EmbeddingGemma 2: Cross-Modal Video Search in FiftyOne

EmbeddingGemma 2, a 740-million-parameter open multimodal model from Google DeepMind, maps text, images, video, and audio into a shared 768-dimensional embedding space, enabling cross-modal similarity search such as finding video clips with text descriptions or using audio to retrieve visually related footage. The post demonstrates its integration with FiftyOne using 10,000 clips from the VGGSound dataset, where separate embeddings for video frames, audio, and combined video-audio content are stored and indexed with LanceDB. These indexes support text-to-media retrieval, audio-to-video searches, dataset curation through uniqueness and representativeness scoring, UMAP visualization, and zero-shot classification across VGGSound’s 309 sound categories. It also describes a Cross-Modal Retrieval panel for the FiftyOne App that allows users to search indexes with text, selected samples, uploaded files, webcam input, or microphone recordings without writing code. The article notes practical considerations including optional GPU acceleration, persistent LanceDB storage configuration, reduced-quality tradeoffs at smaller embedding dimensions, and limitations arising from noisy automated dataset labels, the partial VGGSound shard, and comparatively weaker audio-only retrieval for classes primarily defined by visible sources.

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