no code implementations • EMNLP (NLP+CSS) 2020 • Reyha Verma, Christian von der Weth, Jithin Vachery, Mohan Kankanhalli
Identifying the worries of individuals and societies plays a crucial role in providing social support and enhancing policy decision-making.
no code implementations • ICML 2020 • Andrey Sakryukin, Chedy Raissi, Mohan Kankanhalli
We propose a novel approach to infer the network structure for DQN models operating with high-dimensional continuous actions.
no code implementations • 10 Mar 2022 • Fan Liu, Zhiyong Cheng, Huilin Chen, AnAn Liu, Liqiang Nie, Mohan Kankanhalli
In particular, we adopt a disentangled representation technique to ensure the features of different factors in each modality are independent to each other.
1 code implementation • 25 Feb 2022 • Zhenyang Li, Yangyang Guo, Kejie Wang, Yinwei Wei, Liqiang Nie, Mohan Kankanhalli
Given that our framework is model-agnostic, we apply it to the existing popular baselines and validate its effectiveness on the benchmark dataset.
no code implementations • 25 Feb 2022 • Yangyang Guo, Liqiang Nie, Harry Cheng, Zhiyong Cheng, Mohan Kankanhalli, Alberto del Bimbo
From the results on four datasets regarding the above three tasks, our method yields remarkable performance improvements compared with the baselines, demonstrating its superiority on reducing the modality bias problem.
no code implementations • 7 Feb 2022 • Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli
First, we theoretically show that an adversary can upper-bound the distributional shift which guarantees the attack's invisibility.
1 code implementation • 23 Jan 2022 • Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao
To explore these issues, we formulate a new semi-supervised continual learning method, which can be generically applied to existing continual learning models.
no code implementations • 14 Jan 2022 • Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mohan Kankanhalli
This necessitates deletion of data not only from storage archives but also from ML model.
no code implementations • 17 Nov 2021 • Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Mohan Kankanhalli
In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model.
1 code implementation • 21 Sep 2021 • Tao Zhuo, Qiang Huang, Mohan Kankanhalli
Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans.
1 code implementation • 1 Sep 2021 • Xudong Shen, Yongkang Wong, Mohan Kankanhalli
Motivated by scenarios where data is used for diverse prediction tasks, we study whether fair representation can be used to guarantee fairness for unknown tasks and for multiple fairness notions simultaneously.
1 code implementation • 10 Aug 2021 • Ziwei Xu, Guangzhi Wang, Yongkang Wong, Mohan Kankanhalli
The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images.
no code implementations • CVPR 2021 • Hehe Fan, Yi Yang, Mohan Kankanhalli
To capture the dynamics in point cloud videos, point tracking is usually employed.
no code implementations • 6 Feb 2021 • Jianing Zhu, Jingfeng Zhang, Bo Han, Tongliang Liu, Gang Niu, Hongxia Yang, Mohan Kankanhalli, Masashi Sugiyama
A recent adversarial training (AT) study showed that the number of projected gradient descent (PGD) steps to successfully attack a point (i. e., find an adversarial example in its proximity) is an effective measure of the robustness of this point.
no code implementations • ICLR 2021 • Tao Zhuo, Mohan Kankanhalli
Abstract reasoning is a challenging task in artificial intelligence.
no code implementations • ICLR 2021 • Hehe Fan, Xin Yu, Yuhang Ding, Yi Yang, Mohan Kankanhalli
Then, a spatial convolution is employed to capture the local structure of points in the 3D space, and a temporal convolution is used to model the dynamics of the spatial regions along the time dimension.
no code implementations • ICCV 2021 • Xiheng Zhang, Yongkang Wong, Xiaofei Wu, Juwei Lu, Mohan Kankanhalli, Xiangdong Li, Weidong Geng
In this work, we take a step towards training robust models for cross-domain pose estimation task, which brings together ideas from causal representation learning and generative adversarial networks.
no code implementations • 18 Dec 2020 • Yubao Sun, Ying Yang, Qingshan Liu, Mohan Kankanhalli
Hyperspectral compressive imaging takes advantage of compressive sensing theory to achieve coded aperture snapshot measurement without temporal scanning, and the entire three-dimensional spatial-spectral data is captured by a two-dimensional projection during a single integration period.
1 code implementation • ICLR 2021 • Jingfeng Zhang, Jianing Zhu, Gang Niu, Bo Han, Masashi Sugiyama, Mohan Kankanhalli
The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy.
no code implementations • 23 Sep 2020 • Konstantinos Nikolaidis, Thomas Plagemann, Stein Kristiansen, Vera Goebel, Mohan Kankanhalli
A new model is trained with these labels to generalize reliably despite the label noise.
no code implementations • 22 Sep 2020 • Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann, Vera Goebel, Knut Liestøl, Mohan Kankanhalli, Gunn Marit Traaen, Britt Øverland, Harriet Akre, Lars Aakerøy, Sigurd Steinshamn
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access to a classifier trained on the source data is provided.
