most citedWhat Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?

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6 papers

cs.LG20241 cited

What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?

Weijie Tu, Weijian Deng, Liang Zheng +1

This work aims to develop a measure that can accurately rank the performance of various classifiers when they are tested on unlabeled data from out-of-distribution (OOD) distributi…

cs.CV2024

Optimizing Calibration by Gaining Aware of Prediction Correctness

Yuchi Liu, Lei Wang, Yuli Zou +2

Model calibration aims to align confidence with prediction correctness. The Cross-Entropy (CE) loss is widely used for calibrator training, which enforces the model to increase con…

cs.CV2024

Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment

Lei Wang, Jun Liu, Liang Zheng +2

Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment…

cs.CV2024

Taylor Videos for Action Recognition

Lei Wang, Xiuyuan Yuan, Tom Gedeon +1

Effectively extracting motions from video is a critical and long-standing problem for action recognition. This problem is very challenging because motions (i) do not have an explic…

cs.CV2023

Optimizing Camera Configurations for Multi-View Pedestrian Detection

Yunzhong Hou, Xingjian Leng, Tom Gedeon +1

Jointly considering multiple camera views (multi-view) is very effective for pedestrian detection under occlusion. For such multi-view systems, it is critical to have well-designed…

cs.CV2023

Adaptive Multi-head Contrastive Learning

Lei Wang, Piotr Koniusz, Tom Gedeon +1

In contrastive learning, two views of an original image, generated by different augmentations, are considered a positive pair, and their similarity is required to be high. Similarl…