Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval DOI
Kelong Mao, Zhicheng Dou, Hongjin Qian

et al.

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Journal Year: 2022, Volume and Issue: unknown, P. 176 - 186

Published: July 6, 2022

Conversational search is a crucial and promising branch in information retrieval. In this paper, we reveal that not all historical conversational turns are necessary for understanding the intent of current query. The redundant noisy context largely hinder improvement performance. However, enhancing denoising ability quite challenging due to data scarcity steep difficulty simultaneously learning query encoding denoising. To address these issues, present novel Curriculum cOntrastive conTExt Denoising framework, COTED, towards few-shot dense Under curriculum training order, progressively endow model with capability via contrastive between noised samples denoised generated by new conversation augmentation strategy. Three curriculums tailored exploited our framework. Extensive experiments on two datasets, i.e., CAsT-19 CAsT-20, validate effectiveness superiority method compared state-of-the-art baselines.

Language: Английский

Artificial intelligence in the creative industries: a review DOI Creative Commons
Nantheera Anantrasirichai, David Bull

Artificial Intelligence Review, Journal Year: 2021, Volume and Issue: 55(1), P. 589 - 656

Published: July 2, 2021

This paper reviews the current state of art in Artificial Intelligence (AI) technologies and applications context creative industries. A brief background AI, specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent (RNNs) Deep Reinforcement (DRL). We categorise into five groups related to how AI are used: i) content creation, ii) information analysis, iii) enhancement post production workflows, iv) extraction enhancement, v) data compression. critically examine successes limitations this rapidly advancing technology each these areas. further differentiate between use as a tool its potential creator own right. foresee that, near future, machine learning-based will be adopted widely or collaborative assistant for creativity. In contrast, we observe that learning domains with fewer constraints, where `creator', remain modest. The (or developers) win awards original creations competition human creatives also limited, based on contemporary technologies. therefore conclude industries, maximum benefit from derived focus centric -- it designed augment, rather than replace,

Language: Английский

Citations

427
Human-in-the-loop machine learning: a state of the art DOI Creative Commons
Eduardo Mosqueira-Rey, Elena Hernández-Pereira, David Alonso-Ríos

et al.

Artificial Intelligence Review, Journal Year: 2022, Volume and Issue: 56(4), P. 3005 - 3054

Published: Aug. 17, 2022

Abstract Researchers are defining new types of interactions between humans and machine learning algorithms generically called human-in-the-loop learning. Depending on who is in control the process, we can identify: active learning, which system remains control; interactive there a closer interaction users systems; teaching, where human domain experts have over process. Aside from control, also be involved process other ways. In curriculum try to impose some structure examples presented improve learning; explainable AI focus ability model explain why given solution was chosen. This collaboration models should not limited only process; if go further, see terms that arise such as Usable Useful AI. this paper review state art techniques forms relationship ML algorithms. Our contribution merely listing different approaches, but provide definitions clarifying confusing, varied sometimes contradictory terms; elucidate determine boundaries methods; correlate all searching for connections influences them.

Language: Английский

Citations

369
Curriculum Learning: A Survey DOI

Petru Soviany,

Radu Tudor Ionescu, Paolo Rota

et al.

International Journal of Computer Vision, Journal Year: 2022, Volume and Issue: 130(6), P. 1526 - 1565

Published: April 19, 2022

Language: Английский

Citations

227
Advances in medical image analysis with vision Transformers: A comprehensive review DOI
Reza Azad, Amirhossein Kazerouni, Moein Heidari

et al.

Medical Image Analysis, Journal Year: 2023, Volume and Issue: 91, P. 103000 - 103000

Published: Oct. 19, 2023

Language: Английский

Citations

138
Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges DOI Creative Commons
Zhaolin Chen, Kamlesh Pawar,

Mevan Ekanayake

et al.

