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Xiao Long

7 accepted papers

2026

Exploring Mode Connectivity in Krylov Subspace for Domain Generalization

ICLR 2026poster

This paper explores the geometric characteristics of loss landscapes to enhance domain generalization (DG) in deep neural networks. Existing methods mainly leverage the local flatness around minima for improved generalization. However, recent theoretical studies indicate that flatness does not univ…

Cited by 0SourceScholar
2026

Flatness Guided Test-Time Adaptation for Vision-Language Models

ICLR 2026poster

Test-time adaptation (TTA) of Vision-Language Models (VLMs) has emerged as a technique for tackling distribution shifts during the test time. Recent research indicates that the test-time adaptation is intrinsically linked to the model's training history. However, existing TTA methods, such as Test-…

Cited by 0SourceScholar
2026

PerformRecast: Expression and Head Pose Disentanglement for Portrait Video Editing

CVPR 2026

This paper primarily investigates the task of expression-only portrait video performance editing based on a driving video, which plays a crucial role in animation and film industries. Most existing research mainly focuses on portrait animation, which aims to animate a static portrait image according

Cited by 0SourcecodeScholar
2025

EPERM: An Evidence Path Enhanced Reasoning Model for Knowledge Graph Question and Answering

AAAI 2025technical

Due to the remarkable reasoning ability, Large language models (LLMs) have demonstrated impressive performance in knowledge graph question answering (KGQA) tasks, which find answers to natural language questions over knowledge graphs (KGs). To alleviate the hallucinations and lack of knowledge issue…

Cited by 0SourcePDFScholar
2025

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes

CVPR 2025poster

Domain generalization aims to learn a model from multiple training domains and generalize it to unseen test domains. Recent theory has shown that seeking the deep models, whose parameters lie in the flat minima of the loss landscape, can significantly reduce the out-of-domain generalization error. H…

Cited by 0SourcePDFScholar
2024

KGDM: A Diffusion Model to Capture Multiple Relation Semantics for Knowledge Graph Embedding

AAAI 2024technical

Knowledge graph embedding (KGE) is an efficient and scalable method for knowledge graph completion. However, most existing KGE methods suffer from the challenge of multiple relation semantics, which often degrades their performance. This is because most KGE methods learn fixed continuous vectors for…

2022

Neural-based Mixture Probabilistic Query Embedding for Answering FOL queries on Knowledge Graphs

EMNLP 2022main

Query embedding (QE)—which aims to embed entities and first-order logical (FOL) queries in a vector space, has shown great power in answering FOL queries on knowledge graphs (KGs). Existing QE methods divide a complex query into a sequence of mini-queries according to its computation graph and perfo…

Cited by 11SourcePDFScholar