and top-down attention mechanism was proposed to enable attention to be calculated at the level of objects and other salient image regions, which achieved state-of-the-art results on the MSCOCO test server.
Posted Content
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering.
TL;DR: A combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions is proposed, demonstrating the broad applicability of this approach to VQA.
Book ChapterDOI
SPICE: Semantic Propositional Image Caption Evaluation
TL;DR: This paper proposed a new automated caption evaluation metric defined over scene graphs coined SPICE, which captures human judgments over model-generated captions better than other automatic metrics (e.g., system-level correlation of 0.88 with human judgments on the MS COCO dataset, versus 0.43 for CIDEr and 0.53 for METEOR).
Posted Content
SPICE: Semantic Propositional Image Caption Evaluation
TL;DR: It is hypothesized that semantic propositional content is an important component of human caption evaluation, and a new automated caption evaluation metric defined over scene graphs coined SPICE is proposed, which can answer questions such as which caption-generator best understands colors?
Proceedings ArticleDOI
Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments
Peter Anderson,Qi Wu,Damien Teney,Jake Bruce,Mark Johnson,Niko Sünderhauf,Ian Reid,Stephen Gould,Anton van den Hengel +8 more
TL;DR: The Room-to-Room (R2R) dataset as mentioned in this paper provides a large-scale reinforcement learning environment based on real imagery for visually-grounded natural language navigation in real buildings.