Introduction

The proliferation of video content across diverse platforms, ranging from social media giants to enterprise archives and surveillance systems, has created an unprecedented volume of visual data. This exponential growth presents a significant challenge in terms of information retrieval and knowledge discovery. Traditional methods of video search, which often rely heavily on manual tagging and associated metadata, are increasingly inadequate to navigate and extract meaningful insights from this vast repository of information. The inherent limitations of these conventional approaches stem from the inconsistencies and incompleteness often found in manually generated metadata, as well as their fundamental inability to analyze and understand the rich visual and auditory information contained within the video itself. Even platforms primarily focused on video content, such as YouTube, face difficulties in providing truly effective search capabilities, highlighting the complexities involved. Consequently, there is a pressing need for advanced video search technologies that can transcend these limitations and offer more intuitive and powerful ways to access and analyze video data.

Advanced multimodal video search aims to address these challenges by allowing users to employ a variety of input modalities, including text, voice, image, and raw video, to formulate their queries. This approach recognizes that users may have different ways of expressing their search intent and seeks to leverage the full spectrum of information available in a video. Furthermore, the ability to search at the shot level, identifying specific scenes or moments within a video, is crucial for precise content retrieval, saving users time and effort in sifting through lengthy recordings. Beyond mere keyword matching or visual similarity, true understanding of video content requires semantic analysis, enabling the discovery of contextually relevant information based on the meaning of the video's content, including objects, actions, and overall narrative. This report will delve into the current landscape of both commercial products and open-source projects that are striving to provide these advanced multimodal video search capabilities, analyzing their features, underlying technologies, and potential for addressing the growing need for intelligent video content management and analysis.

Commercial Platforms for Multimodal Video Search

Several commercial platforms are emerging that offer advanced video analysis and search functionalities, leveraging multimodal AI to provide users with more effective ways to interact with their video content. These platforms often integrate various features, including metadata enrichment, analytics, and content management, with multimodal search capabilities.

