Predicting quantized latent residuals (Latent Drift with FSQ) avoids identity collapse and noise interpolation, improving patient-specific 3D MRI neuro-forecasting over diffusion and autoregressive baselines.
In: Proceedings of the IEEE/CVF international confer- ence on computer vision
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A multi-view transformer framework integrating rotational micro-ultrasound sweeps with biopsy frames achieves 87.2% patient-level AUROC for prostate cancer detection, outperforming single-frame and video baselines.
Hi-GaTA is a hierarchical gated temporal aggregation adapter that uses short-to-long temporal pyramids and gated fusion to enable surgical video report generation, backed by a new 214-video benchmark and a surgical ViViT pretrained on 40,000 minutes of video.
Logic Gate Networks produce compact Boolean-circuit descriptors for video copy detection that match or exceed prior accuracy at over 11k inferences per second and orders-of-magnitude smaller size.
citing papers explorer
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Progression as Latent Drift: Generative Forecasting of Slow-Evolving Pathologies
Predicting quantized latent residuals (Latent Drift with FSQ) avoids identity collapse and noise interpolation, improving patient-specific 3D MRI neuro-forecasting over diffusion and autoregressive baselines.
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Compass: Prostate Cancer Detection Needs Multi-View Context
A multi-view transformer framework integrating rotational micro-ultrasound sweeps with biopsy frames achieves 87.2% patient-level AUROC for prostate cancer detection, outperforming single-frame and video baselines.
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Hi-GaTA: Hierarchical Gated Temporal Aggregation Adapter for Surgical Video Report Generation
Hi-GaTA is a hierarchical gated temporal aggregation adapter that uses short-to-long temporal pyramids and gated fusion to enable surgical video report generation, backed by a new 214-video benchmark and a surgical ViViT pretrained on 40,000 minutes of video.
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Efficient Logic Gate Networks for Video Copy Detection
Logic Gate Networks produce compact Boolean-circuit descriptors for video copy detection that match or exceed prior accuracy at over 11k inferences per second and orders-of-magnitude smaller size.