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20182026
most citedMLPerf Training Benchmark

171 citations · 204 across the 18 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

SCOPE: Subspace Clustering with Online Per-Head Top-K Estimation for Sparse Video Attention

Qi Zhao, Qirui Li, Hanlin Tang +10

Diffusion Transformers (DiTs) incur quadratic self-attention cost over spatiotemporal tokens. Existing training-free sparse attention methods often construct sparse masks from bloc…

cs.CV2026

Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers

Maohua Li, Qirui Li, Yanke Zhou +10

Modern text-to-image diffusion transformers (DiTs) generate images through joint attention, in which text and image tokens interact directly within a single sequence. In large-scal…

cs.CV2026

Rethinking Cross-Layer Information Routing in Diffusion Transformers

Chao Xu, Maohua Li, Qirui Li +9

Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization, attention, conditioning, obj…

cs.CV2019

Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain

Barak Battash, Haim Barad, Hanlin Tang +1

Video understanding usually requires expensive computation that prohibits its deployment, yet videos contain significant spatiotemporal redundancy that can be exploited. In particu…

cs.CV2019

Using Image Priors to Improve Scene Understanding

Brigit Schroeder, Hanlin Tang, Alexandre Alahi

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as…

cs.CV2019

Triplet-Aware Scene Graph Embeddings

Brigit Schroeder, Subarna Tripathi, Hanlin Tang

Scene graphs have become an important form of structured knowledge for tasks such as for image generation, visual relation detection, visual question answering, and image retrieval…