Visual Question Answering aims to answer the free-form natural language question based on the visual clues in a given image. It is a difficult problem as it requires understanding the fine-grained structured information of both language and image for compositional reasoning. To establish the compositional reasoning, recent works attempt to introduce the scene graph in VQA. However, as the generated scene graphs are usually quite noisy, it greatly limits the performance of question answering. Therefore, this paper proposes to refine the scene graphs for improving the effectiveness. Specifically, we present a novel Scene Graph Refinement network (SGR), which introduces a transformer-based refinement network to enhance the object and relation features for better classification. Moreover, as the question provides valuable clues for distinguishing whether the subject, predicate, objecttriplets are helpful or not, the SGR network exploits the semantic information presented in the questions to select the most relevant relations for question answering. Extensive experiments conducted on the GQA benchmark demonstrate the effectiveness of our method.

Video question answering (VideoQA) is an essential task in vision-language understanding, which has attracted numerous research attention recently. Nevertheless, existing works mostly achieve promising performances on short videos of duration within 15 seconds. For VideoQA on minute-level long-term videos, those methods are likely to fail because of lacking the ability to deal with noise and redundancy caused by scene changes and multiple actions in the video. Considering the fact that the question often remains concentrated in a short temporal range, we propose to first locate the question to a segment in the video and then infer the answer using the located segment only. Under this scheme, we propose “Locate before Answering” (LocAns), a novel approach that integrates a question localization module and an answer prediction module into an end-to-end model. During the training phase, the available answer label not only serves as the supervision signal of the answer prediction module, but also is used to generate pseudo temporal labels for the question localization module. Moreover, we design a decoupled alternative training strategy to update the two modules separately. In the experiments, LocAns achieves state-of-the-art performance on three modern long-term VideoQA datasets, NExT-QA, ActivityNet-QA, and AGQA. Its qualitative examples show the reliable performance of the question localization.

Given the significant potential of large language models (LLMs) in sequence modeling, emerging studies have begun applying them to time-series forecasting. Despite notable progress, existing methods still face two critical challenges: (1) their reliance on large amounts of paired text data, limiting the model applicability, and (2) a substantial modality gap between text and time series, leading to insufficient alignment and suboptimal performance. This paper introduces Hierarchical Text-Free Alignment (TS-HTFA) a novel method that leverages hierarchical alignment to fully exploit the representation capacity of LLMs for time-series analysis while eliminating the dependence on text data. Specifically, paired text data are replaced with adaptive virtual text based on QR decomposition word embeddings and learnable prompts. Furthermore, comprehensive cross-modal alignment is established at three levels: input, feature, and output, contributing to enhanced semantic symmetry between modalities. Extensive experiments on multiple time-series benchmarks demonstrate that TS-HTFA achieves state-of-the-art performance, significantly improving prediction accuracy and generalization.

SPIE Digital Library Proceedings