TL;DR
The proposed FastVSearch method bridges efficient data indexing and deep multimodal understanding, enabling practical deployment for large-scale video search applications.
The explosive growth of large-scale video archives from surveillance networks, online platforms, and personal devices has made efficient and semantically rich video retrieval a critical challenge. Existing approaches based on deep multimodal embeddings have significantly improved retrieval accuracy. However, they often...
Modern vision-language models (VLMs) have shown promising results in long-video understanding due to the rich semantic information they can capture. However, most methods focus on coarse captioning of extracted image frames that are computationally inefficient and require models with large context windows. While past w...
Scene Text Retrieval (STR) aims to search images containing a given textual query within large-scale image collections. However, existing approaches are fundamentally constrained in two ways: 1) they are evaluated on narrow benchmarks that focus primarily on natural scenes; and 2) they rely on either error-prone multi-...
Streaming video understanding requires Vision Language Models (VLLMs) to process growing video streams and answer user questions under tight latency constraints. Existing methods improve efficiency through token pruning and memory-bank schemes, but mainly reduce visual tokens after visual encoding. Consequently, downst...
Large-scale image retrieval requires compact representations without substantially sacrificing retrieval accuracy. However, Vision Transformer Hashing (VTS) concatenates all output tokens before hash projection, resulting in a high-dimensional hashing head with considerable model and memory overhead. We replace this to...
Ad-hoc video search retrieves relevant video shots from large-scale collections using free-form textual queries. Pretrained vision-language models provide strong frame-level matching, but max-similarity aggregation can miss semantic evidence distributed across neighboring frames. We propose a lightweight retrieve-then-...
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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