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Xiongwei Zhu

3 accepted papers

2026

ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference

ICML 2026poster

Fine-grained Mixture-of-Experts (MoE) models sparsely activate a subset of parameters, significantly reducing computational costs while maintaining performance. However, in memory-constrained inference scenarios, only a small set of experts can be cached. Experts not in the cache must be fetched fro…

Cited by 0SourceScholar
2024

Decouple Content and Motion for Conditional Image-to-Video Generation

AAAI 2024technical

The goal of conditional image-to-video (cI2V) generation is to create a believable new video by beginning with the condition, i.e., one image and text. The previous cI2V generation methods conventionally perform in RGB pixel space, with limitations in modeling motion consistency and visual continuit…

Cited by 5SourcePDFScholar
2022

ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval

CVPR 2022poster

Visual appearance is considered to be the most important cue to understand images for cross-modal retrieval, while sometimes the scene text appearing in images can provide valuable information to understand the visual semantics. Most of existing cross-modal retrieval approaches ignore the usage of s…

Cited by 79PDFScholar