• Coactive's Multimodal Application Platform (MAP) is presented as an AI-powered solution designed to transform content analytics. It supports multimodal querying for video analytics at scale, enabling users to search across image, video, and audio assets to identify trends. While text and voice input are not explicitly detailed, the term "multimodal querying" suggests the platform's ability to handle different input types. Coactive also focuses on visual semantic understanding, employing AI to interpret moods, actions, themes, and logos within video content. This semantic analysis, coupled with scene detection and keyframe analysis features, allows for a granular understanding of video content, potentially enabling search at a shot level. Furthermore, the platform offers SQL capabilities within its user interface, allowing for custom queries to uncover insights about visual assets, such as tag frequency and content trends. This suggests that Coactive aims to provide not just a search tool but a comprehensive analytics platform for visual media. The example provided in the documentation, where a user can search for "families at the dinner table" by providing a visual keyframe example to guide the model, even if the initial tag is just "family," directly illustrates the platform's ability to combine different input modalities for precise insights.
• ContextIQ, while presented in a research paper, outlines a multimodal expert-based video retrieval system specifically designed for contextual advertising. This system utilizes modality-specific experts for video, audio, transcripts, and metadata (including objects, actions, and emotions) to create semantically rich video representations. The primary focus of ContextIQ is text-to-video retrieval, aligning with the needs of advertising campaigns. However, the underlying architecture, which relies on specialized models for different modalities, implies the potential for handling other input modalities as well, with the "video expert" likely analyzing visual content. The system aims to provide semantically rich video representations, indicating an understanding of the content beyond surface-level features. While image or raw video input as direct search queries are not explicitly mentioned, the expert-based approach suggests a sophisticated system capable of deep video content analysis, which could potentially be extended to support a wider range of input modalities in a commercialized version.
• Videospace by Babbobox explicitly positions itself as a video analytics and search platform powered by multimodal AI. A key feature is its "Deep Video Search," which utilizes multimodal AI to understand speech, text, audio, and visuals within video content. This platform boasts extensive support for various input modalities, including speech recognition in over 120 languages (enabling voice search), translation in over 60 languages, tags derived from both speech and visual content, object detection covering over,000 objects (indicating image and video analysis capabilities), logo detection, face detection, and emotion detection. While raw video input as a search query is not explicitly mentioned, the platform's comprehensive understanding of visual content and its ability to combine various data elements suggest this could be a possibility or a future development. Videospace's claim of being the first to integrate diverse video data elements into a unified search platform underscores its commitment to multimodal functionality.
• TwelveLabs offers a video intelligence platform that utilizes multimodal AI to understand video content in terms of time and space. The platform supports search across speech, text, audio, and visuals, promising fast, precise, and context-aware results with semantic understanding. This directly addresses the user's need for multiple input modalities and semantic analysis. The ability to "find any scene in natural language" strongly suggests a granular search capability, likely at the shot level. TwelveLabs emphasizes its high accuracy and the capacity to manage very large video libraries. The platform's AI models, such as Marengo and Pegasus, can be customized by training them on user-specific data. Furthermore, TwelveLabs integrates with Voxel51, an open-source tool, to facilitate semantic video search across multiple modalities, enabling the identification of movements, actions, objects, people, sounds, on-screen text, and speech within videos. Also it integrated with Databricks’ vector search (Databricks Mosaic AI), this solution supports semantic queries across billion-scale datasets, enabling applications like personalized recommendations and automated content curation.
• Google Cloud Video AI provides a suite of tools for precise video analysis, capable of recognizing over,000 objects, places, and actions at various levels of granularity, including video, shot, and frame. This indicates a strong capability for semantic understanding of visual content and the ability to perform analysis at the shot level. The platform allows users to search their video catalogs in a manner similar to searching documents, implying support for text-based queries. Additionally, it offers AutoML Video Intelligence for creating custom entity labels, allowing users to tailor the analysis to their specific needs. While the snippets do not explicitly state support for voice, image, or raw video as search queries, the platform's robust video analysis capabilities and integration within the broader Google Cloud ecosystem suggest potential for future expansion in these areas.
• EnterpriseTube is an enterprise video platform that leverages AI to ensure the searchability, accessibility, and organization of video content. It offers AI-powered video search that allows users to locate videos using spoken words (voice), in-video text, topics, metadata, and more. The platform can also detect objects, faces, people, activities, and sentiments within videos, indicating the use of image and video analysis for search purposes. EnterpriseTube provides features like AI transcription and tagging, automatic metadata generation (including topics and labels), and AI-powered summarization, all of which contribute to semantic understanding. While shot-level granularity is not explicitly mentioned, the ability to detect specific elements and jump to the exact moment they appear suggests a fine-grained search capability that likely aligns with shot-level requirements.
• muse.ai is presented as an AI-powered video search platform with automatic AI indexing capabilities. It enables users to search within their videos using speech, text, people, objects, sounds, and actions. This confirms support for text, voice, and image/video input as search criteria. The platform's AI analysis automatically labels videos based on their content, which suggests a detailed understanding, likely at the shot level. Furthermore, muse.ai indicates semantic understanding through its ability to search for "related concepts" across multiple videos and "surface relevant clips and discover unexpected insights". This goes beyond simple keyword matching to understand the underlying meaning and context of the video content.
• Moments Lab's MXT-1.5, while described on a commercial website, is presented as a multimodal AI for accurate video indexing, claiming superior accuracy in scene descriptions compared to other open-source VLMs. It offers features like sound bite detection and shot sequence identification, suggesting a focus on semantic understanding and shot-level analysis.
• While Webex AI focuses on enhancing communication and collaboration within the Webex suite through AI-powered features for audio, video, and text, its capabilities as a multimodal video search platform for stored content with shot-level granularity and semantic understanding are not clearly detailed in the provided information. Similarly, Vimeo, while an AI-powered video platform offering various tools for video workflows, does not prominently feature the specific multimodal video search capabilities requested by the user in the provided snippet. Cincopa, an enterprise solution for managing media assets, also lacks explicit mention of advanced multimodal video search features in the given context. Movingimage, an enterprise video platform focused on productivity, mentions intelligent search functions but does not provide specifics on multimodal input support or shot-level granularity.

To provide a clearer comparison, the following table summarizes the key multimodal search features of the identified commercial platforms based on the provided snippets, where we consider the end to end ability to understand the raw video semantically as highlighted feature:

Open-Source Projects and Libraries

The open-source landscape offers a variety of projects and libraries that can be leveraged to build custom multimodal video search solutions. These range from foundational vision-language models to specialized tools for shot detection and video indexing.

