PicoEyes delivers a unified end-to-end model for full 3D gaze estimation including eye parameters, axes, segmentation and depth from monocular or binocular near-eye images, supported by a new large-scale multi-view dataset.
U- net: Convolutional networks for biomedical image segmen- tation
4 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 4years
2026 4representative citing papers
OA-VAT improves visual active tracking by combining instance-level prototype discrimination with occlusion-aware diffusion planning, reporting gains over prior SOTA on simulated and real drone benchmarks.
APEX adaptively extracts input-specific prompts from a memory using Fourier domain features and contrastive learning to boost generalization in medical image segmentation across seen and unseen domains.
The paper defines UAV reasoning segmentation, releases DRSeg—10,000 aerial images with reasoning QA and masks—and shows its PixDLM baseline beats prior reasoning-segmentation models under fine-tuning.
citing papers explorer
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PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset
PicoEyes delivers a unified end-to-end model for full 3D gaze estimation including eye parameters, axes, segmentation and depth from monocular or binocular near-eye images, supported by a new large-scale multi-view dataset.
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Instance-level Visual Active Tracking with Occlusion-Aware Planning
OA-VAT improves visual active tracking by combining instance-level prototype discrimination with occlusion-aware diffusion planning, reporting gains over prior SOTA on simulated and real drone benchmarks.
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From Adaptation to Generalization: Adaptive Visual Prompting for Medical Image Segmentation
APEX adaptively extracts input-specific prompts from a memory using Fourier domain features and contrastive learning to boost generalization in medical image segmentation across seen and unseen domains.
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PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
The paper defines UAV reasoning segmentation, releases DRSeg—10,000 aerial images with reasoning QA and masks—and shows its PixDLM baseline beats prior reasoning-segmentation models under fine-tuning.