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Found 5 papers, 4 papers with code
MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection
Combining the strengths of many existing predictors to obtain a Mixture of Experts which is superior to its individual components is an effective way to improve the performance without having to develop new architectures or train a model from scratch.
Ranked #1 on
Oriented Object Detection
on DOTA 1.0
Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration
The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality.
Sample-dependent Adaptive Temperature Scaling for Improved Calibration
The most common post-hoc approach to compensate for this is to perform temperature scaling, which adjusts the confidences of the predictions on any input by scaling the logits by a fixed value.
Learning Multimodal VAEs through Mutual Supervision
Here we introduce a novel alternative, the MEME, that avoids such explicit combinations by repurposing semi-supervised VAEs to combine information between modalities implicitly through mutual supervision.
Capturing Label Characteristics in VAEs
We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels.