• VideoCLIP-XL for Long-Description Understanding, the Alibaba PAI team's VideoCLIP-XL represents a paradigm shift in open-source video understanding, specifically addressing CLIP's 77-token limitation through three architectural innovations. The model processes 512-token descriptions via its VILD dataset—2 million video-long text pairs automatically aggregated from platforms like YouTube and WikiHow—enabling paragraph-level semantic alignment. Unlike traditional CLIP variants that truncate descriptions, VideoCLIP-XL employs Text-similarity-guided Primary Component Matching (TPCM) to dynamically preserve 32-64 critical attributes from long texts based on cosine similarity with short summaries, achieving 14.3% R@1 improvement over ViCLIP on MSR-VTT benchmarks.
• PySceneDetect is a free and open-source Python library and command-line tool for detecting shot changes in videos. It offers various algorithms for shot detection, including content-aware, adaptive, and threshold-based methods, and can automatically split videos into scenes. This library is fundamental for achieving shot-level granularity in any video analysis pipeline.
• The Video-Shot-Detection repository on GitHub provides Python implementations of four different shot detection algorithms: Histogram Intersect, Moment Invariant, Motion Vector, and Twin Comparison 23. This project offers users more direct control over the shot detection process and allows for experimentation with different techniques.
• Voxel51 FiftyOne is an open-source tool for managing and working with machine learning datasets, including video. While its native multimodal search capabilities for video are not detailed in the snippets, it integrates with Twelve Labs, enabling semantic video search across multiple modalities. FiftyOne's strengths lie in dataset curation, model evaluation, and visualization of embeddings, making it a valuable tool for developing and analyzing multimodal video search systems.
• Many other notable examples include Llama 3.2 Vision, NVLM, Molmo, Pixtral, Qwen2-VL, MiniCPM-Llama3-V.6, Florence-2, and Idefics2, each with unique strengths in image and text understanding, and some with explicit video comprehension capabilities.

Underlying Technologies and Techniques

The ability to perform multimodal video search with shot-level granularity and semantic understanding relies on several key underlying technologies and techniques.

• Multimodal Embedding Spaces, the integration of diverse modalities,text, audio, and visual,requires embedding models that map inputs into a unified vector space. Open-source frameworks like CLIP (Contrastive Language–Image Pretraining) generate joint embeddings for text and images, enabling cross-modal search (e.g., text-to-image retrieval). Commercial platforms such as Twelve Labs’ Embed API enhance this by incorporating audio waveforms and temporal video dynamics, creating embeddings that capture interactions between visual expressions, body language, and spoken words. Google Cloud’s Vertex AI further scales this with 1408-dimension vectors supporting interchangeable queries across images, text, and videos.
• Spatial-Temporal Representation and Feature Preservation, modern video vectorization techniques extend beyond frame-by-frame analysis to model temporal coherence. For instance, tetrahedral meshes decompose video volumes into 3D structures that preserve spatial-temporal relationships, enabling feature-aware simplification ratios as low as% while maintaining color fidelity. This approach aligns with academic efforts to unify motion, action, and context in embeddings, as seen in MM-ViT’s transformer architecture, which processes compressed video modalities (I-frames, motion vectors, residuals) to outperform CNN-based models in action recognition tasks.
• Long-Description Understanding, the VideoCLIP-XL framework addresses a critical limitation in conventional CLIP architectures - their inability to process extended textual descriptions tied to video content. While standard CLIP models use 77-token positional embeddings effectively capping text understanding at ~20 meaningful tokens. VideoCLIP-XL's long-description capability complements temporal modeling approaches like tetrahedral meshes through cross-attention layers that weight visual concepts against textual mentions of duration (e.g., "gradual camera pan" vs "sudden cut"). The model's 768D embeddings capture temporal semantics through:
  • Frame-sequence aware positional encoding
  • Motion vector integration from compressed video streams
  • Dynamic attribute selection via TPCM during multi-second video processing

This enables precise alignment between textual phrases describing temporal progression ("the suspect first entered, then removed the item") and corresponding visual sequences in surveillance footage

• Vision-Language Models (VLMs) and Large Language Models (LLMs) with multimodal capabilities provide the core intelligence for understanding video content. VLMs can process both visual and textual data simultaneously, enabling tasks like answering natural language questions about video content. Multimodal LLMs, such as GPT-4 and Gemini, extend these capabilities by understanding and generating content across various modalities, allowing for more complex interactions with video data, such as finding scenes based on a combination of visual and textual descriptions. These models learn complex relationships between visual and textual cues from vast amounts of training data, making them crucial for semantic video understanding.
• Achieving shot-level granularity requires the ability to automatically segment videos into individual shots or scenes. This is accomplished through shot boundary detection (SBD) techniques. These methods analyze the visual content between consecutive frames to detect abrupt or gradual changes that indicate a scene transition. Common approaches involve comparing frame histograms, pixel differences, or edge information. Open-source tools like PySceneDetect provide implementations of various SBD algorithms that can be integrated into video analysis pipelines. Accurate shot boundary detection is a prerequisite for performing search and analysis at a granular, shot-by-shot level.
• Semantic information extraction is essential for enabling search based on the meaning and context of video content. This involves identifying and classifying various elements within the video, such as objects (using object detection models), actions (using action recognition models), scenes, and the spoken content (using Automatic Speech Recognition - ASR). The transcribed text can then be analyzed using Natural Language Processing (NLP) techniques to extract keywords, entities, and sentiment. Knowledge graphs can be used to provide additional contextual information and understand the relationships between the extracted entities. By extracting this semantic information, video search systems can move beyond simple keyword matching and understand the user's intent based on the actual content of the video.

