EarthSight reduces average compute time per image by 1.9x and 90th-percentile end-to-end latency from 51 to 21 minutes by distributing inference decisions between orbit and ground with shared backbones and early rejection filters.
Unleashing the power of multi-task learning: A comprehensive survey spanning traditional, deep, and pretrained foundation model eras
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Introduces progressive task-specific multi-task adaptation for vision transformers, sharing adapters early and specializing later with gradient-based task allocation, outperforming prior methods on PASCAL and NYUD-v2 with fewer trainable parameters.
FLAME is an MoE architecture using modality-specific routers and low-rank compression of expert knowledge to support efficient continual multimodal multi-task learning while reducing catastrophic forgetting.
citing papers explorer
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EarthSight: A Distributed Framework for Low-Latency Satellite Intelligence
EarthSight reduces average compute time per image by 1.9x and 90th-percentile end-to-end latency from 51 to 21 minutes by distributing inference decisions between orbit and ground with shared backbones and early rejection filters.
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Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation
Introduces progressive task-specific multi-task adaptation for vision transformers, sharing adapters early and specializing later with gradient-based task allocation, outperforming prior methods on PASCAL and NYUD-v2 with fewer trainable parameters.
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FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning
FLAME is an MoE architecture using modality-specific routers and low-rank compression of expert knowledge to support efficient continual multimodal multi-task learning while reducing catastrophic forgetting.