148 research outputs found
Density-Based Region Search with Arbitrary Shape for Object Localization
Region search is widely used for object localization. Typically, the region
search methods project the score of a classifier into an image plane, and then
search the region with the maximal score. The recently proposed region search
methods, such as efficient subwindow search and efficient region search, %which
localize objects from the score distribution on an image are much more
efficient than sliding window search. However, for some classifiers and tasks,
the projected scores are nearly all positive, and hence maximizing the score of
a region results in localizing nearly the entire images as objects, which is
meaningless.
In this paper, we observe that the large scores are mainly concentrated on or
around objects. Based on this observation, we propose a method, named level set
maximum-weight connected subgraph (LS-MWCS), which localizes objects with
arbitrary shapes by searching regions with the densest score rather than the
maximal score. The region density can be controlled by a parameter flexibly.
And we prove an important property of the proposed LS-MWCS, which guarantees
that the region with the densest score can be searched. Moreover, the LS-MWCS
can be efficiently optimized by belief propagation. The method is evaluated on
the problem of weakly-supervised object localization, and the quantitative
results demonstrate the superiorities of our LS-MWCS compared to other
state-of-the-art methods
Significant cross-species gene flow detected in the Tamias quadrivittatus group of North American chipmunks
In the past two decades genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used heuristic methods that analyze summaries of the multilocus sequence data to detect gene flow, which may suffer from low power. Here we use the full likelihood method implemented in the Bayesian program BPP to reanalyze these data. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 65%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. Our analyses highlight the importance of using adequate statistical methods to reach reliable biological conclusions concerning cross-species gene flow
Local Averaging Helps: Hierarchical Federated Learning and Convergence Analysis
Federated learning is an effective approach to realize collaborative learning
among edge devices without exchanging raw data. In practice, these devices may
connect to local hubs instead of connecting to the global server (aggregator)
directly. Due to the (possibly limited) computation capability of these local
hubs, it is reasonable to assume that they can perform simple averaging
operations. A natural question is whether such local averaging is beneficial
under different system parameters and how much gain can be obtained compared to
the case without such averaging. In this paper, we study hierarchical federated
learning with stochastic gradient descent (HF-SGD) and conduct a thorough
theoretical analysis to analyze its convergence behavior. In particular, we
first consider the two-level HF-SGD (one level of local averaging) and then
extend this result to arbitrary number of levels (multiple levels of local
averaging). The analysis demonstrates the impact of local averaging precisely
as a function of system parameters. Due to the higher communication cost of
global averaging, a strategy of decreasing the global averaging frequency and
increasing the local averaging frequency is proposed. Experiments validate the
proposed theoretical analysis and the advantages of HF-SGD.Comment: 42 pages, 13 figure
Semi-Supervised Panoptic Narrative Grounding
Despite considerable progress, the advancement of Panoptic Narrative
Grounding (PNG) remains hindered by costly annotations. In this paper, we
introduce a novel Semi-Supervised Panoptic Narrative Grounding (SS-PNG)
learning scheme, capitalizing on a smaller set of labeled image-text pairs and
a larger set of unlabeled pairs to achieve competitive performance. Unlike
visual segmentation tasks, PNG involves one pixel belonging to multiple
open-ended nouns. As a result, existing multi-class based semi-supervised
segmentation frameworks cannot be directly applied to this task. To address
this challenge, we first develop a novel SS-PNG Network (SS-PNG-NW) tailored to
the SS-PNG setting. We thoroughly investigate strategies such as Burn-In and
data augmentation to determine the optimal generic configuration for the
SS-PNG-NW. Additionally, to tackle the issue of imbalanced pseudo-label
quality, we propose a Quality-Based Loss Adjustment (QLA) approach to adjust
the semi-supervised objective, resulting in an enhanced SS-PNG-NW+. Employing
our proposed QLA, we improve BCE Loss and Dice loss at pixel and mask levels,
respectively. We conduct extensive experiments on PNG datasets, with our
SS-PNG-NW+ demonstrating promising results comparable to fully-supervised
models across all data ratios. Remarkably, our SS-PNG-NW+ outperforms
fully-supervised models with only 30% and 50% supervision data, exceeding their
performance by 0.8% and 1.1% respectively. This highlights the effectiveness of
our proposed SS-PNG-NW+ in overcoming the challenges posed by limited
annotations and enhancing the applicability of PNG tasks. The source code is
available at https://github.com/nini0919/SSPNG.Comment: ACM MM 202
NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative Learning
Panoptic Narrative Detection (PND) and Segmentation (PNS) are two challenging
tasks that involve identifying and locating multiple targets in an image
according to a long narrative description. In this paper, we propose a unified
and effective framework called NICE that can jointly learn these two panoptic
narrative recognition tasks. Existing visual grounding tasks use a two-branch
paradigm, but applying this directly to PND and PNS can result in prediction
conflict due to their intrinsic many-to-many alignment property. To address
this, we introduce two cascading modules based on the barycenter of the mask,
which are Coordinate Guided Aggregation (CGA) and Barycenter Driven
Localization (BDL), responsible for segmentation and detection, respectively.
