Tootfinder

Factorizing Diffusion Policies for Observation Modality Prioritization
Omkar Patil, Prabin Rath, Kartikay Pangaonkar, Eric Rosen, Nakul Gopalan
https://arxiv.org/abs/2509.16830 …

Factorizing Diffusion Policies for Observation Modality Prioritization
Diffusion models have been extensively leveraged for learning robot skills from demonstrations. These policies are conditioned on several observational modalities such as proprioception, vision and tactile. However, observational modalities have varying levels of influence for different tasks that diffusion polices fail to capture. In this work, we propose 'Factorized Diffusion Policies' abbreviated as FDP, a novel policy formulation that enables observational modalities to have differing influ…

Folks, @… is also running a donation campaign to get the families they’re helping out of the danger zone in the North and to relative safety in the South.
Her amazing work with Keep Hope Alive: A Gaza Giving Circle has been a huge inspiration for Gaza Verified.
Please follow, help, and share if you can.
Thank you!
💕

🇺🇦 Auf #radioeins läuft...
Methyl Ethel:
🎵 Scream Whole
#NowPlaying #MethylEthel
https://methylethel.bandcamp.com/track/scream-whole
https://open.spotify.com/track/4pyYNLVSzkJzTd66tP7Taz

SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI
Yuanhan Wang, Yifei Chen, Shuo Jiang, Wenjing Yu, Mingxuan Liu, Beining Wu, Jinying Zong, Feiwei Qin, Changmiao Wang, Qiyuan Tian
https://arxiv.org/abs/2509.17925

SmaRT: Style-Modulated Robust Test-Time Adaptation for Cross-Domain Brain Tumor Segmentation in MRI
Reliable brain tumor segmentation in MRI is indispensable for treatment planning and outcome monitoring, yet models trained on curated benchmarks often fail under domain shifts arising from scanner and protocol variability as well as population heterogeneity. Such gaps are especially severe in low-resource and pediatric cohorts, where conventional test-time or source-free adaptation strategies often suffer from instability and structural inconsistency. We propose SmaRT, a style-modulated robust…

MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Zheming Yang, Qi Guo, Yunqing Hu, Chang Zhao, Chang Zhang, Jian Zhao, Wen Ji
https://arxiv.org/abs/2509.16995

MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Multimodal large language models (MLLMs) enable powerful cross-modal inference but impose significant computational and latency burdens, posing severe challenges for deployment in resource-constrained environments. In this paper, we propose MoA-Off, an adaptive heterogeneous modality-aware offloading framework with edge-cloud collaboration for efficient MLLM inference. MoA-Off introduces a lightweight heterogeneous modality-aware module that estimates the complexity of heterogeneous inputs thro…

Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju, Tong Zhao, Donald Loveland, Leonardo Neves, Neil Shah
https://arxiv.org/abs/2512.17820 https://arxiv.org/pdf/2512.17820 https://arxiv.org/html/2512.17820
arXiv:2512.17820v1 Announce Type: new
Abstract: Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling. To do so, several works completely replace ID embeddings with modality embeddings, claiming that modality embeddings render ID embeddings unnecessary because they can match or even exceed ID embedding performance. On the other hand, many works jointly utilize ID and modality features, but posit that complex fusion strategies, such as multi-stage training and/or intricate alignment architectures, are necessary for this joint utilization. However, underlying both these lines of work is a lack of understanding of the complementarity of ID and modality features. In this work, we address this gap by studying the complementarity of ID- and text-based SR models. We show that these models do learn complementary signals, meaning that either should provide performance gain when used properly alongside the other. Motivated by this, we propose a new SR method that preserves ID-text complementarity through independent model training, then harnesses it through a simple ensembling strategy. Despite this method's simplicity, we show it outperforms several competitive SR baselines, implying that both ID and text features are necessary to achieve state-of-the-art SR performance but complex fusion architectures are not.
toXiv_bot_toot

🇺🇦 #NowPlaying on BBCRadio3's #RoundMidnight
Matt Wilde:
🎵 Everyday Words
#MattWilde
https://open.spotify.com/track/3hxax15bX4YjgMEDhcDGYc

Brainprint-Modulated Target Speaker Extraction
Qiushi Han, Yuan Liao, Youhao Si, Liya Huang
https://arxiv.org/abs/2509.17883 https://arxiv.org/pdf/2509.178…

Brainprint-Modulated Target Speaker Extraction
Achieving robust and personalized performance in neuro-steered Target Speaker Extraction (TSE) remains a significant challenge for next-generation hearing aids. This is primarily due to two factors: the inherent non-stationarity of EEG signals across sessions, and the high inter-subject variability that limits the efficacy of generalized models. To address these issues, we propose Brainprint-Modulated Target Speaker Extraction (BM-TSE), a novel framework for personalized and high-fidelity extra…

Can multimodal representation learning by alignment preserve modality-specific information?
Romain Thoreau, Jessie Levillain, Dawa Derksen
https://arxiv.org/abs/2509.17943 https…

Can multimodal representation learning by alignment preserve modality-specific information?
Combining multimodal data is a key issue in a wide range of machine learning tasks, including many remote sensing problems. In Earth observation, early multimodal data fusion methods were based on specific neural network architectures and supervised learning. Ever since, the scarcity of labeled data has motivated self-supervised learning techniques. State-of-the-art multimodal representation learning techniques leverage the spatial alignment between satellite data from different modalities acqu…

🇺🇦 Auf radioeins läuft...
Matilda Mann:
🎵 Just Because
#NowPlaying #MatildaMann
https://matildamann.bandcamp.com/track/just-because
https://open.spotify.com/track/6h6WgFMpWYbqekmeaXuaRl