DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
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In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp
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Introduces the triplet segmentation task, CholecTriplet-Seg dataset with over 30,000 frames, and TargetFusionNet architecture extending Mask2Former for instance-level grounding of surgical <instrument, verb, target> triplets.
Jellyfish enables zero-shot federated unlearning through synthetic proxy data generation, channel-restricted knowledge disentanglement, and a composite loss with repair to forget target data while retaining model utility.
3D CNNs predict elastic moduli of nanoporous metals with R²=0.955, outperforming descriptor-based models, and transfer learning works on smaller denser datasets for large-scale Pareto optimization.
Strategic insertion of Global Average Pooling layers in VGG-16 reduces trainable parameters by 98%, maintains 66.4% ImageNet Top-1 accuracy, doubles translation robustness, and yields superior Spearman correlations in perceptual IQA tasks.
SpectralTrain is a universal training framework that combines curriculum learning and PCA spectral downsampling to deliver 2-7x faster training for hyperspectral image classification across multiple backbones and datasets with only small accuracy trade-offs.
FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.
A decision-support framework applies AFT models to show Nvidia L4 GPUs yield 20% longer adversarial survival time at 75% lower cost than V100, with inference latency as the strongest robustness predictor.
A three-stage framework combines dual-head CNNs, saliency attribution, neuroanatomical atlas mapping, and LLMs to generate interpretable reports for brain tumor classification on MRI images.
RL simulations find explicit action guidance in instructions and a specific ordering curriculum improve number composition learning and generalization.
MS-SSE-Net integrates multi-scale feature extraction and squeeze-and-excitation attention into DenseNet201, reaching 99.26% accuracy on the StructDamage dataset and outperforming the baseline by about 0.73 percentage points.
citing papers explorer
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DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
DyABD is the first benchmark dataset for abdominal muscle segmentation in dynamic MRIs featuring exercise-induced anatomical changes and pre/post-surgery scans, where existing models achieve an average Dice score of 0.82.
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Grounding Surgical Action Triplets with Instrument Instance Segmentation: A Dataset and Target-Aware Fusion Approach
Introduces the triplet segmentation task, CholecTriplet-Seg dataset with over 30,000 frames, and TargetFusionNet architecture extending Mask2Former for instance-level grounding of surgical <instrument, verb, target> triplets.
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Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement
Jellyfish enables zero-shot federated unlearning through synthetic proxy data generation, channel-restricted knowledge disentanglement, and a composite loss with repair to forget target data while retaining model utility.
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Transferable 3D Convolutional Neural Networks for Elastic Constants Prediction in Nanoporous Metals
3D CNNs predict elastic moduli of nanoporous metals with R²=0.955, outperforming descriptor-based models, and transfer learning works on smaller denser datasets for large-scale Pareto optimization.
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Parameter-Efficient Architectural Modifications for Translation-Invariant CNNs
Strategic insertion of Global Average Pooling layers in VGG-16 reduces trainable parameters by 98%, maintains 66.4% ImageNet Top-1 accuracy, doubles translation robustness, and yields superior Spearman correlations in perceptual IQA tasks.
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SpectralTrain: A Universal Framework for Hyperspectral Image Classification
SpectralTrain is a universal training framework that combines curriculum learning and PCA spectral downsampling to deliver 2-7x faster training for hyperspectral image classification across multiple backbones and datasets with only small accuracy trade-offs.
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FocalAD: Local Motion Planning for End-to-End Autonomous Driving
FocalAD adds an ego-local graph interactor and focal loss to prioritize decision-critical neighbors, yielding lower collision rates than prior methods on nuScenes, Bench2Drive, and especially the Adv-nuScenes robustness set.
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Survival of the Cheapest: Cost-Aware Hardware Adaptation for Adversarial Robustness
A decision-support framework applies AFT models to show Nvidia L4 GPUs yield 20% longer adversarial survival time at 75% lower cost than V100, with inference latency as the strongest robustness predictor.
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Bridging visual saliency and large language models for explainable deep learning in medical imaging
A three-stage framework combines dual-head CNNs, saliency attribution, neuroanatomical atlas mapping, and LLMs to generate interpretable reports for brain tumor classification on MRI images.
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Exploring Natural Language-Based Strategies for Efficient Number Learning in Children through Reinforcement Learning
RL simulations find explicit action guidance in instructions and a specific ordering curriculum improve number composition learning and generalization.
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MS-SSE-Net: A Multi-Scale Spatial Squeeze-and-Excitation Network for Structural Damage Detection in Civil and Geotechnical Engineering
MS-SSE-Net integrates multi-scale feature extraction and squeeze-and-excitation attention into DenseNet201, reaching 99.26% accuracy on the StructDamage dataset and outperforming the baseline by about 0.73 percentage points.