Search Results for author:
Found 45 papers, 18 papers with code
Identifying Worry in Twitter: Beyond Emotion Analysis
Identifying the worries of individuals and societies plays a crucial role in providing social support and enhancing policy decision-making.
Inferring DQN structure for high-dimensional continuous control
We propose a novel approach to infer the network structure for DQN models operating with high-dimensional continuous actions.
Disentangled Multimodal Representation Learning for Recommendation
In particular, we adopt a disentangled representation technique to ensure the features of different factors in each modality are independent to each other.
Joint Answering and Explanation for Visual Commonsense Reasoning
Given that our framework is model-agnostic, we apply it to the existing popular baselines and validate its effectiveness on the benchmark dataset.
On Modality Bias Recognition and Reduction
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.
Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests
First, we theoretically show that an adversary can upper-bound the distributional shift which guarantees the attack's invisibility.
Learning to Predict Gradients for Semi-Supervised Continual Learning
To explore these issues, we formulate a new semi-supervised continual learning method, which can be generically applied to existing continual learning models.
Zero-Shot Machine Unlearning
This necessitates deletion of data not only from storage archives but also from ML model.
Fast Yet Effective Machine Unlearning
In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model.
Unsupervised Abstract Reasoning for Raven's Problem Matrices
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.
Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks
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.
Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition
The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images.
Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos
To capture the dynamics in point cloud videos, point tracking is usually employed.
Understanding the Interaction of Adversarial Training with Noisy Labels
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.
Effective Abstract Reasoning with Dual-Contrast Network
Abstract reasoning is a challenging task in artificial intelligence.
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
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.
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions
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.
Unsupervised Spatial-spectral Network Learning for Hyperspectral Compressive Snapshot Reconstruction
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.
Geometry-aware Instance-reweighted Adversarial Training
The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy.
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
A new model is trained with these labels to generalize reliably despite the label noise.
My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
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.
Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization
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.
Robust Federated Recommendation System
We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients.
Hierarchically Fair Federated Learning
When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded.
Adversarial Learning for Personalized Tag Recommendation
We demonstrate the effectiveness of the proposed model on two different large-scale and publicly available datasets, YFCC100M and NUS-WIDE.
Evaluating Salient Object Detection in Natural Images with Multiple Objects having Multi-level Saliency
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.
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger
Adversarial training based on the minimax formulation is necessary for obtaining adversarial robustness of trained models.
GradMix: Multi-source Transfer across Domains and Tasks
Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task.
Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos
To enable research in this direction, we introduce 360Action, the first omnidirectional video dataset for multi-person action recognition.
Solving Raven's Progressive Matrices with Neural Networks
Based on the design of the pseudo target, MCPT converts the unsupervised learning problem to a supervised task.
Fast Video Object Segmentation via Mask Transfer Network
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.
Explainable Video Action Reasoning via Prior Knowledge and State Transitions
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.
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
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.
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection
Supervised machine learning applications in the health domain often face the problem of insufficient training datasets.
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering
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.
$\mathcal{G}$-softmax: Improving Intra-class Compactness and Inter-class Separability of Features
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.
Recognition of Advertisement Emotions with Application to Computational Advertising
Advertisements (ads) often contain strong affective content to capture viewer attention and convey an effective message to the audience.
Towards Robust ResNet: A Small Step but A Giant Leap
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.
Learning to Learn from Noisy Labeled Data
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)
MMALFM: Explainable Recommendation by Leveraging Reviews and Images
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.
Unsupervised Online Video Object Segmentation with Motion Property Understanding
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
Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements
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.
Attention Transfer from Web Images for Video Recognition
However, due to the domain shift problem, the performance of Web images trained deep classifiers tend to degrade when directly deployed to videos.
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking
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.
Near-Optimal Active Learning of Multi-Output Gaussian Processes
This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena.
