Tootfinder

Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
Gang Yang
https://arxiv.org/abs/2508.03403 https://arxiv.org/…

Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
Hyperspectral unmixing aims at estimating material signatures (known as endmembers) and the corresponding proportions (referred to abundances), which is a critical preprocessing step in various hyperspectral imagery applications. This study develops a novel approach called sparsity and total variation (TV) constrained multilayer linear unmixing (STVMLU) for hyperspectral imagery. Specifically, based on a multilayer matrix factorization model, to improve the accuracy of unmixing, a TV constraint…

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The Coup:
🎵 Fat Cats, Bigga Fish
#TheCoup
https://chopperman.bandcamp.com/track/the-coup-fat-cats-bigga-fish
https://open.spotify.com/track/74mjiDuK4B0yKtMBoVWt51

Telefónica-CEO Haas: Haben noch keinen Euro mit Glasfaser verdient
Knapp die Hälfte der deutschen Haushalte könnte Glasfaser buchen. Doch der Netzausbau bleibe wegen VDSL ein Minusgeschäft, beklagt der deutsche Telefónica-Chef.

Constraint-Preserving Data Generation for Visuomotor Policy Learning
Kevin Lin, Varun Ragunath, Andrew McAlinden, Aaditya Prasad, Jimmy Wu, Yuke Zhu, Jeannette Bohg
https://arxiv.org/abs/2508.03944

Constraint-Preserving Data Generation for Visuomotor Policy Learning
Large-scale demonstration data has powered key breakthroughs in robot manipulation, but collecting that data remains costly and time-consuming. We present Constraint-Preserving Data Generation (CP-Gen), a method that uses a single expert trajectory to generate robot demonstrations containing novel object geometries and poses. These generated demonstrations are used to train closed-loop visuomotor policies that transfer zero-shot to the real world and generalize across variations in object geome…

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Loaded Honey:
🎵 Don’t Speak
#LoadedHoney
https://cavi.bandcamp.com/track/loaded-honey-dont-speak-cavi-edit
https://open.spotify.com/track/3HGP6DStszOkaGl1ZtDYGC

This https://arxiv.org/abs/2506.00433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Latent Wavelet Diffusion: Enabling 4K Image Synthesis for Free
High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual detail. We present Latent Wavelet Diffusion (LWD), a lightweight framework that enables any latent diffusion model to scale to ultra-high-resolution image generation (2K to 4K) for free. LWD introduces three key components: (1) a scale-consistent variational autoencoder objective that enhances the spectral fidelity of la…

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Death Cab for Cutie:
🎵 Soul Meets Body
#DeathCabforCutie
https://souzalex.bandcamp.com/track/death-cab-for-cutie-soul-meets-body-souza-remix
https://open.spotify.com/track/6Wuv7MrhesuDsuGv8vcrOd

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Xinzhu Li, Juepeng Zheng, Yikun Chen, Xudong Mao, Guanghui Yue, Wei Zhou, Chenlei Lv, Ruomei Wang, Fan Zhou, Baoquan Zhao
https://arxiv.org/abs/2508.03397

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition
Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait r…

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Ice Cube:
🎵 Check Yo Self (Remix)
#IceCube
https://mototembo.bandcamp.com/track/ice-cube-check-yo-self-moto-tembo-edit
https://open.spotify.com/track/6juIzbXd7ERNLhYCcOpi7u

RadProPoser: A Framework for Human Pose Estimation with Uncertainty Quantification from Raw Radar Data
Jonas Leo Mueller, Lukas Engel, Eva Dorschky, Daniel Krauss, Ingrid Ullmann, Martin Vossiek, Bjoern M. Eskofier
https://arxiv.org/abs/2508.03578

RadProPoser: A Framework for Human Pose Estimation with Uncertainty Quantification from Raw Radar Data
Radar-based human pose estimation (HPE) provides a privacy-preserving, illumination-invariant sensing modality but is challenged by noisy, multipath-affected measurements. We introduce RadProPoser, a probabilistic encoder-decoder architecture that processes complex-valued radar tensors from a compact 3-transmitter, 4-receiver MIMO radar. By incorporating variational inference into keypoint regression, RadProPoser jointly predicts 26 three-dimensional joint locations alongside heteroscedastic al…