Search Results for author: Huanyu Liu

Found 7 papers, 3 papers with code

High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

2 code implementations CVPR 2020 Guan'an Wang, Shuo Yang, Huanyu Liu, Zhicheng Wang, Yang Yang, Shuliang Wang, Gang Yu, Erjin Zhou, Jian Sun

When aligning two groups of local features from two images, we view it as a graph matching problem and propose a cross-graph embedded-alignment (CGEA) layer to jointly learn and embed topology information to local features, and straightly predict similarity score.

Graph Matching Person Re-Identification +1

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models

1 code implementation24 Feb 2024 Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Ge Li

CDD necessitates only the sampled texts to detect data contamination, by identifying the peakedness of LLM's output distribution.

Memorization

An End-to-End Network for Panoptic Segmentation

no code implementations CVPR 2019 Huanyu Liu, Chao Peng, Changqian Yu, Jingbo Wang, Xu Liu, Gang Yu, Wei Jiang

Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic.

Panoptic Segmentation Segmentation

Coarse to Fine: Video Retrieval before Moment Localization

no code implementations14 Oct 2021 Zijian Gao, Huanyu Liu, Jingyu Liu

The current state-of-the-art methods for video corpus moment retrieval (VCMR) often use similarity-based feature alignment approach for the sake of convenience and speed.

Moment Retrieval Retrieval +2

DevEval: Evaluating Code Generation in Practical Software Projects

no code implementations12 Jan 2024 Jia Li, Ge Li, YunFei Zhao, Yongmin Li, Zhi Jin, Hao Zhu, Huanyu Liu, Kaibo Liu, Lecheng Wang, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yihong Dong, Yuqi Zhu, Bin Gu, Mengfei Yang

Compared to previous benchmarks, DevEval aligns to practical projects in multiple dimensions, e. g., real program distributions, sufficient dependencies, and enough-scale project contexts.

Code Generation

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