Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering

• URL: http://arxiv.org/abs/2205.04061v1
• Date: Mon, 9 May 2022 06:28:56 GMT
• Title: Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering
• Authors: Min Peng, Chongyang Wang, Yuan Gao, Yu Shi and Xiang-Dong Zhou
• Abstract summary: We propose a novel Multilevel Hierarchical Network (MHN) with multiscale sampling for VideoQA. MHN comprises two modules, namely Recurrent Multimodal Interaction (RMI) and Parallel Visual Reasoning (PVR) With a multiscale sampling, RMI iterates the interaction of appearance-motion information at each scale and the question embeddings to build the multilevel question-guided visual representations. PVR infers the visual cues at each level in parallel to fit with answering different question types that may rely on the visual information at relevant levels.
• Score: 16.449212284367366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video question answering (VideoQA) is challenging given its multimodal combination of visual understanding and natural language processing. While most existing approaches ignore the visual appearance-motion information at different temporal scales, it is unknown how to incorporate the multilevel processing capacity of a deep learning model with such multiscale information. Targeting these issues, this paper proposes a novel Multilevel Hierarchical Network (MHN) with multiscale sampling for VideoQA. MHN comprises two modules, namely Recurrent Multimodal Interaction (RMI) and Parallel Visual Reasoning (PVR). With a multiscale sampling, RMI iterates the interaction of appearance-motion information at each scale and the question embeddings to build the multilevel question-guided visual representations. Thereon, with a shared transformer encoder, PVR infers the visual cues at each level in parallel to fit with answering different question types that may rely on the visual information at relevant levels. Through extensive experiments on three VideoQA datasets, we demonstrate improved performances than previous state-of-the-arts and justify the effectiveness of each part of our method.

Related papers

• Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want [58.091825321168514]
  We introduce the Draw-and-Understand project: a new model, a multi-domain dataset, and a challenging benchmark for visual prompting. Specifically, we propose a new end-to-end trained Multimodal Large Language Model (MLLM) that connects a vision encoder, a visual prompt encoder and an LLM. To advance visual prompting research for MLLMs, we introduce MDVP-Data and MDVP-Bench.
  arXiv  Detail & Related papers  (2024-03-29T16:26:20Z)
• MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering [73.61182342844639]
  We introduce a new model named Multi-modal Iterative Spatial-temporal Transformer (MIST) to better adapt pre-trained models for long-form VideoQA. MIST decomposes traditional dense spatial-temporal self-attention into cascaded segment and region selection modules. Visual concepts at different granularities are then processed efficiently through an attention module.
  arXiv  Detail & Related papers  (2022-12-19T15:05:40Z)
• M2HF: Multi-level Multi-modal Hybrid Fusion for Text-Video Retrieval [34.343617836027725]
  We propose a multi-level multi-modal hybrid fusion network to explore comprehensive interactions between text queries and each modality content in videos. Our framework provides two kinds of training strategies, including an ensemble manner and an end-to-end manner.
  arXiv  Detail & Related papers  (2022-08-16T10:51:37Z)
• Temporal Pyramid Transformer with Multimodal Interaction for Video Question Answering [13.805714443766236]
  Video question answering (VideoQA) is challenging given its multimodal combination of visual understanding and natural language understanding. This paper proposes a novel Temporal Pyramid Transformer (TPT) model with multimodal interaction for VideoQA.
  arXiv  Detail & Related papers  (2021-09-10T08:31:58Z)
• DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization [127.16984421969529]
  We introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.
  arXiv  Detail & Related papers  (2021-05-13T17:33:26Z)
• Frame Aggregation and Multi-Modal Fusion Framework for Video-Based Person Recognition [13.875674649636874]
  We propose a Frame Aggregation and Multi-Modal Fusion (FAMF) framework for video-based person recognition. FAMF aggregates face features and incorporates them with multi-modal information to identify persons in videos. We show that introducing an attention mechanism to NetVLAD can effectively decrease the impact of low-quality frames.
  arXiv  Detail & Related papers  (2020-10-19T08:06:40Z)
• Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers [89.00926092864368]
  We present a semantics-controlled multi-modal shuffled Transformer reasoning framework for the audio-visual scene aware dialog task. We also present a novel dynamic scene graph representation learning pipeline that consists of an intra-frame reasoning layer producing-semantic graph representations for every frame. Our results demonstrate state-of-the-art performances on all evaluation metrics.
  arXiv  Detail & Related papers  (2020-07-08T02:00:22Z)
• Video Understanding as Machine Translation [53.59298393079866]
  We tackle a wide variety of downstream video understanding tasks by means of a single unified framework. We report performance gains over the state-of-the-art on several downstream tasks including video classification (EPIC-Kitchens), question answering (TVQA), captioning (TVC, YouCook2, and MSR-VTT)
  arXiv  Detail & Related papers  (2020-06-12T14:07:04Z)
• Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA [96.10612095576333]
  We propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. Our model is also comprised of dual-level attention (word/object and frame level), multi-head self-cross-integration for different sources (video and dense captions), and which pass more relevant information to gates. We evaluate our model on the challenging TVQA dataset, where each of our model components provides significant gains, and our overall model outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2020-05-13T16:35:27Z)

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.