VIGiA: Instructional Video Guidance via Dialogue Reasoning and Retrieval

• URL: http://arxiv.org/abs/2602.19146v1
• Date: Sun, 22 Feb 2026 12:20:28 GMT
• Title: VIGiA: Instructional Video Guidance via Dialogue Reasoning and Retrieval
• Authors: Diogo Glória-Silva, David Semedo, João Maglhães,
• Abstract summary: We introduce VIGiA, a novel multimodal dialogue model designed to understand and reason over complex, multi-step instructional video action plans.<n>Our evaluation shows that VIGiA outperforms existing state-of-the-art models on all tasks in a conversational plan guidance setting, reaching over 90% accuracy on plan-aware VQA.
• Score: 2.836258000910872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce VIGiA, a novel multimodal dialogue model designed to understand and reason over complex, multi-step instructional video action plans. Unlike prior work which focuses mainly on text-only guidance, or treats vision and language in isolation, VIGiA supports grounded, plan-aware dialogue that requires reasoning over visual inputs, instructional plans, and interleaved user interactions. To this end, VIGiA incorporates two key capabilities: (1) multimodal plan reasoning, enabling the model to align uni- and multimodal queries with the current task plan and respond accurately; and (2) plan-based retrieval, allowing it to retrieve relevant plan steps in either textual or visual representations. Experiments were done on a novel dataset with rich Instructional Video Dialogues aligned with Cooking and DIY plans. Our evaluation shows that VIGiA outperforms existing state-of-the-art models on all tasks in a conversational plan guidance setting, reaching over 90\% accuracy on plan-aware VQA.

Related papers

• Visual Planning: Let's Think Only with Images [46.501897093584965]
  We argue that language may not always be the most natural or effective modality for reasoning, particularly in tasks involving spatial and geometrical information.<n>Motivated by this, we propose a new paradigm, Visual Planning, which enables planning through purely visual representations for these "vision-first" tasks.<n>In this paradigm, planning is executed via sequences of images that encode step-by-step inference in the visual domain, akin to how humans sketch or visualize future actions.
  arXiv  Detail & Related papers  (2025-05-16T16:17:22Z)
• Show and Guide: Instructional-Plan Grounded Vision and Language Model [9.84151565227816]
  We present MM-PlanLLM, the first multimodal plan-following language model. We bring cross-modality through two key tasks: Conversational Video Moment Retrieval and Visually-Informed Step Generation. MM-PlanLLM is trained using a novel multitask-multistage approach.
  arXiv  Detail & Related papers  (2024-09-27T18:20:24Z)
• Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey [59.95153883166705]
  Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture. Visual Instruction Tuning (VIT) has been intensively studied recently, which finetunes a large vision model with language as task instructions. This work aims to provide a systematic review of visual instruction tuning, covering (1) the background that presents computer vision task paradigms and the development of VIT; (2) the foundations of VIT that introduce commonly used network architectures, visual instruction tuning frameworks and objectives, and evaluation setups and tasks; and (3) the commonly used datasets in visual instruction tuning and evaluation.
  arXiv  Detail & Related papers  (2023-12-27T14:54:37Z)
• EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning [84.6451394629312]
  We introduce EgoPlan-Bench, a benchmark to evaluate the planning abilities of MLLMs in real-world scenarios. We show that EgoPlan-Bench poses significant challenges, highlighting a substantial scope for improvement in MLLMs to achieve human-level task planning. We also present EgoPlan-IT, a specialized instruction-tuning dataset that effectively enhances model performance on EgoPlan-Bench.
  arXiv  Detail & Related papers  (2023-12-11T03:35:58Z)
• Multitask Multimodal Prompted Training for Interactive Embodied Task Completion [48.69347134411864]
  Embodied MultiModal Agent (EMMA) is a unified encoder-decoder model that reasons over images and trajectories. By unifying all tasks as text generation, EMMA learns a language of actions which facilitates transfer across tasks.
  arXiv  Detail & Related papers  (2023-11-07T15:27:52Z)
• Video Language Planning [137.06052217713054]
  Video language planning is an algorithm that consists of a tree search procedure, where we train (i) vision-language models to serve as both policies and value functions, and (ii) text-to-video models as dynamics models. Our algorithm produces detailed multimodal (video and language) specifications that describe how to complete the final task. It substantially improves long-horizon task success rates compared to prior methods on both simulated and real robots.
  arXiv  Detail & Related papers  (2023-10-16T17:48:45Z)
• Look Before you Speak: Visually Contextualized Utterances [88.58909442073858]
  We create a task for predicting utterances in a video using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Our model achieves state-of-the-art performance on a number of downstream VideoQA benchmarks.
  arXiv  Detail & Related papers  (2020-12-10T14:47:02Z)
• Video-Grounded Dialogues with Pretrained Generation Language Models [88.15419265622748]
  We leverage the power of pre-trained language models for improving video-grounded dialogue. We propose a framework by formulating sequence-to-grounded dialogue tasks as a sequence-to-grounded task. Our framework allows fine-tuning language models to capture dependencies across multiple modalities.
  arXiv  Detail & Related papers  (2020-06-27T08:24:26Z)
• Object Relational Graph with Teacher-Recommended Learning for Video Captioning [92.48299156867664]
  We propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model.
  arXiv  Detail & Related papers  (2020-02-26T15:34:52Z)

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.