WildFireVQA is a new large-scale visual question answering benchmark that pairs RGB imagery with radiometric thermal measurements for aerial wildfire monitoring across six task categories.
A review of multi-modal large language and vision models
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Introduces a plug-and-play framework that generates varied texts and uses attribute reasoning plus video-guided loss to improve state-of-the-art Video-Language Models.
On Grace Hopper superchips, energy efficiency during multimodal training is governed by data movement and overlap rather than compute utilization, and runtime-optimal configurations are not always energy-optimal.
citing papers explorer
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WildFireVQA: A Large-Scale Radiometric Thermal VQA Benchmark for Aerial Wildfire Monitoring
WildFireVQA is a new large-scale visual question answering benchmark that pairs RGB imagery with radiometric thermal measurements for aerial wildfire monitoring across six task categories.
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Rethinking Video-Language Model from the Language Input Perspective
Introduces a plug-and-play framework that generates varied texts and uses attribute reasoning plus video-guided loss to improve state-of-the-art Video-Language Models.
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Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips
On Grace Hopper superchips, energy efficiency during multimodal training is governed by data movement and overlap rather than compute utilization, and runtime-optimal configurations are not always energy-optimal.