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From the 1 of 14 linked papers with an AI index.

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20242026
most citedPrompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization

1 citations · 1 across the 3 of their papers we have counts for

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14 papers

cs.CV2026

Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection

Mingyue Zeng, De Cheng, Zhipeng Xu +3

The paper introduces Symbiosis-Inspired Knowledge Distillation (SIKD), a method for incremental object detection that leverages spatial and semantic relationships between old and n…

cs.CV2026

Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement

Xiangqian Zhao, Xinyang Jiang, Zhipeng Xu +5

Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes, resulting in poor generalization on minority group…

cs.CV2026

Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization

Zhipeng Xu, De Cheng, Xinyang Jiang +3

Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain available for training. One o…

cs.CV20261 cited

Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization

De Cheng, Zhipeng Xu, Xinyang Jiang +3

Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains. Notably, recent advances in pre-trained Visual Foundation…

cs.AI2026

Reasoning-Driven Multimodal LLM for Domain Generalization

Zhipeng Xu, Zilong Wang, Xinyang Jiang +3

This paper addresses the domain generalization (DG) problem in deep learning. While most DG methods focus on enforcing visual feature invariance, we leverage the reasoning capabili…

cs.CV2026

Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection

Ying Yang, De Cheng, Chaowei Fang +4

Unsupervised out-of-distribution (OOD) detection aims to identify out-of-domain data by learning only from unlabeled In-Distribution (ID) training samples, which is crucial for dev…