2,309 research outputs found
Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches
We implemented three recently proposed approaches to the identification of
overlapping and hierarchical substructures in graphs and applied the
corresponding algorithms to a network of 492 information-science papers coupled
via their cited sources. The thematic substructures obtained and overlaps
produced by the three hierarchical cluster algorithms were compared to a
content-based categorisation, which we based on the interpretation of titles
and keywords. We defined sets of papers dealing with three topics located on
different levels of aggregation: h-index, webometrics, and bibliometrics. We
identified these topics with branches in the dendrograms produced by the three
cluster algorithms and compared the overlapping topics they detected with one
another and with the three pre-defined paper sets. We discuss the advantages
and drawbacks of applying the three approaches to paper networks in research
fields.Comment: 18 pages, 9 figure
Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID
Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to
match pedestrian images of the same identity from different modalities without
annotations. Existing works mainly focus on alleviating the modality gap by
aligning instance-level features of the unlabeled samples. However, the
relationships between cross-modality clusters are not well explored. To this
end, we propose a novel bilateral cluster matching-based learning framework to
reduce the modality gap by matching cross-modality clusters. Specifically, we
design a Many-to-many Bilateral Cross-Modality Cluster Matching (MBCCM)
algorithm through optimizing the maximum matching problem in a bipartite graph.
Then, the matched pairwise clusters utilize shared visible and infrared
pseudo-labels during the model training. Under such a supervisory signal, a
Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework
is proposed to align features jointly at a cluster-level. Meanwhile, the
cross-modality Consistency Constraint (CC) is proposed to explicitly reduce the
large modality discrepancy. Extensive experiments on the public SYSU-MM01 and
RegDB datasets demonstrate the effectiveness of the proposed method, surpassing
state-of-the-art approaches by a large margin of 8.76% mAP on average
Entity Ranking on Graphs: Studies on Expert Finding
Todays web search engines try to offer services for finding various information in addition to simple web pages, like showing locations or answering simple fact queries. Understanding the association of named entities and documents is one of the key steps towards such semantic search tasks. This paper addresses the ranking of entities and models it in a graph-based relevance propagation framework. In particular we study the problem of expert finding as an example of an entity ranking task. Entity containment graphs are introduced that represent the relationship between text fragments on the one hand and their contained entities on the other hand. The paper shows how these graphs can be used to propagate relevance information from the pre-ranked text fragments to their entities. We use this propagation framework to model existing approaches to expert finding based on the entity's indegree and extend them by recursive relevance propagation based on a probabilistic random walk over the entity containment graphs. Experiments on the TREC expert search task compare the retrieval performance of the different graph and propagation models
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
Detection transformers have recently shown promising object detection results
and attracted increasing attention. However, how to develop effective domain
adaptation techniques to improve its cross-domain performance remains
unexplored and unclear. In this paper, we delve into this topic and empirically
find that direct feature distribution alignment on the CNN backbone only brings
limited improvements, as it does not guarantee domain-invariant sequence
features in the transformer for prediction. To address this issue, we propose a
novel Sequence Feature Alignment (SFA) method that is specially designed for
the adaptation of detection transformers. Technically, SFA consists of a domain
query-based feature alignment (DQFA) module and a token-wise feature alignment
(TDA) module. In DQFA, a novel domain query is used to aggregate and align
global context from the token sequence of both domains. DQFA reduces the domain
discrepancy in global feature representations and object relations when
deploying in the transformer encoder and decoder, respectively. Meanwhile, TDA
aligns token features in the sequence from both domains, which reduces the
domain gaps in local and instance-level feature representations in the
transformer encoder and decoder, respectively. Besides, a novel bipartite
matching consistency loss is proposed to enhance the feature discriminability
for robust object detection. Experiments on three challenging benchmarks show
that SFA outperforms state-of-the-art domain adaptive object detection methods.
Code has been made available at: https://github.com/encounter1997/SFA.Comment: Fix a typo in Eq. 1
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