arXiv ID
string | arXiv URL
string | PDF URL
string | DOI
string | Publication Date
timestamp[us] | Updated Date
string | Title
string | Authors
string | Author Affiliations
string | Abstract
string | Categories
string | Primary Category
string | Comment
string | Journal Reference
string | label
int64 | source
string | classification_embedding
list | proximity_embedding
list |
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2301.00395v1
|
http://arxiv.org/abs/2301.00395v1
|
http://arxiv.org/pdf/2301.00395v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
|
Ge Zhang; Yizhi Li; Yaoyao Wu; Linyuan Zhang; Chenghua Lin; Jiayi Geng; Shi Wang; Jie Fu
| null |
As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.
|
cs.CL; cs.AI; cs.CY; cs.LG
|
cs.CL
| null | null | 0
|
Random
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] |
2301.00436v3
|
http://arxiv.org/abs/2301.00436v3
|
http://arxiv.org/pdf/2301.00436v3
| null | 2023-01-01T00:00:00
|
2023-04-03
|
Hierarchical Explanations for Video Action Recognition
|
Sadaf Gulshad; Teng Long; Nanne van Noord
| null |
To interpret deep neural networks, one main approach is to dissect the visual input and find the prototypical parts responsible for the classification. However, existing methods often ignore the hierarchical relationship between these prototypes, and thus can not explain semantic concepts at both higher level (e.g., water sports) and lower level (e.g., swimming). In this paper inspired by human cognition system, we leverage hierarchal information to deal with uncertainty: When we observe water and human activity, but no definitive action it can be recognized as the water sports parent class. Only after observing a person swimming can we definitively refine it to the swimming action. To this end, we propose HIerarchical Prototype Explainer (HIPE) to build hierarchical relations between prototypes and classes. HIPE enables a reasoning process for video action classification by dissecting the input video frames on multiple levels of the class hierarchy, our method is also applicable to other video tasks. The faithfulness of our method is verified by reducing accuracy-explainability trade off on ActivityNet and UCF-101 while providing multi-level explanations.
|
cs.CV; cs.AI; cs.LG
|
cs.CV
| null | null | 0
|
Random
|
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] |
2301.00433v1
|
http://arxiv.org/abs/2301.00433v1
|
http://arxiv.org/pdf/2301.00433v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
Optimization of Image Transmission in a Cooperative Semantic Communication Networks
|
Wenjing Zhang; Yining Wang; Mingzhe Chen; Tao Luo; Dusit Niyato
| null |
In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem aiming to minimize each server's transmission latency while reaching the ISS requirement. To solve this problem, a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) is proposed, which enables servers to coordinate for training and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL, the proposed RL improves the valuable action exploration of servers and the probability of finding a globally optimal RB allocation policy based on local observation. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% compared to traditional multi-agent RL.
|
cs.AI; cs.CV; cs.IT; math.IT
|
cs.AI
|
29 pages, 10 figures
| null | 0
|
Random
|
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] |
2301.00406v4
|
http://arxiv.org/abs/2301.00406v4
|
http://arxiv.org/pdf/2301.00406v4
| null | 2023-01-01T00:00:00
|
2024-03-06
|
Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data
|
Rui Ding; Juntian Ye; Qifeng Gao; Feihu Xu; Yuping Duan
| null |
Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly possibility to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.
|
cs.CV; eess.IV
|
cs.CV
| null | null | 0
|
Random
|
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] |
2301.00383v2
|
http://arxiv.org/abs/2301.00383v2
|
http://arxiv.org/pdf/2301.00383v2
|
10.1109/TIP.2023.3235583
| 2023-01-01T00:00:00
|
2023-02-13
|
Discriminative Radial Domain Adaptation
|
Zenan Huang; Jun Wen; Siheng Chen; Linchao Zhu; Nenggan Zheng
| null |
Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDA) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.
|
cs.LG; cs.CV
|
cs.LG
|
13 pages, 14 figures
| null | 0
|
Random
|
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] |
2301.00345v1
|
http://arxiv.org/abs/2301.00345v1
|
http://arxiv.org/pdf/2301.00345v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
|
Jorge Quesada; Lakshmi Sathidevi; Ran Liu; Nauman Ahad; Joy M. Jackson; Mehdi Azabou; Jingyun Xiao; Christopher Liding; Matthew Jin; Carolina Urzay; William Gray-Roncal; Erik C. Johnson; Eva L. Dyer
| null |
There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .
