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Found 19 papers, 9 papers with code
WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization
Despite the advancements in deep learning for camera relocalization tasks, obtaining ground truth pose labels required for the training process remains a costly endeavor.
Fair Classifiers Without Fair Training: An Influence-Guided Data Sampling Approach
A fair classifier should ensure the benefit of people from different groups, while the group information is often sensitive and unsuitable for model training.
Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Given the cost and difficulty of cleaning these datasets by humans, we introduce a systematic framework for evaluating the credibility of datasets, identifying label errors, and evaluating the influence of noisy labels in the curated language data, specifically focusing on unsafe comments and conversation classification.
Procedural Fairness Through Decoupling Objectionable Data Generating Components
We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i. e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals.
T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation
In this work, we seek to measure more complex human biases exist in the task of text-to-image generations.
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment
Extensive experiments on XTD and Multi30K datasets, covering 11 languages under zero-shot, few-shot, and full-dataset learning scenarios, show that our framework significantly reduces the multilingual disparities among languages and improves cross-lingual transfer results, especially in low-resource scenarios, while only keeping and fine-tuning an extremely small number of parameters compared to the full model (e. g., Our framework only requires 0. 16\% additional parameters of a full-model for each language in the few-shot learning scenario).
Deep Learning based Multi-Label Image Classification of Protest Activities
With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication.
JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents
Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, etc.
Understanding Instance-Level Impact of Fairness Constraints
A variety of fairness constraints have been proposed in the literature to mitigate group-level statistical bias.
Fairness Transferability Subject to Bounded Distribution Shift
Given an algorithmic predictor that is "fair" on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound?
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
We observe that tasks with lower-quality features fail to meet the anchor-point or clusterability condition, due to the coexistence of both uninformative and informative representations.
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather
As a result, it learns to generate minimal image perturbations that are still capable of perplexing the network.
Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good.
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
In this paper, we first quantify the trade-offs introduced by increasing a certain group of instances' label noise rate w. r. t.
Assessing Multilingual Fairness in Pre-trained Multimodal Representations
To answer these questions, we view language as the fairness recipient and introduce two new fairness notions, multilingual individual fairness and multilingual group fairness, for pre-trained multimodal models.
Fair Classification with Group-Dependent Label Noise
We begin by presenting analytical results which show that naively imposing parity constraints on demographic disparity measures, without accounting for heterogeneous and group-dependent error rates, can decrease both the accuracy and the fairness of the resulting classifier.
Linear Classifiers that Encourage Constructive Adaptation
Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome.
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing.
TRL: Discriminative Hints for Scalable Reverse Curriculum Learning
However, tasks with sparse rewards remain challenging when the state space is large.
