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Found 9 papers, 4 papers with code
FairRAG: Fair Human Generation via Fair Retrieval Augmentation
In this work, we introduce Fair Retrieval Augmented Generation (FairRAG), a novel framework that conditions pre-trained generative models on reference images retrieved from an external image database to improve fairness in human generation.
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
We address these problems by introducing: (1) A unified benchmarking and implementation framework, Align and Distill (ALDI), enabling comparison of DAOD methods and supporting future development, (2) A fair and modern training and evaluation protocol for DAOD that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset, CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method, ALDI++, that achieves state-of-the-art results by a large margin.
Ranked #1 on
Unsupervised Domain Adaptation
on CFC-DAOD
Balanced Filtering via Non-Disclosive Proxies
Importantly, we require that the proxy classification itself not reveal significant information about the sensitive group membership of any individual sample (i. e., it should be sufficiently non-disclosive).
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer.
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for detecting, tracking, and counting fish in sonar videos.
Multi-Dimensional, Nuanced and Subjective - Measuring the Perception of Facial Expressions
Humans can perceive multiple expressions, each one with varying intensity, in the picture of a face.
Harnessing Unrecognizable Faces for Improving Face Recognition
The common implementation of face recognition systems as a cascade of a detection stage and a recognition or verification stage can cause problems beyond failures of the detector.
Positive-Congruent Training: Towards Regression-Free Model Updates
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
SMOT: Single-Shot Multi Object Tracking
We combine this scheme with SSD detectors by proposing a novel tracking anchor assignment module.
