ENTER: Event Based Interpretable Reasoning for VideoQA
- URL: http://arxiv.org/abs/2501.14194v1
- Date: Fri, 24 Jan 2025 02:56:59 GMT
- Title: ENTER: Event Based Interpretable Reasoning for VideoQA
- Authors: Hammad Ayyubi, Junzhang Liu, Ali Asgarov, Zaber Ibn Abdul Hakim, Najibul Haque Sarker, Zhecan Wang, Chia-Wei Tang, Hani Alomari, Md. Atabuzzaman, Xudong Lin, Naveen Reddy Dyava, Shih-Fu Chang, Chris Thomas,
- Abstract summary: We present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs.
Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships form the edges.
- Score: 29.710826599316302
- License:
- Abstract: In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships (temporal/causal/hierarchical) form the edges. This structured representation offers many benefits: 1) Interpretable VideoQA via generated code that parses event-graph; 2) Incorporation of contextual visual information in the reasoning process (code generation) via event graphs; 3) Robust VideoQA via Hierarchical Iterative Update of the event graphs. Existing interpretable VideoQA systems are often top-down, disregarding low-level visual information in the reasoning plan generation, and are brittle. While bottom-up approaches produce responses from visual data, they lack interpretability. Experimental results on NExT-QA, IntentQA, and EgoSchema demonstrate that not only does our method outperform existing top-down approaches while obtaining competitive performance against bottom-up approaches, but more importantly, offers superior interpretability and explainability in the reasoning process.
Related papers
- TimeLogic: A Temporal Logic Benchmark for Video QA [64.32208175236323]
We introduce the TimeLogic QA (TLQA) framework to automatically generate temporal logical questions.
We leverage 4 datasets, STAR, Breakfast, AGQA, and CrossTask, and generate 2k and 10k QA pairs for each category.
We assess the VideoQA model's temporal reasoning performance on 16 categories of temporal logic with varying temporal complexity.
arXiv Detail & Related papers (2025-01-13T11:12:59Z) - Multi-object event graph representation learning for Video Question Answering [4.236280446793381]
We propose a contrastive language event graph representation learning method called CLanG to address this limitation.
Our method outperforms a strong baseline, achieving up to 2.2% higher accuracy on two challenging VideoQA, NExT-QA and TGIF-QA-R datasets.
arXiv Detail & Related papers (2024-09-12T04:42:51Z) - VProChart: Answering Chart Question through Visual Perception Alignment Agent and Programmatic Solution Reasoning [13.011899331656018]
VProChart is a novel framework designed to address the challenges of Chart Question Answering (CQA)
It integrates a lightweight Visual Perception Alignment Agent (VPAgent) and a Programmatic Solution Reasoning approach.
VProChart significantly outperforms existing methods, highlighting its capability in understanding and reasoning with charts.
arXiv Detail & Related papers (2024-09-03T07:19:49Z) - Semantic-aware Dynamic Retrospective-Prospective Reasoning for
Event-level Video Question Answering [14.659023742381777]
Event-Level Video Question Answering (EVQA) requires complex reasoning across video events to provide optimal answers.
We propose a semantic-aware dynamic retrospective-prospective reasoning approach for video-based question answering.
Our proposed approach achieves superior performance compared to previous state-of-the-art models.
arXiv Detail & Related papers (2023-05-14T03:57:11Z) - Learning Situation Hyper-Graphs for Video Question Answering [95.18071873415556]
We propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs.
We train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip.
Our results show that learning the underlying situation hyper-graphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks.
arXiv Detail & Related papers (2023-04-18T01:23:11Z) - Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal
Sentence Localization in Videos [67.12603318660689]
We propose a novel Hierarchical Visual- and Semantic-Aware Reasoning Network (HVSARN)
HVSARN enables both visual- and semantic-aware query reasoning from object-level to frame-level.
Experiments on three datasets demonstrate that our HVSARN achieves a new state-of-the-art performance.
arXiv Detail & Related papers (2023-03-02T08:00:22Z) - Video as Conditional Graph Hierarchy for Multi-Granular Question
Answering [80.94367625007352]
We argue that while video is presented in frame sequence, the visual elements are not sequential but rather hierarchical in semantic space.
We propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner.
arXiv Detail & Related papers (2021-12-12T10:35:19Z) - NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions [80.60423934589515]
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark.
We set up multi-choice and open-ended QA tasks targeting causal action reasoning, temporal action reasoning, and common scene comprehension.
We find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning.
arXiv Detail & Related papers (2021-05-18T04:56:46Z) - Location-aware Graph Convolutional Networks for Video Question Answering [85.44666165818484]
We propose to represent the contents in the video as a location-aware graph.
Based on the constructed graph, we propose to use graph convolution to infer both the category and temporal locations of an action.
Our method significantly outperforms state-of-the-art methods on TGIF-QA, Youtube2Text-QA, and MSVD-QA datasets.
arXiv Detail & Related papers (2020-08-07T02:12:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.