ExPLoRe turns MoE dispatch weights into per-patch loss coefficients for multi-objective masked image modeling, reporting gains on ImageNet-1K and ADE20K transfer.
Milan: Masked image pretraining on language assisted representation
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
UNIV introduces Patch Cross-modal Contrastive Learning (PCCL) to build a unified semantic feature space for infrared and visible modalities, supported by the new MVIP dataset of 98,992 aligned pairs, with reported gains on infrared segmentation and detection tasks.
Survey benchmarks SSL instance discrimination and masked image modeling for object detection, finding instance discrimination suits CNN encoders while MIM suits ViT encoders and custom pre-training, especially for small objects.
citing papers explorer
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ExPLoRe: Expert Patch-Level Loss Routing for Multi-Objective Masked Image Modeling
ExPLoRe turns MoE dispatch weights into per-patch loss coefficients for multi-objective masked image modeling, reporting gains on ImageNet-1K and ADE20K transfer.
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UNIV: Unified Foundation Model for Infrared and Visible Modalities
UNIV introduces Patch Cross-modal Contrastive Learning (PCCL) to build a unified semantic feature space for infrared and visible modalities, supported by the new MVIP dataset of 98,992 aligned pairs, with reported gains on infrared segmentation and detection tasks.
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Self-Supervised Learning for Real-World Object Detection: a Survey
Survey benchmarks SSL instance discrimination and masked image modeling for object detection, finding instance discrimination suits CNN encoders while MIM suits ViT encoders and custom pre-training, especially for small objects.