Search Results for author:
Found 9 papers, 1 papers with code
Development of a MultiModal Annotation Framework and Dataset for Deep Video Understanding
The objective of the framework is to provide a guide to annotating long duration videos to support tasks and challenges in the video and multimedia understanding domains.
Corrected Evaluation Results of the NTCIR WWW-2, WWW-3, and WWW-4 English Subtasks
The noise is due to a fatal bug in the backend of our relevance assessment interface.
Can Old TREC Collections Reliably Evaluate Modern Neural Retrieval Models?
Neural retrieval models are generally regarded as fundamentally different from the retrieval techniques used in the late 1990's when the TREC ad hoc test collections were constructed.
Podcast Metadata and Content: Episode Relevance andAttractiveness in Ad Hoc Search
We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance.
Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID
We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19.
TREC Deep Learning Track: Reusable Test Collections in the Large Data Regime
The TREC Deep Learning (DL) Track studies ad hoc search in the large data regime, meaning that a large set of human-labeled training data is available.
How to Measure the Reproducibility of System-oriented IR Experiments
Replicability and reproducibility of experimental results are primary concerns in all the areas of science and IR is not an exception.
TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic.
HLVU : A New Challenge to Test Deep Understanding of Movies the Way Humans do
In this paper we propose a new evaluation challenge and direction in the area of High-level Video Understanding.
