107 citations · 181 across the 16 of their papers we have counts for
16 papers
SurgNarrator: A Generative Retrieval Framework for Surgical Video Understanding
Yuqing Feng, Jiawei Ma, Kevin Qinghong Lin +6
Surgical procedures unfold as structured and recurring clinical events, whose real-time understanding via intraoperative surgical videos is critical for intraoperative decision-mak…
Bi-CRCL: Bidirectional Conservative-Radical Complementary Learning with Pre-trained Foundation Models for Class-incremental Medical Image Analysis
Xinyao Wu, Zhe Xu, Cheng Chen +3
Class-incremental learning (CIL) in medical image-guided diagnosis requires retaining prior diagnostic knowledge while adapting to newly emerging disease categories, which is criti…
WIDIn: Wording Image for Domain-Invariant Representation in Single-Source Domain Generalization
Jiawei Ma, Yulei Niu, Shiyuan Huang +2
Language has been useful in extending the vision encoder to data from diverse distributions without empirical discovery in training domains. However, as the image description is mo…
MoDE: CLIP Data Experts via Clustering
Jiawei Ma, Po-Yao Huang, Saining Xie +5
The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We…
Supervised Masked Knowledge Distillation for Few-Shot Transformers
Han Lin, Guangxing Han, Jiawei Ma +3
Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features. However,…
DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection
Jiawei Ma, Yulei Niu, Jincheng Xu +3
Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approa…
TempCLR: Temporal Alignment Representation with Contrastive Learning
Yuncong Yang, Jiawei Ma, Shiyuan Huang +4
Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a commo…
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
Guangxing Han, Long Chen, Jiawei Ma +3
We study multi-modal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection, which are complementary to ea…
Few-Shot Object Detection with Fully Cross-Transformer
Guangxing Han, Jiawei Ma, Shiyuan Huang +2
Few-shot object detection (FSOD), with the aim to detect novel objects using very few training examples, has recently attracted great research interest in the community. Metric-lea…
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks
Guangxing Han, Yicheng He, Shiyuan Huang +2
Few-shot object detection (FSOD) aims to detect never-seen objects using few examples. This field sees recent improvement owing to the meta-learning techniques by learning how to m…
Partner-Assisted Learning for Few-Shot Image Classification
Jiawei Ma, Hanchen Xie, Guangxing Han +3
Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learn…
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
Guangxing Han, Shiyuan Huang, Jiawei Ma +2
Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object…
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
Shiyuan Huang, Jiawei Ma, Guangxing Han +1
We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rej…
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang, Manling Li, Xuan Wang +24
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and rel…
Snapshot Interferometric 3D Imaging by Compressive Sensing and Deep Learning
Mu Qiao, Yangyang Sun, Jiawei Ma +3
We demonstrate single-shot compressive three-dimensional (3D) imaging based on interference coding. The depth dimension of the object is encoded into the interferometri…
CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
Jiawei Ma, Zheng Shou, Alireza Zareian +3
Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements. For example, air quality monit…