14 citations · 21 across the 20 of their papers we have counts for
6 papers · 1 filter
Sparse Attention for Video Generation Acceleration via Growing Sparsity and Reduced Search
Yun Dai, Yuyun Chen, Jiarui Wen +3
Diffusion Transformers (DiTs) dominate video generation but suffer from high inference cost due to the quadratic complexity of self-attention. Sparse attention mitigates this by sk…
EgoSafe: A First-Person Mobile-Captured Benchmark for Visual Safety Understanding
Yuyun Chen, Tianao Li, TianQuan Feng +4
Reliable visual safety understanding in real-world scenarios demands more than just object recognition; it requires causal reasoning under epistemic uncertainty. While Large Vision…
ProCache: Constraint-Aware Feature Caching with Selective Computation for Diffusion Transformer Acceleration
Fanpu Cao, Yaofo Chen, Zeng You +1
Diffusion Transformers (DiTs) have achieved state-of-the-art performance in generative modeling, yet their high computational cost hinders real-time deployment. While feature cachi…
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Xiang Zhang, Run He, Jiao Chen +5
Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-wor…
Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning
Run He, Di Fang, Yicheng Xu +5
Exemplar-Free Class-Incremental Learning (EFCIL) aims to sequentially learn from distinct categories without retaining exemplars but easily suffers from catastrophic forgetting of…
F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning
Huiping Zhuang, Yuchen Liu, Run He +5
Online Class Incremental Learning (OCIL) aims to train models incrementally, where data arrive in mini-batches, and previous data are not accessible. A major challenge in OCIL is C…