Content-based image retrieval (CBIR), which consists of ranking a set of images with respect
to a query image based on visual similarity, can assist diagnostic radiologists in assessing …

Recent advancements in TableQA leverage sequence-to-sequence (Seq2seq) deep
learning models to accurately respond to natural language queries. These models achieve …

Learning to rank models are broadly applied in ad hoc retrieval for scoring and sorting
documents based on their relevance to textual queries. The generalizability of the trained …

Community question answering (CQA) websites have grown rapidly, but they face a gap
between questions and answerers. This gap causes delays in getting answers and …

Conversational search and recommendation systems can ask clarifying questions through
the conversation and collect valuable information from users. However, an important …

Learning-to-rank has been intensively studied and has shown significantly increasing
values in a wide range of domains, such as web search, recommender systems, dialogue …

Listwise learning to rank models, which optimize the ranking of a document list, are among
the most widely adopted algorithms for finding and ranking relevant documents to user …

Rerankers, typically cross-encoders, are often used to re-score the documents retrieved by
cheaper initial IR systems. This is because, though expensive, rerankers are assumed to be …

Reconstructing 3D face models from a single image is an inherently ill-posed problem,
which becomes even more challenging in the presence of occlusions. In addition to fewer …

When information retrieval systems return a ranked list of results in response to a query, they
may be choosing from a large set of candidate results that are equally useful and relevant …