1,376 research outputs found
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation
learning, yet acquiring dense and pixel-wise ground-truth is both labor- and
time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an
economical alternative, with which training phase could hardly generate
satisfactory performance unfortunately. In order to generate high-quality
annotated data with a low time cost for accurate segmentation, in this paper,
we propose a novel annotation enrichment strategy, which expands existing
coarse annotations of training data to a finer scale. Extensive experiments on
the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural
networks trained with the enriched annotations from our framework yield a
significant improvement over that trained with the original coarse labels. It
is highly competitive to the performance obtained by using human annotated
dense annotations. The proposed method also outperforms among other
state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge
Managemen
Neural Locality Sensitive Hashing for Entity Blocking
Locality-sensitive hashing (LSH) is a fundamental algorithmic technique
widely employed in large-scale data processing applications, such as
nearest-neighbor search, entity resolution, and clustering. However, its
applicability in some real-world scenarios is limited due to the need for
careful design of hashing functions that align with specific metrics. Existing
LSH-based Entity Blocking solutions primarily rely on generic similarity
metrics such as Jaccard similarity, whereas practical use cases often demand
complex and customized similarity rules surpassing the capabilities of generic
similarity metrics. Consequently, designing LSH functions for these customized
similarity rules presents considerable challenges. In this research, we propose
a neuralization approach to enhance locality-sensitive hashing by training deep
neural networks to serve as hashing functions for complex metrics. We assess
the effectiveness of this approach within the context of the entity resolution
problem, which frequently involves the use of task-specific metrics in
real-world applications. Specifically, we introduce NLSHBlock (Neural-LSH
Block), a novel blocking methodology that leverages pre-trained language
models, fine-tuned with a novel LSH-based loss function. Through extensive
evaluations conducted on a diverse range of real-world datasets, we demonstrate
the superiority of NLSHBlock over existing methods, exhibiting significant
performance improvements. Furthermore, we showcase the efficacy of NLSHBlock in
enhancing the performance of the entity matching phase, particularly within the
semi-supervised setting
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