76 citations · 98 across the 9 of their papers we have counts for
14 papers
SelecMix: Debiased Learning by Contradicting-pair Sampling
Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak +4
Neural networks trained with ERM (empirical risk minimization) sometimes learn unintended decision rules, in particular when their training data is biased, i.e., when training labe…
Active Learning by Feature Mixing
Amin Parvaneh, Ehsan Abbasnejad, Damien Teney +3
The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is e…
Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models
Zheyuan Liu, Cristian Rodriguez-Opazo, Damien Teney +1
We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been…
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering
Corentin Dancette, Remi Cadene, Damien Teney +1
We introduce an evaluation methodology for visual question answering (VQA) to better diagnose cases of shortcut learning. These cases happen when a model exploits spurious statisti…
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge
Violetta Shevchenko, Damien Teney, Anthony Dick +1
The limits of applicability of vision-and-language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense a…
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Damien Teney, Kushal Kafle, Robik Shrestha +3
Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine learning system's ability to generalize beyond the biases of a training set. OOD benchmarks are d…