1 code implementation • 21 Sep 2020 • Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann, Vera Goebel, Knut Liestøl, Mohan Kankanhalli, Gunn Marit Traaen, Britt Øverland, Harriet Akre, Lars Aakerøy, Sigurd Steinshamn
We use sleep monitoring data from both an open and a large closed clinical study and evaluate whether (1) end-users can create and successfully use customized classification models for sleep apnea detection, and (2) the identity of participants in the study is protected.
no code implementations • 15 Jun 2020 • Chen Chen, Jingfeng Zhang, Anthony K. H. Tung, Mohan Kankanhalli, Gang Chen
We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients.
no code implementations • 22 Apr 2020 • Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly, Mohan Kankanhalli
When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded.
1 code implementation • 1 Apr 2020 • Erik Quintanilla, Yogesh Rawat, Andrey Sakryukin, Mubarak Shah, Mohan Kankanhalli
We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100M and NUS-WIDE.
1 code implementation • 19 Mar 2020 • Gökhan Yildirim, Debashis Sen, Mohan Kankanhalli, Sabine Süsstrunk
In this paper, we corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance.
1 code implementation • ICML 2020 • Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, Mohan Kankanhalli
Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models.
no code implementations • 9 Feb 2020 • Junnan Li, Ziwei Xu, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task.
no code implementations • 9 Feb 2020 • Junnan Li, Jianquan Liu, Yongkang Wong, Shoji Nishimura, Mohan Kankanhalli
To enable research in this direction, we introduce 360Action, the first omnidirectional video dataset for multi-person action recognition.
1 code implementation • 5 Feb 2020 • Tao Zhuo, Mohan Kankanhalli
Based on the design of the pseudo target, MCPT converts the unsupervised learning problem to a supervised task.
no code implementations • 28 Aug 2019 • Tao Zhuo, Zhiyong Cheng, Mohan Kankanhalli
To overcome this limitation, we propose a novel mask transfer network (MTN), which can greatly boost the processing speed of VOS and also achieve a reasonable accuracy.
1 code implementation • 28 Aug 2019 • Tao Zhuo, Zhiyong Cheng, Peng Zhang, Yongkang Wong, Mohan Kankanhalli
Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking by humans.
1 code implementation • 21 Aug 2019 • Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, Mohan Kankanhalli
To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items.
no code implementations • 22 May 2019 • Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel, Thomas Plagemann, Knut Liestøl, Mohan Kankanhalli
Supervised machine learning applications in the health domain often face the problem of insufficient training datasets.
1 code implementation • 13 May 2019 • Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yibing Liu, Yinglong Wang, Mohan Kankanhalli
Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years.
1 code implementation • 8 Apr 2019 • Yan Luo, Yongkang Wong, Mohan Kankanhalli, Qi Zhao
In addition, analysis of the intra-class compactness and inter-class separability demonstrates the advantages of the proposed function over the softmax function, which is consistent with the performance improvement.
no code implementations • 3 Apr 2019 • Abhinav Shukla, Shruti Shriya Gullapuram, Harish Katti, Mohan Kankanhalli, Stefan Winkler, Ramanathan Subramanian
Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience.
no code implementations • 28 Feb 2019 • Jingfeng Zhang, Bo Han, Laura Wynter, Kian Hsiang Low, Mohan Kankanhalli
Our analytical studies reveal that the step factor h in the Euler method is able to control the robustness of ResNet in both its training and generalization.
1 code implementation • CVPR 2019 • Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
Despite the success of deep neural networks (DNNs) in image classification tasks, the human-level performance relies on massive training data with high-quality manual annotations, which are expensive and time-consuming to collect.
Ranked #14 on
Image Classification
on Clothing1M
(using extra training data)
no code implementations • 12 Nov 2018 • Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C. Kanjirathinkal, Mohan Kankanhalli
Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.
1 code implementation • IEEE Transactions on Image Processing 2019 • Tao Zhuo, Zhiyong Cheng, Peng Zhang, Yongkang Wong, Mohan Kankanhalli
Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset.
Ranked #20 on
Unsupervised Video Object Segmentation
on DAVIS 2016
no code implementations • 14 Aug 2018 • Abhinav Shukla, Harish Katti, Mohan Kankanhalli, Ramanathan Subramanian
Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.
no code implementations • 3 Aug 2017 • Junnan Li, Yongkang Wong, Qi Zhao, Mohan Kankanhalli
However, due to the domain shift problem, the performance of Web images trained deep classifiers tend to degrade when directly deployed to videos.
no code implementations • 21 Jul 2016 • Wenhui Li, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, Mohan Kankanhalli
To enable the study of this problem, there exist a vast number of action datasets, which are recorded under controlled laboratory settings, real-world surveillance environments, or crawled from the Internet.
1 code implementation • 21 Nov 2015 • Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan Kankanhalli
This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena.