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 …

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 …

Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph
representation for downstream reasoning tasks. Despite recent advancements existing …

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 …

Abstract Dynamic Scene Graph Generation (DSGG) focuses on identifying visual
relationships within the spatial-temporal domain of videos. Conventional approaches often …

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

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 …

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 …

Multimodal event causality reasoning aims to recognize the causal relations based on the
given events and accompanying image pairs, requiring the model to have a comprehensive …

Visual reasoning between visual images and natural language is a long-standing challenge
in computer vision. Most of the methods aim to look for answers to questions only on the …