16 citations · 16 across the 8 of their papers we have counts for
8 papers
VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning
Zhong-Yu Li, Ruoyi Du, Juncheng Yan +6
Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models…
Multi-Token Enhancing for Vision Representation Learning
Zhong-Yu Li, Yu-Song Hu, Bo-Wen Yin +1
Vision representation learning, especially self-supervised learning, is pivotal for various vision applications. Ensemble learning has also succeeded in enhancing the performance a…
PR-MIM: Delving Deeper into Partial Reconstruction in Masked Image Modeling
Zhong-Yu Li, Yunheng Li, Deng-Ping Fan +1
Masked image modeling has achieved great success in learning representations but is limited by the huge computational costs. One cost-saving strategy makes the decoder reconstruct…
Towards RAW Object Detection in Diverse Conditions
Zhong-Yu Li, Xin Jin, Boyuan Sun +2
Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might los…
Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation
Yunheng Li, ZhongYu Li, Quansheng Zeng +2
Pre-trained vision-language models, e.g., CLIP, have been successfully applied to zero-shot semantic segmentation. Existing CLIP-based approaches primarily utilize visual features…
A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation
Yunheng Li, Zhongyu Li, Shanghua Gao +3
Effectively modeling discriminative spatio-temporal information is essential for segmenting activities in long action sequences. However, we observe that existing methods are limit…
Enhancing Representations through Heterogeneous Self-Supervised Learning
Zhong-Yu Li, Bo-Wen Yin, Yongxiang Liu +2
Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions. How…
DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
Bowen Yin, Xuying Zhang, Zhongyu Li +3
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) Unlike previous…