6 papers
What Remains OOD? OODConceptBench for Open-World Concept Recognition in Vision-Language Models
Zhiran Luo, Zhiyong Yang, Zitai Wang +3
In the era of large-scale pretrained models, zero-shot generalization has increasingly blurred the boundary between known and unknown concepts. However, current evaluations do not…
ToPA: Block-wise Toeplitz Adaptation for Expressive and Efficient Fine-Tuning
Sicong Li, Qianqian Xu, Zhiyong Yang +4
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a prevalent strategy for adapting large pre-trained models. Among PEFT techniques, reparameterization-based methods, particula…
Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs
Zhikang Xu, Qianqian Xu, Zitai Wang +4
Out-of-distribution (OOD) detection seeks to identify samples from unknown classes, a critical capability for deploying machine learning models in open-world scenarios. Recent rese…
Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection
Boyu Han, Qianqian Xu, Shilong Bao +3
In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data. To handle the challenges posed by subtle and infrequ…
Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification
Sicong Li, Qianqian Xu, Zhiyong Yang +4
Real-world datasets often follow a long-tailed distribution, making generalization to tail classes difficult. Recent methods resorted to long-tail variants of Sharpness-Aware Minim…
DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations
Zhiyong Yang, Qianqian Xu, Sicong Li +3
This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced. We argue that the v…