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Adapting Dual-encoder Vision-language Models for Paraphrased Retrieval
In this work, we consider the task of paraphrased text-to-image retrieval where a model aims to return similar results given a pair of paraphrased queries.
Calibrating Deep Neural Networks by Pairwise Constraints
This suggests the hypothesis that DNN calibration can be improved by providing calibration supervision to all such binary problems.
Learning Deep Classifiers Consistent With Fine-Grained Novelty Detection
The problem of novelty detection in fine-grained visual classification (FGVC) is considered.
Learning with Bounded Instance- and Label-dependent Label Noise
Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN.
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