VeRVE uses a shared MLLM backbone with contrastive alignment and LoRA training to surpass other MLLM methods on zero-shot video retrieval while enabling competitive moment retrieval and state-of-the-art composed retrieval without further training.
Repre- sentation learning with contrastive predictive coding, 2019
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ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.
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VeRVE: Versatile Retrieval for Videos via Unified Embeddings
VeRVE uses a shared MLLM backbone with contrastive alignment and LoRA training to surpass other MLLM methods on zero-shot video retrieval while enabling competitive moment retrieval and state-of-the-art composed retrieval without further training.
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ID-Sim: An Identity-Focused Similarity Metric
ID-Sim is a new similarity metric that aims to capture human selective sensitivity to identities by training on curated real and generative synthetic data and validating against human annotations on recognition, retrieval, and generative tasks.