27 research outputs found
Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting
The past few years have witnessed the rapid development in multivariate time
series forecasting. The key to accurate forecasting results is capturing the
long-term dependency between each time step (cross-time dependency) and
modeling the complex dependency between each variable (cross-variable
dependency) in multivariate time series. However, recent methods mainly focus
on the cross-time dependency but seldom consider the cross-variable dependency.
To fill this gap, we find that convolution, a traditional technique but
recently losing steam in time series forecasting, meets the needs of
respectively capturing the cross-time and cross-variable dependency. Based on
this finding, we propose a modern pure convolution structure, namely
Cross-LKTCN, to better utilize both cross-time and cross-variable dependency
for time series forecasting. Specifically in each Cross-LKTCN block, a
depth-wise large kernel convolution with large receptive field is proposed to
capture cross-time dependency, and then two successive point-wise group
convolution feed forward networks are proposed to capture cross-variable
dependency. Experimental results on real-world benchmarks show that Cross-LKTCN
achieves state-of-the-art forecasting performance and improves the forecasting
accuracy significantly compared with existing convolutional-based models and
cross-variable methods
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection
This paper presents a novel framework, named Global-Local Correspondence
Framework (GLCF), for visual anomaly detection with logical constraints. Visual
anomaly detection has become an active research area in various real-world
applications, such as industrial anomaly detection and medical disease
diagnosis. However, most existing methods focus on identifying local structural
degeneration anomalies and often fail to detect high-level functional anomalies
that involve logical constraints. To address this issue, we propose a
two-branch approach that consists of a local branch for detecting structural
anomalies and a global branch for detecting logical anomalies. To facilitate
local-global feature correspondence, we introduce a novel semantic bottleneck
enabled by the visual Transformer. Moreover, we develop feature estimation
networks for each branch separately to detect anomalies. Our proposed framework
is validated using various benchmarks, including industrial datasets, Mvtec AD,
Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show
that our method outperforms existing methods, particularly in detecting logical
anomalies.Comment: Submission to IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO
TECHNOLOG
A Unified Framework for 3D Point Cloud Visual Grounding
Thanks to its precise spatial referencing, 3D point cloud visual grounding is
essential for deep understanding and dynamic interaction in 3D environments,
encompassing 3D Referring Expression Comprehension (3DREC) and Segmentation
(3DRES). We argue that 3DREC and 3DRES should be unified in one framework,
which is also a natural progression in the community. To explain, 3DREC help
3DRES locate the referent, while 3DRES also facilitate 3DREC via more
fine-grained language-visual alignment. To achieve this, this paper takes the
initiative step to integrate 3DREC and 3DRES into a unified framework, termed
3D Referring Transformer (3DRefTR). Its key idea is to build upon a mature
3DREC model and leverage ready query embeddings and visual tokens from the
3DREC model to construct a dedicated mask branch. Specially, we propose
Superpoint Mask Branch, which serves a dual purpose: i) By harnessing on the
inherent association between the superpoints and point cloud, it eliminates the
heavy computational overhead on the high-resolution visual features for
upsampling; ii) By leveraging the heterogeneous CPU-GPU parallelism, while the
GPU is occupied generating visual and language tokens, the CPU concurrently
produces superpoints, equivalently accomplishing the upsampling computation.
This elaborate design enables 3DRefTR to achieve both well-performing 3DRES and
3DREC capacities with only a 6% additional latency compared to the original
3DREC model. Empirical evaluations affirm the superiority of 3DRefTR.
Specifically, on the ScanRefer dataset, 3DRefTR surpasses the state-of-the-art
3DRES method by 12.43% in mIoU and improves upon the SOTA 3DREC method by 0.6%
[email protected]. The codes and models will be released soon
TEINet: Towards an Efficient Architecture for Video Recognition
Efficiency is an important issue in designing video architectures for action
recognition. 3D CNNs have witnessed remarkable progress in action recognition
from videos. However, compared with their 2D counterparts, 3D convolutions
often introduce a large amount of parameters and cause high computational cost.
To relieve this problem, we propose an efficient temporal module, termed as
Temporal Enhancement-and-Interaction (TEI Module), which could be plugged into
the existing 2D CNNs (denoted by TEINet). The TEI module presents a different
paradigm to learn temporal features by decoupling the modeling of channel
correlation and temporal interaction. First, it contains a Motion Enhanced
Module (MEM) which is to enhance the motion-related features while suppress
irrelevant information (e.g., background). Then, it introduces a Temporal
Interaction Module (TIM) which supplements the temporal contextual information
in a channel-wise manner. This two-stage modeling scheme is not only able to
capture temporal structure flexibly and effectively, but also efficient for
model inference. We conduct extensive experiments to verify the effectiveness
of TEINet on several benchmarks (e.g., Something-Something V1&V2, Kinetics,
UCF101 and HMDB51). Our proposed TEINet can achieve a good recognition accuracy
on these datasets but still preserve a high efficiency.Comment: Accepted by AAAI 202
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Unsupervised learning of optical flow, which leverages the supervision from
view synthesis, has emerged as a promising alternative to supervised methods.
