3 citations · 6 across the 12 of their papers we have counts for
8 papers · 1 filter
ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog
Lingao Xiao, Yalun Dai, Yangyu Huang +17
Despite growing automation, turning a paper into a coherent poster, talk video, and blog piece often remains a labor-intensive last mile. Recent systems increasingly generate multi…
PEACE: Empowering Geologic Map Holistic Understanding with MLLMs
Yangyu Huang, Tianyi Gao, Haoran Xu +8
Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface. These maps are indispens…
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians
Shuyi Jiang, Qihao Zhao, Hossein Rahmani +3
Recently, with the development of Neural Radiance Fields and Gaussian Splatting, 3D reconstruction techniques have achieved remarkably high fidelity. However, the latent representa…
LTRL: Boosting Long-tail Recognition via Reflective Learning
Qihao Zhao, Yalun Dai, Shen Lin +3
In real-world scenarios, where knowledge distributions exhibit long-tail. Humans manage to master knowledge uniformly across imbalanced distributions, a feat attributed to their di…
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content
Qihao Zhao, Yalun Dai, Hao Li +3
Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper, w…
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
Qihao Zhao, Chen Jiang, Wei Hu +2
Recently, multi-expert methods have led to significant improvements in long-tail recognition (LTR). We summarize two aspects that need further enhancement to contribute to LTR boos…
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
Qihao Zhao, Yangyu Huang, Wei Hu +2
The recently proposed data augmentation TransMix employs attention labels to help visual transformers (ViT) achieve better robustness and performance. However, TransMix is deficien…
P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions
Wei Hu, QiHao Zhao, Yangyu Huang +1
Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability. In this…