Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
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Updated
Jun 4, 2024 - Python
Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
Code for ALBEF: a new vision-language pre-training method
Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering
A curated (most recent) list of resources for Learning with Noisy Labels
Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation [Inoue+, CVPR2018].
Single-Stage Semantic Segmentation from Image Labels (CVPR 2020)
Weakly-supervised object detection.
Mask-Free Video Instance Segmentation [CVPR 2023]
Weakly Supervised Instance Segmentation using Class Peak Response, in CVPR 2018 (Spotlight)
DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image
Weakly Supervised Learning for Findings Detection in Medical Images
[EMNLP 2020] Text Classification Using Label Names Only: A Language Model Self-Training Approach
BOND: BERT-Assisted Open-Domain Name Entity Recognition with Distant Supervision
PyTorch implementation of "WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation", CVPR 2017
Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
Caffe codes for our papers "Multiple Instance Detection Network with Online Instance Classifier Refinement" and "PCL: Proposal Cluster Learning for Weakly Supervised Object Detection".
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
Recent weakly supervised semantic segmentation paper
[NAACL 2021] This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach'.
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