Papers Weakly-supervised Learning
“Weakly-supervised Learning” 태그가 달린 논문 613편 · 필터 해제
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data
We present RASO, a foundation model designed to Recognize Any Surgical Object, offering robust open-set recognition capabilities across a broad range of surgical procedures and object classes, in both surgical images and…
Action RecognitionOpen Set LearningTAGWeakly-supervised LearningSelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation
We present SelfPrompt, a novel prompt-tuning approach for vision-language models (VLMs) in a semi-supervised learning setup. Existing methods for tuning VLMs in semi-supervised setups struggle with the negative impact of…
Language ModelingLanguage ModellingPseudo LabelWeakly-supervised LearningTowards Robust Incremental Learning under Ambiguous Supervision
Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotation uncertainty and ambiguity, collectin…
Incremental LearningPartial Label LearningWeakly-supervised LearningViSoLex: An Open-Source Repository for Vietnamese Social Media Lexical Normalization
ViSoLex is an open-source system designed to address the unique challenges of lexical normalization for Vietnamese social media text. The platform provides two core services: Non-Standard Word (NSW) Lookup and Lexical No…
Lexical NormalizationWeakly-supervised LearningWeakly Supervised Learning on Large Graphs
Graph classification plays a pivotal role in various domains, including pathology, where images can be represented as graphs. In this domain, images can be represented as graphs, where nodes might represent individual nu…
ClassificationGraph AttentionGraph ClassificationWeakly-supervised LearningODA-GAN: Orthogonal Decoupling Alignment GAN Assisted by Weakly-supervised Learning for Virtual Immunohistochemistry Staining
Recently, virtual staining has emerged as a promising alternative to revolutionize histological staining by digitally generating stains. However, most existing methods suffer from the curse of staining unreality and …
Contrastive LearningGenerative Adversarial NetworkVirtual StainingWeakly-supervised Learning+1FG^2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching
We propose a novel fine-grained cross-view localization method that estimates the 3 Degrees of Freedom pose of a ground-level image in an aerial image of the surroundings by matching fine-grained features between the…
Weakly-supervised LearningHisynSeg: Weakly-Supervised Histopathological Image Segmentation via Image-Mixing Synthesis and Consistency Regularization
Tissue semantic segmentation is one of the key tasks in computational pathology. To avoid the expensive and laborious acquisition of pixel-level annotations, a wide range of studies attempt to adopt the class activation …
Image SegmentationSegmentationSemantic SegmentationWeakly-supervised Learning+22by2: Weakly-Supervised Learning for Global Action Segmentation
This paper presents a simple yet effective approach for the poorly investigated task of global action segmentation, aiming at grouping frames capturing the same action across videos of different activities. Unlike the ca…
Action SegmentationWeakly-supervised LearningLabel up: Learning Pulmonary Embolism Segmentation from Image Level Annotation through Model Explainability
Pulmonary Embolisms (PE) are a leading cause of cardiovascular death. Computed tomographic pulmonary angiography (CTPA) stands as the gold standard for diagnosing pulmonary embolisms (PE) and there has been a lot of inte…
Weakly-supervised LearningExact: Exploring Space-Time Perceptive Clues for Weakly Supervised Satellite Image Time Series Semantic Segmentation
Automated crop mapping through Satellite Image Time Series (SITS) has emerged as a crucial avenue for agricultural monitoring and management. However, due to the low resolution and unclear parcel boundaries, annotating p…
Semantic SegmentationTime SeriesWeakly-supervised Learninglibcll: an Extendable Python Toolkit for Complementary-Label Learning
Complementary-label learning (CLL) is a weakly supervised learning paradigm for multiclass classification, where only complementary labels -- indicating classes an instance does not belong to -- are provided to the learn…
Weakly-supervised LearningForming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
Learning from label proportions (LLP), i.e., a challenging weakly-supervised learning task, aims to train a classifier by using bags of instances and the proportions of classes within bags, rather than annotated labels f…
Weakly-supervised LearningWeakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network
The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the fiel…
Anomaly DetectionAnomaly Detection In Surveillance VideosMultiple Instance LearningSupervised Anomaly Detection+2Efficient Whole Slide Image Classification through Fisher Vector Representation
The advancement of digital pathology, particularly through computational analysis of whole slide images (WSI), is poised to significantly enhance diagnostic precision and efficiency. However, the large size and complexit…
Diagnosticimage-classificationImage ClassificationWeakly-supervised Learning+1LFSamba: Marry SAM with Mamba for Light Field Salient Object Detection
A light field camera can reconstruct 3D scenes using captured multi-focus images that contain rich spatial geometric information, enhancing applications in stereoscopic photography, virtual reality, and robotic vision. I…
Mambaobject-detectionObject DetectionRelation+2Distribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation
Despite alleviating the dependence on dense annotations inherent to fully supervised methods, weakly supervised point cloud semantic segmentation suffers from inadequate supervision signals. In response to this challenge…
Semantic SegmentationWeakly-supervised LearningAnalysis of Hybrid Compositions in Animation Film with Weakly Supervised Learning
We present an approach for the analysis of hybrid visual compositions in animation in the domain of ephemeral film. We combine ideas from semi-supervised and weakly supervised learning to train a model that can segment h…
Weakly-supervised LearningSSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot
The 3D reconstruction of plants is challenging due to their complex shape causing many occlusions. Next-Best-View (NBV) methods address this by iteratively selecting new viewpoints to maximize information gain (IG). Deep…
3D ReconstructionComputational EfficiencySelf-Supervised LearningWeakly-supervised LearningAn Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
Partial Label Learning (PLL) is a typical weakly supervised learning task, which assumes each training instance is annotated with a set of candidate labels containing the ground-truth label. Recent PLL methods adopt iden…
Partial Label LearningWeakly-supervised Learning