Search Results for author: Xinyu Sun

Found 7 papers, 4 papers with code

RGM: A Robust Generalizable Matching Model

1 code implementation18 Oct 2023 Songyan Zhang, Xinyu Sun, Hao Chen, Bo Li, Chunhua Shen

Finding corresponding pixels within a pair of images is a fundamental computer vision task with various applications.

Optical Flow Estimation

Contrastive Vision-Language Alignment Makes Efficient Instruction Learner

1 code implementation29 Nov 2023 Lizhao Liu, Xinyu Sun, Tianhang Xiang, Zhuangwei Zhuang, Liuren Yin, Mingkui Tan

To address this, existing methods typically train a visual adapter to align the representation between a pre-trained vision transformer (ViT) and the LLM by a generative image captioning loss.

Contrastive Learning Image Captioning +4

SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector

1 code implementation14 Dec 2023 Shuailei Ma, Yuefeng Wang, Ying WEI, Jiaqi Fan, Enming Zhang, Xinyu Sun, Peihao Chen

Ablation experiments demonstrate that both of them are effective in mitigating the impact of open-world knowledge distillation on the learning of known objects.

Knowledge Distillation Object +3

Descriptellation: Deep Learned Constellation Descriptors

no code implementations1 Mar 2022 Chunwei Xing, Xinyu Sun, Andrei Cramariuc, Samuel Gull, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Florian Tschopp

However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.

Simultaneous Localization and Mapping

$A^2$Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models

no code implementations15 Aug 2023 Peihao Chen, Xinyu Sun, Hongyan Zhi, Runhao Zeng, Thomas H. Li, Gaowen Liu, Mingkui Tan, Chuang Gan

We study the task of zero-shot vision-and-language navigation (ZS-VLN), a practical yet challenging problem in which an agent learns to navigate following a path described by language instructions without requiring any path-instruction annotation data.

Navigate Robot Navigation +1

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