Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task

Abstract

VQA is an ambitious task aiming to answer any image-related question. However, in reality, it is hard to build such a system once for all since the needs of users are continuously updated, and the system has to implement new functions. Thus, Continual Learning (CL) ability is a must in developing advanced VQA systems. Recently, a pioneer work split a VQA dataset into disjoint answer sets to study this topic. However, CL on VQA involves not only the expansion of label sets (new Answer sets). It is crucial to study how to answer questions when deploying VQA systems to new environments (new Visual scenes) and how to answer questions requiring new functions (new Question types). Thus, we propose CLOVE, a benchmark for Continual Learning On Visual quEstion answering, which contains scene- and function-incremental settings for the two aforementioned CL scenarios. In terms of methodology, the main difference between CL on VQA and classification is that the former additionally involves expanding and preventing forgetting of reasoning mechanisms, while the latter focusing on class representation. Thus, we propose a real-data-free replay-based method tailored for CL on VQA, named Scene Graph as Prompt for Symbolic Replay. Using a piece of scene graph as a prompt, it replays pseudo scene graphs to represent the past images, along with correlated QA pairs. A unified VQA model is also proposed to utilize the current and replayed data to enhance its QA ability. Finally, experimental results reveal challenges in CLOVE and demonstrate the effectiveness of our method. Code and data are available at https://github.com/showlab/CLVQA.

Negative Pre-aware for Noisy Cross-Modal Matching

“…Cross-modal matching aims to align different modalities (e.g., text and image) within a common space and pair them based on similarity score. With the explosion of multimedia data, cross-modal matching has gained traction in both industry and academia, e.g., text-to-image generation (Zhou et al 2022;Ding et al 2021), image captioning (Li et al 2019b;Stefanini et al 2022;Wang et al 2023), and visual question answering (Lin et al 2022;Lei et al 2023). These works have achieved promising performance by training on large-scale datasets.…”