However, the objective of unsupervised learning is likely to be unreliable in
challenging scenes. In this work, we present a framework to use more reliable
supervision from transformations. It simply twists the general unsupervised
learning pipeline by running another forward pass with transformed data from
augmentation, along with using transformed predictions of original data as the
self-supervision signal. Besides, we further introduce a lightweight network
with multiple frames by a highly-shared flow decoder. Our method consistently
gets a leap of performance on several benchmarks with the best accuracy among
deep unsupervised methods. Also, our method achieves competitive results to
recent fully supervised methods while with much fewer parameters.Comment: Accepted to CVPR 2020, https://github.com/lliuz/ARFlo
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
Generating temporal action proposals remains a very challenging problem,
where the main issue lies in predicting precise temporal proposal boundaries
and reliable action confidence in long and untrimmed real-world videos. In this
paper, we propose an efficient and unified framework to generate temporal
action proposals named Dense Boundary Generator (DBG), which draws inspiration
from boundary-sensitive methods and implements boundary classification and
action completeness regression for densely distributed proposals. In
particular, the DBG consists of two modules: Temporal boundary classification
(TBC) and Action-aware completeness regression (ACR). The TBC aims to provide
two temporal boundary confidence maps by low-level two-stream features, while
the ACR is designed to generate an action completeness score map by high-level
action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to
encode RGB and optical flow information, which helps to capture discriminative
boundary and actionness features. Extensive experiments on popular benchmarks
ActivityNet-1.3 and THUMOS14 demonstrate the superiority of DBG over the
state-of-the-art proposal generator (e.g., MGG and BMN). Our code will be made
available upon publication.Comment: Accepted by AAAI 2020. Ranked No. 1 on ActivityNet Challenge 2019 on
Temporal Action Proposals
(http://activity-net.org/challenges/2019/evaluation.html
Neuroendocrine pathways and breast cancer progression : a pooled analysis of somatic mutations and gene expression from two large breast cancer cohorts
Funding Information: Open access funding provided by Karolinska Institute. This work was supported by grants awarded to KH by the China Scholarship Council (No. 201806240005); to FF by the Swedish Cancer Society (20 0846 PjF); to DL by the National Natural Science Foundation of China (No. 8187111500) and the Swedish Research Council (2018–00648). The funding bodies did not play any role in the design of the study and collection, analysis, or interpretation of data or in writing the manuscript. Funding Information: We thank the West China Biobank, Department of Clinical Research Management, West China Hospital, Sichuan University for the bio-sample storage. We thank Dr. Jianming Zeng (University of Macau) and his team biotrainee for generously sharing their experiences and codes. The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This work was presented as an e-Poster (215P) in ESMO Congress 2021, 16-21 September 2021. Publisher Copyright: © 2022, The Author(s).Background: Experimental studies indicate that neuroendocrine pathways might play a role in progression of breast cancer. We aim to test the hypothesis that somatic mutations in the genes of neuroendocrine pathways influence breast cancer prognosis, through dysregulated gene expression in tumor tissue. Methods: We conducted an extreme case–control study including 208 breast cancer patients with poor invasive disease-free survival (iDFS) and 208 patients with favorable iDFS who were individually matched on molecular subtype from the Breast Cancer Cohort at West China Hospital (WCH; N = 192) and The Cancer Genome Atlas (TCGA; N = 224). Whole exome sequencing and RNA sequencing of tumor and paired normal breast tissues were performed. Adrenergic, glucocorticoid, dopaminergic, serotonergic, and cholinergic pathways were assessed for differences in mutation burden and gene expression in relation to breast cancer iDFS using the logistic regression and global test, respectively. Results: In the pooled analysis, presence of any somatic mutation (odds ratio = 1.66, 95% CI: 1.07–2.58) of the glucocorticoid pathway was associated with poor iDFS and a two-fold increase of tumor mutation burden was associated with 17% elevated odds (95% CI: 2–35%), after adjustment for cohort membership, age, menopausal status, molecular subtype, and tumor stage. Differential expression of genes in the glucocorticoid pathway in tumor tissue (P = 0.028), but not normal tissue (P = 0.701), was associated with poor iDFS. Somatic mutation of the adrenergic and cholinergic pathways was significantly associated with iDFS in WCH, but not in TCGA. Conclusion: Glucocorticoid pathway may play a role in breast cancer prognosis through differential mutations and expression. Further characterization of its functional role may open new avenues for the development of novel therapeutic targets for breast cancer.Peer reviewe
Public health insurance and cancer‐specific mortality risk among patients with breast cancer: A prospective cohort study in China
We thank all staff members working on the Breast Cancer Information Management System (BCIMS) for their contributions to data collection and management. We also thank Dr Bo Fu, Mr Yan Li and Mr Pei Liu at the University of Electronic Science and Technology of China for data cleaning and zip code mapping. Our study was supported by the Key Research and Development Project of Sichuan Province of China (grant number: 2017SZ00005) and Swedish Research Council (grant number: 2018‐00648).Little is known about how health insurance policies, particularly in developing countries, influence breast cancer prognosis. Here, we examined the association between individual health insurance and breast cancer-specific mortality in China. We included 7436 women diagnosed with invasive breast cancer between 2009 and 2016, at West China Hospital, Sichuan University. The health insurance plan of patient was classified as either urban or rural schemes and was also categorized as reimbursement rate (ie, the covered/total charge) below or above the median. Breast cancer-specific mortality was the primary outcome. Using Cox proportional hazards models, we calculated hazard ratios (HRs) for cancer-specific mortality, contrasting rates among patients with a rural insurance scheme or low reimbursement rate to that of those with an urban insurance scheme or high reimbursement rate, respectively. During a median follow-up of 3.1 years, we identified 326 deaths due to breast cancer. Compared to patients covered by urban insurance schemes, patients covered by rural insurance schemes had a 29% increased cancer-specific mortality (95% CI 0%-65%) after adjusting for demographics, tumor characteristics and treatment modes. Reimbursement rate below the median was associated with a 42% increased rate of cancer-specific mortality (95% CI 11%-82%). Every 10% increase in the reimbursement rate is associated with a 7% (95% CI 2%-12%) reduction in cancer-specific mortality risk, particularly in patients covered by rural insurance schemes (26%, 95% CI 9%-39%). Our findings suggest that underinsured patients face a higher risk of breast cancer-specific mortality in developing countries.VetenskapsrådetPeer Reviewe