24 skills found
ruanxiang / Mr Saliencya python implementation of manifold ranking saliency
lrsoenksen / SPL UD DLA reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to im- proved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatolog- ical patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.
yangchuancv / Ranking Saliencyc++ code for paper "saliency detection via graph-based manifold ranking" by chuan yang, lihe zhang, huchuan lu, xiang ruan, ming-hsuan yang
EricFH / SORimplementation of "Salient Object Ranking with Position-Preserved Attention"
EricDengbowen / QAGNetOfficial repository for CVPR 2024 paper "Advancing Saliency Ranking with Human Fixations: Dataset, Models and Benchmarks".
islamamirul / RsdnetSaliency Ranking
dragonlee258079 / Saliency RankingCode release for the TPAMI 2021 paper "Instance-Level Relative Saliency Ranking with Graph Reasoning" by Nian Liu, Long Li, Wangbo Zhao, Junwei Han, and Ling Shao.
MinglangQiao / Awesome Salient Object RankingA curated list of awesome resources for salient object ranking (SOR)
GrassBro / OCORBi-directional Object-context Prioritization Learning for Saliency Ranking
mbar0075 / SaRLVisionA reinforcement learning object detector leveraging saliency ranking, offering a self-explainable system with a fully observable action log. | B.Sc. IT (Hons) Artificial Intelligence Dissertation | University of Malta Dean's List Awards 2024
zhulei2016 / RST SaliencyHere is the code and saliency maps for the paper 'Saliency Pattern Detection by Ranking Structured Trees' in ICCV 2017.
yuanyc06 / RrSource code of the CVPR 2015 paper "Robust saliency detection via regularized random walks ranking"
KamalaSowmya / DiscussionSummarizationDiscussion Summarization is the process of condensing a text document which is a collection of discussion threads, using CBS (Cluster Based Summarization) approach in order to create a relevant summary which enlists most of the important points of the original thematic discussion, thereby providing the users, both concise and comprehensive piece of information. This outlines all the opinions which are described from multiple perspectives in a single document. This summary is completely unbiased as they present information extracted from multiple sources based on a designed algorithm, without any editorial touch or subjective human intervention. Extractive methods used here, follow the technique of selecting a subset of existing words, phrases, or sentences in the original text to form the summary. An iterative ranking algorithm is followed for clustering. The NLP (Natural Language Processing) is used to process human language data. Precisely, it is applied while working with corpora, categorizing text, analyzing linguistic structure. Thus, the quick summary is aimed at being salient, relevant and non-redundant. The proposed model is validated by testing its ability to generate optimal summary of discussions in Yahoo Answers. Results show that the proposed model is able to generate much relevant summary when compared to present summarization techniques.
guanhuankang / SeqRankPaper "SeqRank: Sequential Ranking of Salient Objects" is accepted in AAAI-24.
ruohaoguo / PavsodrOfficial Implementation of "Instance-Level Panoramic Audio-Visual Saliency Detection and Ranking" [ACM MM 2024].
zxccade / SHINE(ECCV2024) SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal Grounding
zyf-815 / VSORpaper for "A Motion-aware Spatio-temporal Graph for Video Salient Object Ranking"
MengkeSong / Saliency Ranking ParadigmOfficial implementation of “Rethinking Object Saliency Ranking: A Novel Whole-flow Processing Paradigm”.
jiayusun / KSR ALVisual Saliency Detection via Kernelized Subspace Ranking with Active Learning, TIP2019
ssecv / PSRThe official implementation of ACM MM 2023 "Partitioned Saliency Ranking with Dense Pyramid Transformers"
