11 citations · 28 across the 9 of their papers we have counts for
9 papers
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images
Yingping Liang, Ying Fu, Yutao Hu +3
Optical flow estimation is a crucial subfield of computer vision, serving as a foundation for video tasks. However, the real-world robustness is limited by animated synthetic datas…
Recurrent Self-Supervised Video Denoising with Denser Receptive Field
Zichun Wang, Yulun Zhang, Debing Zhang +1
Self-supervised video denoising has seen decent progress through the use of blind spot networks. However, under their blind spot constraints, previous self-supervised video denoisi…
AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection
Wentao Zhu, Yufang Huang, Xiufeng Xie +5
The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram…
LP-SLAM: Language-Perceptive RGB-D SLAM system based on Large Language Model
Weiyi Zhang, Yushi Guo, Liting Niu +6
Simultaneous localization and mapping (SLAM) is a critical technology that enables autonomous robots to be aware of their surrounding environment. With the development of deep lear…
One-shot Implicit Animatable Avatars with Model-based Priors
Yangyi Huang, Hongwei Yi, Weiyang Liu +6
Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large…
End-to-End Video Text Spotting with Transformer
Weijia Wu, Yuanqiang Cai, Chunhua Shen +4
Recent video text spotting methods usually require the three-staged pipeline, i.e., detecting text in individual images, recognizing localized text, tracking text streams with post…
A Bilingual, OpenWorld Video Text Dataset and End-to-end Video Text Spotter with Transformer
Weijia Wu, Yuanqiang Cai, Debing Zhang +5
Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data. In this work, we introduce a large-scale, Bilingual, Open World V…
TransAug: Translate as Augmentation for Sentence Embeddings
Jue Wang
While contrastive learning greatly advances the representation of sentence embeddings, it is still limited by the size of the existing sentence datasets. In this paper, we present…
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling
Jue Wang, Haofan Wang, Jincan Deng +2
While large scale pre-training has achieved great achievements in bridging the gap between vision and language, it still faces several challenges. First, the cost for pre-training…