VideoRefer - A video object perception and reasoning technology jointly developed by Zhejiang University and Alibaba DAMO Academy
VideoRefer, jointly developed by Zhejiang University and Alibaba DAMO Academy, is specifically designed for object perception and reasoning within videos. Based on the spatial-temporal understanding capabilities of Enhanced Video Large Language Models (Video LLMs), the model can...
What is VideoRefer?
VideoRefer, jointly developed by Zhejiang University and Alibaba DAMO Academy, is specifically designed for object perception and reasoning within videos. Leveraging the spatial-temporal understanding capabilities of Enhanced Video Large Language Models (Video LLMs), it enables fine-grained perception and reasoning of any object within a video. VideoRefer is built upon three core components: the VideoRefer-700K dataset, providing large-scale, high-quality object-level video instruction data; the VideoRefer model, equipped with a multi-functional spatial-temporal object encoder supporting single-frame and multi-frame input for accurate perception, reasoning, and retrieval of any object in a video; and the VideoRefer-Bench benchmark, used to comprehensively evaluate the model's performance in video referencing tasks, driving the development of fine-grained video understanding technology.
The main functions of VideoRefe
- Fine-grained video object understandingIt enables precise perception and understanding of any object in a video, capturing detailed information such as the object's spatial location, appearance features, and motion state.
- Complex Relationship AnalysisAnalyze the complex relationships between multiple objects in a video, such as interactions and changes in relative positions, to understand the interactions and influences between objects.
- Reasoning and PredictionBased on the understanding of video content, reasoning and prediction are made, such as inferring the future behavior or state of an object, and predicting the development trend of an event.
- Video object retrievalBased on user-specified objects or conditions, it retrieves relevant objects or scene segments from videos, achieving accurate video content retrieval.
- Multimodal interactionIt supports multimodal interaction with users, such as interacting with users based on text commands, voice prompts or image tags, understanding user needs and providing corresponding video understanding results.
The technical principles of VideoRefer
- Multi-agent data engineIntroducing a multi-agent data engine that uses multiple expert models (such as video understanding models and segmentation models) to work collaboratively and automatically generate high-quality object-level video instruction data, including detailed descriptions, short descriptions, and multi-turn question-and-answer pairs, providing ample and diverse data support for model training.
- Spatial-temporal object encoderThis paper designs a multifunctional spatial-temporal object encoder, including a spatial marker extractor and an adaptive temporal marker merging module. The spatial marker extractor is used to extract precise regional features of objects from a single frame, while the temporal marker merging module merges features from adjacent frames in multi-frame mode based on the similarity of object features, capturing the continuity and changes of objects in the temporal dimension and generating rich object-level representations.
- Fusion and DecodingThe system integrates global scene-level features, object-level features, and language instructions from the video to form a unified input sequence. This sequence is then fed into a pre-trained Large Language Model (LLM) for decoding, generating fine-grained semantic understanding results of the video content, such as textual information like object descriptions, relationship analysis, and inference predictions.
- Comprehensive evaluation benchmarkThe VideoRefer-Bench evaluation benchmark is constructed, including two sub-benchmarks: description generation and multiple-choice question answering. It comprehensively evaluates the model's performance in video referencing tasks from multiple dimensions (such as topic correspondence, appearance description, time description, illusion detection, etc.) to ensure the effectiveness and reliability of the model in fine-grained video understanding.
VideoRefer's project address
- Project official website:https://damo-nlp-sg.github.io/VideoRefer/
- GitHub repository:https://github.com/DAMO-NLP-SG/VideoRefer
- HuggingFace model library:https://huggingface.co/DAMO-NLP-SG/VideoRefer
- arXiv technical paper:https://arxiv.org/pdf/2501.00599
Application scenarios of VideoRefer
- Video editingIt helps editors quickly find specific shots or scenes, improving editing efficiency.
- educateBased on students' learning progress, we recommend suitable video clips to help them learn efficiently.
- Security monitoringIt can identify abnormal behavior in surveillance videos in real time, issue alarms in a timely manner, and ensure safety.
- Interactive RobotIt enables convenient home operation by controlling smart home devices based on video commands.
- e-commerceAnalyze product videos to inspect product quality and ensure that listed products meet standards.