20 citations · 22 across the 11 of their papers we have counts for
11 papers
CoGe-GCD: Reframing Generalized Category Discovery with Compositional Generalization
Luyao Tang, Jiewei Zheng, Kunze Huang +3
Generalized Category Discovery (GCD) assigns unlabeled instances, mixed with labeled data, to known or novel categories, requiring human-like compositional reasoning: reusing primi…
Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance
Luyao Tang, Bingjun Luo, Dong Yi +5
Reliable video generation requires more than high-quality frames to form a coherent story: a model must maintain a persistent state, transporting visual attributes such as identity…
Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction
Luyao Tang, Kunze Huang, Chaoqi Chen +5
Human perceptual systems excel at inducing and recognizing objects across both known and novel categories, a capability far beyond current machine learning frameworks. While genera…
Generalized Category Discovery in Event-Centric Contexts: Latent Pattern Mining with LLMs
Yi Luo, Qiwen Wang, Junqi Yang +5
Generalized Category Discovery (GCD) aims to classify both known and novel categories using partially labeled data that contains only known classes. Despite achieving strong perfor…
Generalized Category Discovery via Token Manifold Capacity Learning
Luyao Tang, Kunze Huang, Chaoqi Chen +1
Generalized category discovery (GCD) is essential for improving deep learning models' robustness in open-world scenarios by clustering unlabeled data containing both known and nove…
OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +3
Although foundation models (FMs) claim to be powerful, their generalization ability significantly decreases when faced with distribution shifts, weak supervision, or malicious atta…
Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric Learning
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +3
Foundation models have made incredible strides in achieving zero-shot or few-shot generalization, leveraging prompt engineering to mimic the problem-solving approach of human intel…
Mixstyle-Entropy: Domain Generalization with Causal Intervention and Perturbation
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +2
Despite the considerable advancements achieved by deep neural networks, their performance tends to degenerate when the test environment diverges from the training ones. Domain gene…
Activate and Reject: Towards Safe Domain Generalization under Category Shift
Chaoqi Chen, Luyao Tang, Leitian Tao +4
Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel do…
SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection
Zhijie Rao, Jingcai Guo, Luyao Tang +3
This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structure…
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
Chaoqi Chen, Luyao Tang, Feng Liu +3
Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one. The general objective of DG methods is to learn semant…