8 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
From Imitation to Intuition: Intrinsic Reasoning for Open-Instance Video Classification
Ke Zhang, Xiangchen Zhao, Yunjie Tian +3
Conventional video classification models, acting as effective imitators, excel in scenarios with homogeneous data distributions. However, real-world applications often present an o…
Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation
Ali Abbasi, Shima Imani, Chenyang An +6
With the rapid scaling of neural networks, data storage and communication demands have intensified. Dataset distillation has emerged as a promising solution, condensing information…
One Category One Prompt: Dataset Distillation using Diffusion Models
Ali Abbasi, Ashkan Shahbazi, Hamed Pirsiavash +1
The extensive amounts of data required for training deep neural networks pose significant challenges on storage and transmission fronts. Dataset distillation has emerged as a promi…
GeNIe: Generative Hard Negative Images Through Diffusion
Soroush Abbasi Koohpayegani, Anuj Singh, K L Navaneet +2
Data augmentation is crucial in training deep models, preventing them from overfitting to limited data. Recent advances in generative AI, e.g., diffusion models, have enabled more…
CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization
KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani +1
3D Gaussian Splatting (3DGS) is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods. Howe…