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Found 4 papers, 2 papers with code
FairDeDup: Detecting and Mitigating Vision-Language Fairness Disparities in Semantic Dataset Deduplication
Recent dataset deduplication techniques have demonstrated that content-aware dataset pruning can dramatically reduce the cost of training Vision-Language Pretrained (VLP) models without significant performance losses compared to training on the original dataset.
On the Behavior of Audio-Visual Fusion Architectures in Identity Verification Tasks
We train an identity verification architecture and evaluate modifications to the part of the model that combines audio and visual representations, including in scenarios where one input is missing in either of two examples to be compared.
VLSlice: Interactive Vision-and-Language Slice Discovery
Recent work in vision-and-language demonstrates that large-scale pretraining can learn generalizable models that are efficiently transferable to downstream tasks.
Fine-Grained Classroom Activity Detection from Audio with Neural Networks
We obtain strong results on the new fine-grained task and state-of-the-art on the 4-way task: our best model obtains frame-level error rates of 6. 2%, 7. 7% and 28. 0% when generalizing to unseen instructors for the 4-way, 5-way, and 9-way classification tasks, respectively (relative reductions of 35. 4%, 48. 3% and 21. 6% over a strong baseline).
