no code implementations • 28 Mar 2024 • Lingjun Zhao, Jingyu Song, Katherine A. Skinner
Alternatively, camera and radar are commonly deployed on vehicles already on the road today, but performance of Camera-Radar (CR) fusion falls behind LC fusion.
no code implementations • 26 Feb 2024 • Lingjun Zhao, Khanh Nguyen, Hal Daumé III
This paper addresses the challenge of leveraging imperfect language models to guide human decision-making in the context of a grounded navigation task.
1 code implementation • 18 Feb 2024 • Jingyu Song, Lingjun Zhao, Katherine A. Skinner
We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors.
no code implementations • 23 Oct 2023 • Lingjun Zhao, Khanh Nguyen, Hal Daumé III
We investigate the problem of generating instructions to guide humans to navigate in simulated residential environments.
no code implementations • 21 Dec 2022 • Lingjun Zhao, Khanh Nguyen, Hal Daumé III
Recent work studies the cognitive capabilities of language models through psychological tests designed for humans.
no code implementations • 18 Jan 2022 • Yan Zhao, Lingjun Zhao, Zhong Liu, Dewen Hu, Gangyao Kuang, Li Liu
Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference.
no code implementations • LREC 2020 • Le Zhang, Damianos Karakos, William Hartmann, Manaj Srivastava, Lee Tarlin, David Akodes, Sanjay Krishna Gouda, Numra Bathool, Lingjun Zhao, Zhuolin Jiang, Richard Schwartz, John Makhoul
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English.
no code implementations • LREC 2020 • Bonan Min, Yee Seng Chan, Lingjun Zhao
Previous approaches treat event extraction as {``}one size fits all{''} with an ontology defined a priori.
1 code implementation • LREC 2020 • Zhuolin Jiang, Amro El-Jaroudi, William Hartmann, Damianos Karakos, Lingjun Zhao
Multiple neural language models have been developed recently, e. g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking.
no code implementations • WS 2019 • Lingjun Zhao, Rabih Zbib, Zhuolin Jiang, Damianos Karakos, Zhongqiang Huang
We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages.