256 research outputs found
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Query on Knowledge Graphs with Hierarchical Relationships
The dramatic popularity of graph database has resulted in a growing interest in graph queries. Two major topics are included in graph queries. One is based on structural relationship to find meaningful results, such as subgraph pattern match and shortest-path query. The other one focuses on semantic-based query to find question answering from knowledge bases. However, most of these queries take knowledge graphs as flat forms and use only normal relationship to mine these graphs, which may lead to mistakes in the query results. In this thesis, we find hierarchical relationship in the knowledge on their semantic relations and make use of hierarchical relationship to query on knowledge graphs; and then we propose a meaningful query and its corresponding efficient query algorithm to get top-k answers on hierarchical knowledge graphs. We also design algorithms on distributed frameworks, which can improve its performance. To demonstrate the effectiveness and the efficiency of our algorithms, we use CISCO related products information that we crawled from official websites to do experiments on distributed frameworks
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Co-saliency detection aims to discover the common and salient foregrounds
from a group of relevant images. For this task, we present a novel adaptive
graph convolutional network with attention graph clustering (GCAGC). Three
major contributions have been made, and are experimentally shown to have
substantial practical merits. First, we propose a graph convolutional network
design to extract information cues to characterize the intra- and interimage
correspondence. Second, we develop an attention graph clustering algorithm to
discriminate the common objects from all the salient foreground objects in an
unsupervised fashion. Third, we present a unified framework with
encoder-decoder structure to jointly train and optimize the graph convolutional
network, attention graph cluster, and co-saliency detection decoder in an
end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency
detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method
obtains significant improvements over the state-of-the-arts on most of them.Comment: CVPR202
Invariant Feature Learning for Generalized Long-Tailed Classification
Existing long-tailed classification (LT) methods only focus on tackling the
class-wise imbalance that head classes have more samples than tail classes, but
overlook the attribute-wise imbalance. In fact, even if the class is balanced,
samples within each class may still be long-tailed due to the varying
attributes. Note that the latter is fundamentally more ubiquitous and
challenging than the former because attributes are not just implicit for most
datasets, but also combinatorially complex, thus prohibitively expensive to be
balanced. Therefore, we introduce a novel research problem: Generalized
Long-Tailed classification (GLT), to jointly consider both kinds of imbalances.
By "generalized", we mean that a GLT method should naturally solve the
traditional LT, but not vice versa. Not surprisingly, we find that most
class-wise LT methods degenerate in our proposed two benchmarks: ImageNet-GLT
and MSCOCO-GLT. We argue that it is because they over-emphasize the adjustment
of class distribution while neglecting to learn attribute-invariant features.
To this end, we propose an Invariant Feature Learning (IFL) method as the first
strong baseline for GLT. IFL first discovers environments with divergent
intra-class distributions from the imperfect predictions and then learns
invariant features across them. Promisingly, as an improved feature backbone,
IFL boosts all the LT line-up: one/two-stage re-balance, augmentation, and
ensemble. Codes and benchmarks are available on Github:
https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorchComment: Accepted to ECCV 2022. Codes and benchmarks are available on Github:
https://github.com/KaihuaTang/Generalized-Long-Tailed-Benchmarks.pytorc
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