#weak supervision try — diffusion models for protein design efficient attention beyond transformers papers like retrieval-augmented generation
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optimization traffic engineering transportation engineering tribology type theory ultracold quantum gases uncertainty quantification urban climate modeling urban computing urban economics urban planning vehicular communications wireless communications wireless sensor networks Any year 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986
6 papers match
q-bio.GN 2026 ☆
PlantBGC: Transformer for Plant BGC Discovery via Label-Free Domain Adaptation and Weak Supervision Yuhan Zhao, Nidhi Grover, Zhishan Guo +1
The paper presents PlantBGC, a transformer-based model that transfers knowledge from microbial biosynthetic gene clusters to plant genomes using label-free domain adaptation and we…
#biosynthetic gene clusters #plant genomics #transformer models #domain adaptation
cs.CV 2026 ☆
Weakly-Supervised RGB-D Salient Object Detection via SAM-driven Pseudo Annotation and State Space Interaction-based Diffusion Wenqi Si, Gongyang Li, Shixiang Shi +1
The paper proposes a weakly‑supervised RGB‑D salient object detection framework that expands sparse scribble labels into dense pseudo annotations using the Segment Anything Model a…
#salient object detection #rgb-d data #weak supervision #diffusion models
cs.CV 2026 ☆
Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels Yaqi Zhao, Haoliang Sun, Yating Wang +2
The paper introduces Holistic Optimal Label Selection (HopS), which combines a local density‑based filter with a global optimal‑transport objective to choose reliable labels for pr…
#prompt learning #partial labels #label selection #optimal transport
cs.LG 2026 ☆
Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning Wei Tang, Yin-Fang Yang, Weijia Zhang +1
The paper introduces a calibratable disambiguation loss (CDL) that improves both classification accuracy and confidence calibration for multi-instance partial-label learning by inc…
#multi-instance learning #partial-label learning #calibration #weak supervision
cs.CV 2026 ☆
Weakly Supervised Spatio-Temporal Candidate Discovery of Dairy Farm Sites from Seasonal Satellite Imagery Usman Haider, Fatima Khalid, Karl Mason
The paper presents a weakly supervised method that uses multi‑season Sentinel satellite images and OpenStreetMap priors to rank and cluster candidate dairy farm locations, reducing…
#weak supervision #satellite imagery #farm detection #seasonal representation learning
cs.CV 2026 ☆
Active Learning for Efficient Annotation of Surgical Videos with Weak Supervision Manasa Dendukuri, Matjaz Jogan, Daniel A. Hashimoto +1
The paper presents a human‑in‑the‑loop framework that combines active learning with dual‑loss weak supervision to cut the effort needed for annotating laparoscopic video frames, en…
#active learning #weak supervision #surgical video segmentation #human-in-the-loop
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