5 accepted papers
Numerous studies have demonstrated that Visual Question Answering (VQA) models are vulnerable to language priors and dataset biases, often leading to spurious correlations between questions and answers. As a result, these models excessively rely on linguistic cues, neglecting essential visual inform
Few-Shot Class-Incremental Learning (FSCIL) aims to incrementally learn new class knowledge from limited samples while preserving previously knowledge from encountered classes. However, existing FSCIL methods encounter two primary challenges: (1) inadequate adaptation, where overfitting to new class…
Existing Medical Visual Question Answering (Med-VQA) models often suffer from language biases, where spurious correlations between question types and answer categories are inadvertently established. To address these issues, we propose a novel Cause-Effect Driven Optimization framework called CEDO, t
Language bias in Visual Question Answering (VQA) arises when models exploit spurious statistical correlations between question templates and answers, particularly in out-of-distribution scenarios, thereby neglecting essential visual cues and compromising genuine multimodal reasoning. Despite numerou…
Class-incremental learning (CIL) enables models to learn new tasks without forgetting previously acquired knowledge. However, existing CIL approaches often struggle with inadequate adaptation to task-specific feature spaces and catastrophic forgetting of previously-acquired knowledge, compromising t…