By linking PNS and PND in series with the barycenter of segmentation as the
anchor, our approach naturally aligns the two tasks and allows them to
complement each other for improved performance. Specifically, CGA provides the
barycenter as a reference for detection, reducing BDL's reliance on a large
number of candidate boxes. BDL leverages its excellent properties to
distinguish different instances, which improves the performance of CGA for
segmentation. Extensive experiments demonstrate that NICE surpasses all
existing methods by a large margin, achieving 4.1% for PND and 2.9% for PNS
over the state-of-the-art. These results validate the effectiveness of our
proposed collaborative learning strategy. The project of this work is made
publicly available at https://github.com/Mr-Neko/NICE.Comment: 18 pages. 9 figures, 9 table
M3PS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization in E-commerce
Given the long textual product information and the product image, Multi-Modal
Product Summarization (MMPS) aims to attract customers' interest and increase
their desire to purchase by highlighting product characteristics with a short
textual summary. Existing MMPS methods have achieved promising performance.
Nevertheless, there still exist several problems: 1) lack end-to-end product
summarization, 2) lack multi-grained multi-modal modeling, and 3) lack
multi-modal attribute modeling. To address these issues, we propose an
end-to-end multi-grained multi-modal attribute-aware product summarization
method (M3PS) for generating high-quality product summaries in e-commerce. M3PS
jointly models product attributes and generates product summaries. Meanwhile,
we design several multi-grained multi-modal tasks to better guide the
multi-modal learning of M3PS. Furthermore, we model product attributes based on
both text and image modalities so that multi-modal product characteristics can
be manifested in the generated summaries. Extensive experiments on a real
large-scale Chinese e-commence dataset demonstrate that our model outperforms
state-of-the-art product summarization methods w.r.t. several summarization
metrics
Power of Bayesian and heuristic tests to detect cross-species introgression with reference to gene flow in the Tamias quadrivittatus group of North American chipmunks
In the past two decades genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HyDe to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program bpp to reanalyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HyDe, and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data
Generating Persuasive Responses to Customer Reviews with Multi-Source Prior Knowledge in E-commerce
Customer reviews usually contain much information about one's online shopping
experience. While positive reviews are beneficial to the stores, negative ones
will largely influence consumers' decision and may lead to a decline in sales.
Therefore, it is of vital importance to carefully and persuasively reply to
each negative review and minimize its disadvantageous effect. Recent studies
consider leveraging generation models to help the sellers respond. However,
this problem is not well-addressed as the reviews may contain multiple aspects
of issues which should be resolved accordingly and persuasively. In this work,
we propose a Multi-Source Multi-Aspect Attentive Generation model for
persuasive response generation. Various sources of information are
appropriately obtained and leveraged by the proposed model for generating more
informative and persuasive responses. A multi-aspect attentive network is
proposed to automatically attend to different aspects in a review and ensure
most of the issues are tackled. Extensive experiments on two real-world
datasets, demonstrate that our approach outperforms the state-of-the-art
methods and online tests prove that our deployed system significantly enhances
the efficiency of the stores' dealing with negative reviews.Comment: Accepted at CIKM 2022 applied researc
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