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cs.CV; cs.LG
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cs.CV
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10 pages, 4 figures, Accepted at NeurIPS 2022
| null | 1
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Accepted
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] |
2301.00429v1
|
http://arxiv.org/abs/2301.00429v1
|
http://arxiv.org/pdf/2301.00429v1
| null | 2023-01-01T00:00:00
|
2023-01-01
| "Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machi(...TRUNCATED)
|
Hang Thi-Thu Le; Viet-Duc Ho; Duc-Vu Nguyen; Ngan Luu-Thuy Nguyen
| null | "Machine Reading Comprehension has become one of the most advanced and popular research topics in th(...TRUNCATED)
|
cs.CL
|
cs.CL
|
In Proceedings of the 9th NAFOSTED Conference on Information and Computer Science (NICS 2022)
| null | 0
|
Random
| [-0.741712212562561,0.616409957408905,0.7550923228263855,-1.3214318752288818,0.7090908885002136,-0.4(...TRUNCATED)
| [0.11741513758897781,1.011696696281433,-0.17807281017303467,-0.4733656644821167,-0.2507184147834778,(...TRUNCATED)
|
2301.01143v1
|
http://arxiv.org/abs/2301.01143v1
|
http://arxiv.org/pdf/2301.01143v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning
|
Fengbei Liu; Yuanhong Chen; Chong Wang; Yu Tain; Gustavo Carneiro
| null | "Learning with noisy-labels has become an important research topic in computer vision where state-of(...TRUNCATED)
|
cs.CV
|
cs.CV
| null | null | 0
|
Random
| [-0.6067773103713989,-0.6639298796653748,-0.007478766143321991,-0.3101578652858734,-0.55225896835327(...TRUNCATED)
| [0.17487922310829163,0.5665837526321411,-0.4335457384586334,-0.26730039715766907,-0.7572511434555054(...TRUNCATED)
|
2301.00346v1
|
http://arxiv.org/abs/2301.00346v1
|
http://arxiv.org/pdf/2301.00346v1
| null | 2023-01-01T00:00:00
|
2023-01-01
|
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
|
Thanh Vinh Vo; Arnab Bhattacharyya; Young Lee; Tze-Yun Leong
| null | "We propose a new causal inference framework to learn causal effects from multiple, decentralized da(...TRUNCATED)
|
cs.LG; cs.AI; stat.ME; stat.ML
|
cs.LG
|
NeurIPS 2022
| null | 1
|
Accepted
| [-1.5719794034957886,-0.6084272861480713,-1.0499109029769897,-0.8661954402923584,-1.476027488708496,(...TRUNCATED)
| [0.30771827697753906,0.28745752573013306,-0.8301564455032349,0.1310075968503952,-0.7442911267280579,(...TRUNCATED)
|
2301.00442v1
|
http://arxiv.org/abs/2301.00442v1
|
http://arxiv.org/pdf/2301.00442v1
|
10.1145/3407023.3407026
| 2023-01-01T00:00:00
|
2023-01-01
|
An Overview of Limitations and Approaches in Identity Management
|
Daniela Pöhn; Wolfgang Hommel
| null | "Identity and access management (I&AM) is the umbrella term for managing users and their permissions(...TRUNCATED)
|
cs.CR; cs.SE; cs.SY; eess.SY
|
cs.CR
| "The 15th International Conference on Availability, Reliability and Security (ARES 2020), August 2(...TRUNCATED)
| null | 0
|
Random
| [-1.5291080474853516,-0.13225285708904266,-0.7900806665420532,-1.0844886302947998,0.6214520335197449(...TRUNCATED)
| [-0.15094700455665588,0.46172070503234863,-0.779064953327179,0.46088409423828125,0.04571940377354622(...TRUNCATED)
|
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