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MM-VID: Advancing Video Understanding with GPT-4V(ision)

Kevin Lin, Faisal Ahmed, Linjie Li, Chung-Ching Lin, Ehsan Azarnasab, Zhengyuan Yang, Jianfeng Wang, Lin Liang, Zicheng Liu, Yumao Lu, Ce Liu, Lijuan Wang

arXiv:2310.19773v1cs.CV

TL;DR

Long videos and multi-episode narratives pose challenges for understanding multimodal sequences, while many existing models focus on short clips or predefined actions. MM-VID combines GPT-4V with specialized tools to generate video scripts and support video-understanding tasks. The paper reports capabilities across hour-long videos, episodic analysis, and interactive game and GUI environments.

  • Problem

    Long-form video understanding requires analyzing multimodal sequences across extended durations and episodes, while many existing models focus on short clips or predefined actions.

    Source: §1 · p. 2

  • Method

    MM-VID combines GPT-4V and specialized tools to process video and speech, generate clip descriptions, and use GPT-4 to form a coherent video script.

    Source: §1 · p. 2, §4 · p. 3

  • Results

    MM-VID is reported to handle hour-long videos, multi-episode analysis, character and speaker identification, and interactive game and GUI tasks.

    Source: §1 · p. 2, §7 · p. 10

  • Takeaways & Limitations

    The reported applications span long-video analysis and interaction with video games and graphical user interfaces.

    Source: §1 · p. 2, §7 · p. 10

    MM-VID-generated audio descriptions received lower overall satisfaction among blind than sighted participants; the authors cite differing modality access and possible lack of listening practice.

    Source: Hypotheses and Results · p. 9

Abstract

from arXiv · show

We present MM-VID, an integrated system that harnesses the capabilities of GPT-4V, combined with specialized tools in vision, audio, and speech, to facilitate advanced video understanding. MM-VID is designed to address the challenges posed by long-form videos and intricate tasks such as reasoning within hour-long content and grasping storylines spanning multiple episodes. MM-VID uses a video-to-script generation with GPT-4V to transcribe multimodal elements into a long textual script. The generated script details character movements, actions, expressions, and dialogues, paving the way for large language models (LLMs) to achieve video understanding. This enables advanced capabilities, including audio description, character identification, and multimodal high-level comprehension. Experimental results demonstrate the effectiveness of MM-VID in handling distinct video genres with various video lengths. Additionally, we showcase its potential when applied to interactive environments, such as video games and graphic user interfaces.

1. Introduction

Real-world video understanding must handle diverse modalities and extended narratives, while many existing models focus on short clips or predefined actions. MM-VID turns videos into scripts with GPT-4V and reports capabilities spanning long videos and interactive settings.

1. Introduction

  • MM-VID addresses long-video challenges involving multimodal sequences, speaker and character identification, narrative coherence, and real-time dynamic environments.The motivating scope includes videos over an hour and content across multiple episodes.Source: §1 · p. 2
  • Existing video models often target short clips or predefined action classes, motivating methods for detailed understanding of real-world video.The paper proposes breaking extended content into coherent narratives for subsequent analysis.Source: §1 · p. 2
  • MM-VID combines GPT-4V with specialized tools: it processes video and speech, describes scene-based clips, then uses GPT-4 to generate a coherent full-video script.Available video metadata may also condition script generation.Source: §1 · p. 2
  • MM-VID is reported to comprehend hour-long videos, localize events with timestamps, and predict possible next steps in video games or GUI interaction.These reported results span video analysis and interactive environments.Source: §1 · p. 2
  • The Figure 2 pipeline maps a video file through four modules to a long textual script describing its contents.The modules are multimodal pre-processing, external knowledge collection, clip-level video description generation, and script generation.Source: Figure 2 · p. 3

2. Related Work

Prior video-understanding approaches include foundation models, video-language models, visual instruction tuning, and tool-using LLM systems. The paper positions MM-VID as an integration of GPT-4V with specialized tools for video understanding.

