Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Abstract 2D image understanding is a complex problem within computer vision, but it holds
the key to providing human-level scene comprehension. It goes further than identifying the …

Multi-modal Named Entity Recognition (MNER), which mainly focuses on enhancing text-
only NER with visual information, has recently attracted considerable attention. Most current …

Abstract Vision Language Model (VLM) is a popular research field located at the fusion of
computer vision and natural language processing (NLP). With the emergence of transformer …

Image–text retrieval plays a considerable role in associating vision and language. Existing
mainstream approaches focus on fine-grained alignment while ignoring the influence of …

The intersection of vision and language is of major interest due to the increased focus on
seamless integration between recognition and reasoning. Scene graphs (SGs) have …

Visual question answering (VQA) requires systems to perform concept-level reasoning by
unifying unstructured (eg, the context in question and answer;" QA context") and structured …

Existing Unbiased Scene Graph Generation (USGG) methods only focus on addressing the
predicate-level imbalance that high-frequency classes dominate predictions of rare ones …

Visual Question Answering (VQA) is a multimodal task that uses natural language to ask and
answer questions based on image content. For multimodal tasks, obtaining accurate …

Visual Question Answering (VQA) models fail catastrophically on questions related to the
reading of text-carrying images. However, TextVQA aims to answer questions by …