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How I Study AI - Learn AI Papers & Lectures the Easy Way

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All SourcesarXiv
#pseudo-labeling

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Intermediate
Baorui Ma, Jiahui Yang et al.Jan 29arXiv

Metric Anything is a new way to teach AI real, ruler-like distances (metric depth) from very mixed and noisy 3D data.

#metric depth estimation#sparse metric prompt#monocular depth

VideoMaMa: Mask-Guided Video Matting via Generative Prior

Intermediate
Sangbeom Lim, Seoung Wug Oh et al.Jan 20arXiv

VideoMaMa is a model that turns simple black-and-white object masks into soft, precise cutouts (alpha mattes) for every frame of a video.

#video matting#alpha matte#binary segmentation mask

An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

Intermediate
Constantinos Karouzos, Xingwei Tan et al.Jan 9arXiv

Preference tuning teaches language models to act the way people like, but those habits can fall apart when the topic or style changes (domain shift).

#preference tuning#domain shift#supervised fine-tuning

Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation

Intermediate
Xin Lin, Meixi Song et al.Dec 18arXiv

This paper builds a foundation model called DAP that estimates real-world (metric) depth from any 360ยฐ panorama, indoors or outdoors.

#panoramic depth estimation#metric depth#360-degree vision