6 papers
Adaptive Attention Distillation for Robust Few-Shot Segmentation under Environmental Perturbations
Qianyu Guo, Jingrong Wu, Jieji Ren +2
Few-shot segmentation (FSS) aims to rapidly learn novel class concepts from limited examples to segment specific targets in unseen images, and has been widely applied in areas such…
Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection
Jinglun Li, Kaixun Jiang, Zhaoyu Chen +4
Pre-trained vision-language models have exhibited remarkable abilities in detecting out-of-distribution (OOD) samples. However, some challenging OOD samples, which lie close to in-…
Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning
Qianyu Guo, Jingrong Wu, Tianxing Wu +3
Few-shot learning (FSL) has recently been extensively utilized to overcome the scarcity of training data in domain-specific visual recognition. In real-world scenarios, environment…
DeTrack: In-model Latent Denoising Learning for Visual Object Tracking
Xinyu Zhou, Jinglun Li, Lingyi Hong +4
Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. Image-feature regression methods hea…
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection
Jinglun Li, Xinyu Zhou, Pinxue Guo +4
Detecting out-of-distribution inputs for visual recognition models has become critical in safe deep learning. This paper proposes a novel hierarchical visual category modeling sche…
TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning
Jinglun Li, Xinyu Zhou, Kaixun Jiang +5
Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This enriched data representation improves performance across various tasks. Existing meth…