6 papers
Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation
Hanwen Shen, Ting Ying, Jiajie Lu +1
Although debiased large language models (LLMs) excel at handling known or low-bias prompts, they often fail on unfamiliar and high-bias prompts. We demonstrate via out-of-distribut…
FOUND: Fourier-based von Mises Distribution for Robust Single Domain Generalization in Object Detection
Mengzhu Wang, Changyuan Deng, Shanshan Wang +3
Single Domain Generalization (SDG) for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains. While recent metho…
StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection
Yanning Hou, Yanran Ruan, Junfa Li +3
Enhancing the alignment between text and image features in the CLIP model is a critical challenge in zero-shot industrial anomaly detection tasks. Recent studies predominantly util…
H2OVL-Mississippi Vision Language Models Technical Report
Shaikat Galib, Shanshan Wang, Guanshuo Xu +4
Smaller vision-language models (VLMs) are becoming increasingly important for privacy-focused, on-device applications due to their ability to run efficiently on consumer hardware f…
Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion
Jiawei Li, Sitong Li, Shanshan Wang +3
Deploying machine learning in open environments presents the challenge of encountering diverse test inputs that differ significantly from the training data. These out-of-distributi…
Positive and negative sampling strategies for self-supervised learning on audio-video data
Shanshan Wang, Soumya Tripathy, Toni Heittola +1
In Self-Supervised Learning (SSL), Audio-Visual Correspondence (AVC) is a popular task to learn deep audio and video features from large unlabeled datasets. The key step in AVC is…