OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models
📰 ArXiv cs.AI
Learn how OmniFocus improves inference efficiency in omni-modal large language models by compressing tokens using query-guided modality-balanced methods
Action Steps
- Implement query-guided token compression using OmniFocus to reduce inference costs in omni-modal LLMs
- Evaluate the performance of OmniFocus on audio-visual inputs to measure its effectiveness
- Compare the results with existing unimodal guidance methods to assess the benefits of modality-balanced compression
- Apply OmniFocus to real-world applications such as audio-visual question answering or multimodal dialogue systems
- Test the robustness of OmniFocus under different query types and input conditions
Who Needs to Know This
NLP engineers and researchers working on large language models can benefit from this technique to reduce inference costs and improve model efficiency
Key Insight
💡 OmniFocus reduces inference costs in omni-modal LLMs by compressing tokens using query-guided modality-balanced methods
Share This
🚀 Improve omni-modal LLM efficiency with OmniFocus: query-guided modality-balanced token compression! 🤖
Key Takeaways
Learn how OmniFocus improves inference efficiency in omni-modal large language models by compressing tokens using query-guided modality-balanced methods
Full Article
Title: OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models
Abstract:
arXiv:2607.03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost. Existing audio-visual token compression methods often rely on unimodal guidance, overlooking the temporal locality of query-relevant evidence in audio-visual inputs and implicitly assuming that the two modalities sha
Abstract:
arXiv:2607.03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost. Existing audio-visual token compression methods often rely on unimodal guidance, overlooking the temporal locality of query-relevant evidence in audio-visual inputs and implicitly assuming that the two modalities sha
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