Towards unbiased visual language reasoning and consistent segmentation - Huang, Jianqiang (Nanyang Technological University, 2023)
In recent years, we have made significant advances in standard recognition tasks such as classification, detection or segmentation. To further understand from vi- sion, more and more researchers pay attention to introduce text information for reasoning. Such as image caption, visual question answering (VQA) and visual grounding (VG). For a general visual language framework, both visual feature and language feature are firstly extracted by backbone, then they are aggregated for the downstream task with an end-to-end manner. However, the intrinsic data bias haven’t been fully addressed, thus the training process are usually biased, which leads to biased inference. To tackle these challenges, we conducted two works to address the co-occurence bias and language bias. The main contributions are summarized as bellow: