4 citations · 6 across the 10 of their papers we have counts for
10 papers
MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
Mingqiao Ye, Zhaochong An, Zhitong Gao +11
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly…
(1D) Ordered Tokens Enable Efficient Test-Time Search
Zhitong Gao, Parham Rezaei, Ali Cy +7
Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, tokens describe local information,…
Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts
Zhitong Gao, Bingnan Li, Mathieu Salzmann +1
In open-world scenarios, where both novel classes and domains may exist, an ideal segmentation model should detect anomaly classes for safety and generalize to new domains. However…
Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation
Bingnan Li, Zhitong Gao, Xuming He
Cross-modal MRI segmentation is of great value for computer-aided medical diagnosis, enabling flexible data acquisition and model generalization. However, most existing methods hav…
ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution Detection in Segmentation
Zhitong Gao, Shipeng Yan, Xuming He
Recent advancements in dense out-of-distribution (OOD) detection have primarily focused on scenarios where the training and testing datasets share a similar domain, with the assump…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
Chuanyang Hu, Shipeng Yan, Zhitong Gao +1
Despite deep learning has achieved great success, it often relies on a large amount of training data with accurate labels, which are expensive and time-consuming to collect. A prom…
Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts
Zhitong Gao, Yucong Chen, Chuyu Zhang +1
Equipping predicted segmentation with calibrated uncertainty is essential for safety-critical applications. In this work, we focus on capturing the data-inherent uncertainty (aka a…
Mutual Information-guided Knowledge Transfer for Novel Class Discovery
Chuyu Zhang, Chuanyang Hu, Ruijie Xu +3
We tackle the novel class discovery problem, aiming to discover novel classes in unlabeled data based on labeled data from seen classes. The main challenge is to transfer knowledge…
Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation
Shuailin Li, Zhitong Gao, Xuming He
Learning segmentation from noisy labels is an important task for medical image analysis due to the difficulty in acquiring highquality annotations. Most existing methods neglect th…