Journal of Digital Imaging, Journal Year: 2022, Volume and Issue: 36(1), P. 204 - 230

Published: Nov. 2, 2022

Abstract Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine biology. The post-processing of reconstructed MR images is often automated incorporation into MRI scanners by the manufacturers increasingly plays a critical role final image quality reporting interpretation. For enhancement correction, steps include noise reduction, artefact resolution improvements. With success deep learning fields, there great potential to apply enhancement, publications have demonstrated promising results. Motivated rapidly growing literature this area, review paper, we provide comprehensive overview learning-based methods enhance correct artefacts. We aim researchers or other including computer vision processing, survey approaches enhancement. discuss current limitations application artificial intelligence highlight possible directions future developments. In era learning, importance appraisal explanatory information provided generalizability algorithms medical imaging.

Language: Английский

Citations

92
MotionBERT: A Unified Perspective on Learning Human Motion Representations DOI
Wentao Zhu, Xiaoxuan Ma, Zhaoyang Liu

et al.

2021 IEEE/CVF International Conference on Computer Vision (ICCV), Journal Year: 2023, Volume and Issue: unknown, P. 15039 - 15053

Published: Oct. 1, 2023

We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose pretraining stage in which encoder is trained to recover the underlying 3D noisy partial 2D observations. The acquired this way incorporate geometric, kinematic, physical knowledge about motion, can be easily transferred multiple downstream tasks. implement with Dual-stream Spatio-temporal Transformer (DSTformer) neural network. It could capture long-range spatio-temporal relationships among skeletal joints comprehensively adaptively, exemplified lowest pose estimation error so far when scratch. Furthermore, our proposed framework achieves state-of-the-art performance all three simply finetuning pretrained simple regression head (1-2 layers), demonstrates versatility of learned representations. Code models are available at https://motionbert.github.io/

Language: Английский

Citations

92
Curriculum Temperature for Knowledge Distillation DOI Open Access
Zheng Li, Xiang Li, Lingfeng Yang

et al.

Proceedings of the AAAI Conference on Artificial Intelligence, Journal Year: 2023, Volume and Issue: 37(2), P. 1504 - 1512

Published: June 26, 2023

Most existing distillation methods ignore the flexible role of temperature in loss function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In general, controls discrepancy between two distributions faithfully determine difficulty level task. Keeping constant temperature, i.e., fixed task difficulty, is usually sub-optimal for growing student during its progressive learning stages. this paper, we propose simple curriculum-based technique, termed Curriculum Temperature Knowledge Distillation (CTKD), which student's career through dynamic learnable temperature. Specifically, following easy-to-hard curriculum, gradually increase w.r.t. leading to increased adversarial manner. As easy-to-use plug-in CTKD seamlessly integrated into knowledge frameworks brings general improvements at negligible additional computation cost. Extensive experiments on CIFAR-100, ImageNet-2012, MS-COCO demonstrate effectiveness our method.

Language: Английский

Citations

74
DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT DOI

Qinghai Miao,

Wenbo Zheng, Yisheng Lv

et al.

IEEE/CAA Journal of Automatica Sinica, Journal Year: 2023, Volume and Issue: 10(4), P. 877 - 897

Published: March 28, 2023

From AlphaGo to ChatGPT, the field of AI has launched a series remarkable achievements in recent years. Analyzing, comparing, and summarizing these at paradigm level is important for future innovation, but not received sufficient attention. In this paper, we give an overview perspective on machine learning paradigms. First, propose taxonomy with three levels seven dimensions from knowledge perspective. Accordingly, basic twelve extended paradigms, such as Ensemble Learning, Transfer etc., figures unified style. We further analyze advanced i.e., AlphaGo, AlphaFold ChatGPT. Second, enable more efficient effective scientific discovery, build new ecosystem that drives shifts through decentralized science (DeSci) movement based autonomous organization (DAO). To end, design Hanoi framework, which integrates human factors, parallel intelligence combination artificial systems natural world, DAO inspire innovations.

Language: Английский

Citations

55
Deep reinforcement learning-based air combat maneuver decision-making: literature review, implementation tutorial and future direction DOI
Xinwei Wang, Yihui Wang, Xichao Su

et al.

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 57(1)

Published: Dec. 28, 2023

Language: Английский

Citations

52
Graph Neural Network with curriculum learning for imbalanced node classification DOI
Xiaohe Li, Zide Fan,

Feilong Huang

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 574, P. 127229 - 127229

Published: Jan. 5, 2024

Language: Английский

Citations

24