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Found 17 papers, 7 papers with code
Rich Human Feedback for Text-to-Image Generation
We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.
UniAR: Unifying Human Attention and Response Prediction on Visual Content
Such a model would enable predicting subjective feedback such as overall satisfaction or aesthetic quality ratings, along with the underlying human attention or interaction heatmaps and viewing order, enabling designers and content-creation models to optimize their creation for human-centric improvements.
Dual Node and Edge Fairness-Aware Graph Partition
To address this gap, we propose a notion edge balance to measure the proportion of edges connecting different demographic groups in clusters.
Learning Antidote Data to Individual Unfairness
Fairness is essential for machine learning systems deployed in high-stake applications.
Characterizing the Influence of Graph Elements
To fill this gap, we started with the simple graph convolution (SGC) model that operates on an attributed graph and formulated an influence function to approximate the changes in model parameters when a node or an edge is removed from an attributed graph.
Robust Fair Clustering: A Novel Fairness Attack and Defense Framework
Experimentally, we observe that CFC is highly robust to the proposed attack and is thus a truly robust fair clustering alternative.
Exploiting Temporal Relations on Radar Perception for Autonomous Driving
We consider the object recognition problem in autonomous driving using automotive radar sensors.
Ranked #1 on
Multiple Object Tracking
on RADIATE
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
With the fast development of algorithmic governance, fairness has become a compulsory property for machine learning models to suppress unintentional discrimination.
Code Editing from Few Exemplars by Adaptive Multi-Extent Composition
To achieve this, we propose a machine learning approach to adapt the editorial style derived from few exemplars to a query code snippet.
Deep Clustering based Fair Outlier Detection
In this paper, we focus on the fairness issues regarding unsupervised outlier detection.
SelfDoc: Self-Supervised Document Representation Learning
For downstream usage, we propose a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals.
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
Disparate impact has raised serious concerns in machine learning applications and its societal impacts.
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain.
Deep Fair Clustering for Visual Learning
In light of these limitations, in this paper, we propose Deep Fair Clustering (DFC) to learn fair and clustering-favorable representations for clustering simultaneously.
Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking
In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT).
Two-Stream Multi-Task Network for Fashion Recognition
In this paper, we present a two-stream multi-task network for fashion recognition.
Model-free Tracking with Deep Appearance and Motion Features Integration
To overcome the object-centric information scarcity, both appearance and motion features are deeply integrated by the proposed AMNet, which is an end-to-end offline trained two-stream network.
