633 citations · 2.7k across the 61 of their papers we have counts for
19 papers · 1 filter
How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?
Yifei Ming, Yixuan Li
Recent large vision-language models such as CLIP have shown remarkable out-of-distribution (OOD) detection and generalization performance. However, their zero-shot in-distribution…
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey
Ben Fei, Weidong Yang, Liwen Liu +4
Point cloud data has been extensively studied due to its compact form and flexibility in representing complex 3D structures. The ability of point cloud data to accurately capture a…
Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
Yiyou Sun, Yaojie Liu, Xiaoming Liu +2
This work studies the generalization issue of face anti-spoofing (FAS) models on domain gaps, such as image resolution, blurriness and sensor variations. Most prior works regard do…
I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps through Bird's Eye View Projections
Shuhang Zheng, Yixuan Li, Zhu Yu +8
Place recognition is an important technique for autonomous cars to achieve full autonomy since it can provide an initial guess to online localization algorithms. Although current m…
Delving into Out-of-Distribution Detection with Vision-Language Representations
Yifei Ming, Ziyang Cai, Jiuxiang Gu +3
Recognizing out-of-distribution (OOD) samples is critical for machine learning systems deployed in the open world. The vast majority of OOD detection methods are driven by a single…
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Jingkang Yang, Pengyun Wang, Dejian Zou +13
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the lit…
Are Vision Transformers Robust to Spurious Correlations?
Soumya Suvra Ghosal, Yifei Ming, Yixuan Li
Deep neural networks may be susceptible to learning spurious correlations that hold on average but not in atypical test samples. As with the recent emergence of vision transformer…
Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild
Xuefeng Du, Xin Wang, Gabriel Gozum +1
Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical yet underexplored. One of the key challenges is that models lack supervision signal…
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?
Yifei Ming, Yiyou Sun, Ousmane Dia +1
Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent advances in representation learning give rise to distance-based OOD detection, where te…
Interactive Image Inpainting Using Semantic Guidance
Wangbo Yu, Jinhao Du, Ruixin Liu +2
Image inpainting approaches have achieved significant progress with the help of deep neural networks. However, existing approaches mainly focus on leveraging the priori distributio…
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks +3
Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign…
Generalized Out-of-Distribution Detection: A Survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li +1
Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous driving, we would like the driving s…
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
Rui Huang, Yixuan Li
Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Existing solutions are mainly driven by small data…
MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions
Yixuan Li, Lei Chen, Runyu He +3
Spatio-temporal action detection is an important and challenging problem in video understanding. The existing action detection benchmarks are limited in aspects of small numbers of…
MOOD: Multi-level Out-of-distribution Detection
Ziqian Lin, Sreya Dutta Roy, Yixuan Li
Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. While improved OOD detection methods have emerged, they…
Actions as Moving Points
Yixuan Li, Zixu Wang, Limin Wang +1
The existing action tubelet detectors often depend on heuristic anchor design and placement, which might be computationally expensive and sub-optimal for precise localization. In t…
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search
Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz +2
A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as t…
Exploring the Limits of Weakly Supervised Pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan +5
State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Yet,…
Stacked Generative Adversarial Networks
Xun Huang, Yixuan Li, Omid Poursaeed +2
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up…