Knowledge-based Visual Question Answering (KBVQA) aims to utilize external knowledge to answer image-related questions. Current KBVQA frameworks prompt large language models (LLMs) with answer heuristics, narrowing the focus to more relevant answers. However, these answer heuristic based frameworks exhibit the bias towards overemphasizing highest-scoring answers, neglecting potential answers with lower scores. This bias arises from the deficiencies of in-context example constructions and underperformances of multi-query ensemble strategy. In this paper, we propose MinBias, an approach designed tomitigateinherentbiasof answer heuristic based VQA framework. Firstly, to help LLMs learn dialectically, we propose Dialectical Learning Space Construction strategy (DLSC). This module mitigates bias via guaranteeing diversity in the selected examples. Secondly, to strengthen the connection between image captions and questions, we propose Large Language Model guided Visual Masking (LVM) algorithm. This module mitigates bias via enhancing visual clues in example content. Finally, we propose Visual Entailment based Answer Rerank module (VEAR). This module mitigates the bias arising from multi-query strategy via obtaining unbiased evaluations. Extensive experiments demonstrate that our method mitigates the bias of the answer heuristic based VQA framework and enhances the model performance.

Action segmentation is a challenging task that requires accurate parsing and labeling of each action. There are two types of methods for action segmentation. The first type primarily focuses on extracting high-quality features from videos, while the second type focuses on combining textual and perceptual features through multimodal fusion. However, both types of methods have their limitations. The first type is limited to a single modality and does not leverage multimodal information, while the second type, although promising, is restricted by the language used to describe the actions in the texts. To solve these problems, we propose in this paper, an Action Text Diffusion Prior Network (ATDPN) which simultaneously improves the quality of the extracted visual features (by introducing a Video-level Diffusion Prior Sampling) and integrates the textual information to fullest extent. This leads to superior action segmentation results. Our experiments performed on GTEA dataset demonstrate the effective feature extraction ability of ATDPN.