NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
Efficientnet: Rethinking model scaling for convolutional neural networks
8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
IonMorphNet is a ConvNeXt-based classifier trained on six spatial pattern classes from 53 MSI datasets that performs generalizable peak picking and improves mSCF1 by 7% over prior methods while also aiding tumor classification via ion selection.
Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
CraterBench-R is a new retrieval benchmark where self-supervised ViTs with a training-free instance-token aggregation method achieve high accuracy for identifying individual craters while reducing storage needs.
DLED reformulates open-set face forgery detection as an uncertainty estimation task and uses dual-level spatial-frequency evidence collection to identify novel fake categories, claiming 20% average gains over baselines.
SWNet combines visible and NIR spectra with a Pyramid Vision Transformer, bimodal gated fusion, and edge refinement to outperform prior methods on camouflaged weed detection in the Weeds-Banana dataset.
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.
A staged multimodal fusion model for predicting six continuous emotion intensities from in-the-wild video achieves 0.4722 validation and 0.57 test Pearson correlation in the EMI challenge.
citing papers explorer
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NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity
NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
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IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging
IonMorphNet is a ConvNeXt-based classifier trained on six spatial pattern classes from 53 MSI datasets that performs generalizable peak picking and improves mSCF1 by 7% over prior methods while also aiding tumor classification via ion selection.
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Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection
Camo-M3FD is a curated visible-thermal benchmark dataset for cross-spectral camouflaged pedestrian detection, with annotations and baseline evaluations showing the value of fusion.
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CraterBench-R: Instance-Level Crater Retrieval for Planetary Scale
CraterBench-R is a new retrieval benchmark where self-supervised ViTs with a training-free instance-token aggregation method achieve high accuracy for identifying individual craters while reducing storage needs.
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Open Set Face Forgery Detection via Dual-Level Evidence Collection
DLED reformulates open-set face forgery detection as an uncertainty estimation task and uses dual-level spatial-frequency evidence collection to identify novel fake categories, claiming 20% average gains over baselines.
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SWNet: A Cross-Spectral Network for Camouflaged Weed Detection
SWNet combines visible and NIR spectra with a Pyramid Vision Transformer, bimodal gated fusion, and edge refinement to outperform prior methods on camouflaged weed detection in the Weeds-Banana dataset.
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Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.
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Two-Stage Multimodal Framework for Emotion Mimicry Intensity Prediction
A staged multimodal fusion model for predicting six continuous emotion intensities from in-the-wild video achieves 0.4722 validation and 0.57 test Pearson correlation in the EMI challenge.