Conventional Video Understanding Methods

  • Video foundation models have analyzed short clips, typically under 30 seconds, but often use vision alone and may need adjustment for multimodal downstream tasks.Video-language models also commonly use clips around 10 seconds, which can challenge longer-sequence comprehension.Source: §2 · p. 3

Video-Language Models

  • Video-language models improve multimodal video-and-language representations for tasks including question answering, retrieval, and captioning, often through pretraining followed by task-specific fine-tuning.Their training is usually based on short clips.Source: §2 · p. 3

Visual Instruction Tuning

  • Visual instruction tuning combines a frozen LLM, image encoder, and learnable modules to generate textual video descriptions, but may fall short on extended-duration videos.Source: §2 · p. 3

Prompting LLMs for Video Understanding

  • Tool-integrating LLM systems such as VLog transcribe video into text descriptions for question answering; MM-VID explores this paradigm with GPT-4V.VLog combines image captioners, Whisper ASR, and ChatGPT.Source: §2 · p. 3

3. Preliminary Study with GPT-4V(ision)

The preliminary discussion describes GPT-4V as accepting visual and textual inputs and generating contextually relevant text. It also identifies frame sequences as a means for interpreting temporal relationships and dynamic content.

3. Preliminary Study with GPT-4V(ision)

  • GPT-4V can accept textual descriptions, questions, images, and short video clips as inputs.Source: §3 · p. 3
  • GPT-4V combines visual-input comprehension with contextually relevant text generation.Source: §3 · p. 3
  • Sequences of frames can help GPT-4V grasp temporal relationships and interactions in dynamic visual content.Source: §3 · p. 3

4. MM-VID

MM-VID converts a video file into a script through multimodal preprocessing, external knowledge collection, clip-level descriptions, and script generation. It also supports a streaming-input setting in which frames are continually received and processed.

4. MM-VID

  • Multimodal preprocessing extracts speech with ASR and divides video into short clips, each sampled with 10 frames.Scene detection helps identify crucial scene boundaries.Source: §4 · pp. 3–4
  • For streaming inputs, MM-VID continually receives and processes video frames as an agent in an interactive environment.Source: Figure 3 · p. 4
  • MM-VID can collect metadata, titles, abstracts, and character face photos as external knowledge for GPT-4V prompts.The experiments gather metadata, title, and abstract from YouTube.Source: §4 · p. 4
  • GPT-4V generates detailed descriptions of clip frames, while visual prompts pairing character faces and names can improve character identification.Source: §4 · p. 4
  • GPT-4 integrates clip descriptions into a coherent script used for diverse video-understanding tasks.Source: §4 · p. 4

5. MM-VID for Streaming Inputs

For streaming inputs, MM-VID treats incoming frames as states and processes them with GPT-4V to make decisions and generate responses. The system is presented for applications including game play, embodied agents, and GUI navigation.

  • MM-VID continually receives streaming video frames as states representing the visual information unfolding in the environment.Source: §5 · p. 4
  • GPT-4V processes these states to make informed decisions and generate responses.Source: §5 · p. 4
  • The streaming approach is presented for applications such as video game play, embodied agents, and GUI navigation.Source: §5 · p. 4

6.1. Experiment Setup

MM-VID is implemented on the MM-REACT codebase, with separate tools for speech recognition and scene detection.

  • The implementation is based on the MM-REACT codebase.Source: §6.1 · p. 4
  • It uses Azure Cognitive Services APIs for ASR and PySceneDetect for scene detection.Source: §6.1 · p. 4

6.2. MM-VID Capabilities

MM-VID combines scene detection, ASR, external knowledge, GPT-4V clip descriptions, and GPT-4 script generation to support video understanding. Demonstrations cover grounded multimodal question answering, long and episodic videos, character and speaker identification, audio description, self-refinement, and short-form clips.

  • MM-VID processes videos with expert tools and external knowledge, generates clip-level descriptions with GPT-4V, then uses GPT-4 to assemble a coherent script.Source: §6.2 · p. 4, Figure 4 · p. 5
  • The generated script supports grounded answers by recording timestamps and events, including identifying a home-run highlight and answering questions about people in the video.Source: §6.2 · p. 4
  • MM-VID integrates visual frames, speech transcriptions, and available external knowledge to answer questions using visual and auditory cues.Source: §6.2 · pp. 4, 6
  • For an approximately 50-minute documentary, MM-VID generates a timestamped script that supports summarization, video-specific queries, and indexing pivotal moments.Source: §6.2 · p. 6
  • Across multiple lengthy episodes, MM-VID demonstrates series summarization, cross-episode reasoning, character-journey descriptions, and grounded question answering.Source: §6.2 · p. 6
  • Visual prompts using character face photos and names improve character identification, while visual context can help ASR identify speakers and attribute their speech.Source: §6.2 · p. 6
  • For a short film with limited or no speech, MM-VID generates a detailed script without ASR inputs; self-refinement can correct script errors such as misidentifying a bird in blurry footage.Source: §6.2 · p. 6
  • Demonstrations span documentaries, episodic series, and short-form clips, with MM-VID adapting its descriptions to varied video types.Source: §6.2 · p. 6

6.3. Applications to Interactive Environments

In interactive settings, MM-VID uses egocentric video and game frames to assist with practical tasks and generate possible control actions. It also predicts next steps from GUI screenshots and prior user actions.