Challenges and Limitations of Existing Solutions

Despite significant advancements in multimodal retrieval technology, several challenges persist with current approaches:

• Limited Native Video Support: Currently, very few embedding models can directly process raw video input. Most methods still rely on extracting individual frames, which leads to the loss of temporal information and negatively impacts retrieval performance.
• Lack of Commercial-Grade Video Embedding Models: There is a scarcity of high-performance, commercially available video embedding models capable of supporting large-scale multimodal search.
• Fragmented Representation Spaces Across Modalities: Many existing models map text, images, and video into separate, distinct embedding spaces. This fragmentation hinders the effectiveness of cross-modal retrieval.
• Limited Granularity in Video Retrieval: Traditional methods primarily operate at the segment or clip level, lacking the precision to pinpoint content at the shot or second level.
• Monolingual Focus: The majority of video-text retrieval models are optimized primarily for English, limiting their applicability in multilingual scenarios.

Our new model developed

Our model effectively addresses these limitations through native video input support, a unified multimodal representation space, and integrated temporal sequence learning.

Key Features

• Largest Video Embedding Model to Date: With 3 billion (3B) parameters, this is the largest known video embedding model currently available.
• First Unified Video-Text Embedding Model: This is currently the only model that integrates video and text representations into a single, unified embedding space.
• Native Video Embedding: Processes video input directly, rather than relying on frame extraction, thereby preserving crucial temporal information.
• Unified Multimodal Representation: Supports seamless retrieval across text, images, and video, enabling powerful cross-modal search capabilities.
• Absolute Temporal Alignment: Incorporates a mechanism for absolute temporal alignment, allowing the model to learn time sequences and motion speeds. This enables precise localization of specific moments within videos and better utilization of temporal context.
• Bi-directional Multimodal Retrieval: Supports multiple search directions:
  • Text-to-Video: Find relevant video content based on text descriptions.
  • Video-to-Text: Retrieve corresponding text descriptions for a given video.
  • Image-to-Video: Find matching video shots using a static image query.
  • Video-to-Image: Extract the most representative keyframes from a video.
• Bilingual Support (Chinese & English): Delivers high-quality retrieval performance in both Chinese and English environments, making it suitable for global applications.

Training Details

• Model Scale: 3 Billion (3B) parameters.
• Training Dataset:
  • Comprised 1.8 million video shots, covering a diverse range of scenes and content.
  • All videos were compressed to 360p resolution to improve training efficiency.
  • Total storage footprint: 2.7 TB.
• Compute Resources:
  • Training utilized a total of 6,000 GPU hours.
  • Hardware: Tesla L40S GPUs.
  • Platform: AWS G6e instances.

Evaluation

Dataset: Shot2Story

We evaluate the model on the Shot2Story dataset, a key benchmark for shot-level video-text retrieval.

Dataset Description

Shot2Story (Han et al., 2023) comprises 20,000 video clips, accompanied by:

• Shot-level Captions: Detailed descriptions for individual video shots, accurately capturing visual details and scene transitions.
• Video Summaries: Providing a higher-level overview of the video content to facilitate understanding of the overall video structure.

The dataset's annotations were automatically generated by Large Multimodal Models (LMMs) and subsequently refined manually. This process ensures the reliability of both captions and summaries, establishing it as a high-quality benchmark for video-text matching.

Furthermore, this dataset was not used during the training phase, ensuring the fairness and generalization ability of our evaluation.

Evaluation Metrics

We employ Recall@5 and Recall@10 as the retrieval evaluation metrics:

• Recall@5: The percentage of queries for which the correct result is found within the top 5 retrieved items.
• Recall@10: The percentage of queries for which the correct result is found within the top 10 retrieved items.

Some highlights:

• Our model demonstrates substantial gains in recall over baseline methods for both English and Chinese queries.
• Enhanced Cross-Lingual Retrieval: Unlike traditional methods that typically show markedly weaker performance in Chinese retrieval compared to English, our model significantly reduces this performance disparity, improving its suitability for multilingual applications.
• More Effective Utilization of Temporal Information: Compared to models relying solely on frame-based features (such as Jina-CLIP-v2), our approach better captures the temporal relationships within videos, yielding retrieval results that align more closely with semantic meaning.

Actual query examples along with the retrieved video shots will be shown in the demostration website, along with our future roadmap, stay tuned or ping me directly.