1.2k citations · 1.2k across the 9 of their papers we have counts for
9 papers
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection
Yiran Qin, Chaoqun Wang, Zijian Kang +3
In this paper, we propose a novel training strategy called SupFusion, which provides an auxiliary feature level supervision for effective LiDAR-Camera fusion and significantly boos…
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
Jie Yang, Chaoqun Wang, Zhen Li +2
This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i.e. vario…
CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization
Wei Liu, Huanqin Wu, Wenjing Mu +3
Generating factual-consistent summaries is a challenging task for abstractive summarization. Previous works mainly encode factual information or perform post-correct/rank after dec…
Active Learning for Event Extraction with Memory-based Loss Prediction Model
Shirong Shen, Zhen Li, Guilin Qi
Event extraction (EE) plays an important role in many industrial application scenarios, and high-quality EE methods require a large amount of manual annotation data to train superv…
Multi-Compound Transformer for Accurate Biomedical Image Segmentation
Yuanfeng Ji, Ruimao Zhang, Huijie Wang +4
The recent vision transformer(i.e.for image classification) learns non-local attentive interaction of different patch tokens. However, prior arts miss learning the cross-scale depe…
CARLS: Cross-platform Asynchronous Representation Learning System
Chun-Ta Lu, Yun Zeng, Da-Cheng Juan +13
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge make…
Combining Supervised and Un-supervised Learning for Automatic Citrus Segmentation
Heqing Huang, Tongbin Huang, Zhen Li +2
Citrus segmentation is a key step of automatic citrus picking. While most current image segmentation approaches achieve good segmentation results by pixel-wise segmentation, these…
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Chao Jia, Yinfei Yang, Ye Xia +7
Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human anno…
Unifying Specialist Image Embedding into Universal Image Embedding
Yang Feng, Futang Peng, Xu Zhang +7
Deep image embedding provides a way to measure the semantic similarity of two images. It plays a central role in many applications such as image search, face verification, and zero…