  • For an egocentric home video, MM-VID helps locate items and lists the wearer’s activities within a specified time range.Source: §6.3 · p. 6
  • The questionnaire for participants with visual impairments asks them to rate distinct auditory criteria on a 0–10 scale.Source: Table 1 · p. 7
  • In the Mario game demonstration, the agent receives three frames as states and calculates possible control actions, producing reasonable actions for game play.Source: §6.3 · p. 7
  • For GUI navigation, the agent uses iPhone screenshots and previous user actions to predict next steps, including searches and placing an order.Source: §6.3 · p. 7

6.4. User Study

The user study compared MM-VID-generated and human-crafted audio descriptions with 4 participants with visual impairments and 5 with normal vision. Ratings were generally close, while satisfaction was lower for MM-VID-generated descriptions, and participants noted both benefits and concerns.

6.4.1 Evaluation Procedure

  • Participants compared human-crafted and MM-VID-generated AD versions narrated with text-to-speech, rating responses on a 0–10 Likert scale.Participants with visual impairments rated auditory cues only, while those with normal vision considered both visual and auditory elements.Source: §6.4.1 · p. 7

6.4.2 Results on the Group with Visual Impairments

  • The evaluation used 3 videos lasting 1 minute, 1 minute 42 seconds, and 2 minutes 42 seconds, with 4 participants with visual impairments and 5 with normal vision.All participants had normal hearing; the visually impaired participants were experienced with screen readers and other common accessibility tools.Source: §6.4.2 · pp. 7–8, §6.4 · p. 7

Hypotheses and Results

  • For participants with visual impairments, MM-VID-generated AD scored 8.91 ± 1.23 for voice and audio quality and 7.14 ± 1.39 for effective delivery, versus 9.07 ± 0.65 and 8.33 ± 0.90 for human-crafted AD.Informativeness scores were 7.14 ± 1.16 for MM-VID-generated AD and 9.29 ± 0.91 for human-crafted AD; overall satisfaction was around 2 points lower for MM-VID-generated AD.Source: Hypotheses and Results · p. 8
  • MM-VID-generated AD was considered cost-effective and scalable, with the authors suggesting it could make videos lacking professional audio description accessible to people with visual impairments.The study also notes that dialogue overlap and GPT-4V hallucinations were reported as difficulties.Source: Hypotheses and Results · p. 8
  • For participants with normal vision, MM-VID-generated AD scored 7.83 ± 1.24 for clarity and accuracy and 8.73±0.49 for conciseness, versus 8.9 ± 0.74 and 9.16±0.54 for human-curated AD.MM-VID-generated AD scored 7.97 ± 1.54 for informativeness and 8.53 ± 0.58 for timing and synchronization, compared with 8.90 ± 0.90 and 8.59 ± 0.95 for human-crafted AD; satisfaction was <1 points lower.Source: Hypotheses and Results · pp. 8–9

6.4.4 Participant Feedback

  • Participants with visual impairments described detailed AD as helping them follow storylines and expressed interest in using the service for content including live conferences.Some participants also voiced concerns about subjective descriptions, possible made-up stories, and repeated use of character names instead of pronouns.Source: §6.4.4 · p. 9

7. Conclusion

MM-VID uses GPT-4V to turn video content into detailed scripts and reports effectiveness across challenging video-understanding tasks. A user study found its generated audio descriptions closely mirror human-crafted quality.

7. Conclusion

  • MM-VID transcribes video content into long, detailed scripts with GPT-4V, supporting hour-long video comprehension, multi-episode analysis, character and speaker identification, and interaction with games and GUIs.The conclusion describes these as challenging tasks addressed by the system.Source: §7 · p. 10
  • A user study found that MM-VID-generated audio descriptions closely mirror the quality of human-crafted descriptions.The study drew feedback from a varied group of participants.Source: §7 · p. 10
  • Future work will explore SoM and object tracking techniques to enhance tasks and functionalities.Source: §7 · p. 10

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