3 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
DFM: Interpolant-free Dual Flow Matching
Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata
Continuous normalizing flows (CNFs) can model data distributions with expressive infinite-length architectures. But this modeling involves computationally expensive process of solv…
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
Yuan Zhang, Chun-Kai Fan, Junpeng Ma +8
In vision-language models (VLMs), visual tokens usually bear a significant amount of computational overhead despite sparsity of information in them when compared to text tokens. To…
Fisher-aware Quantization for DETR Detectors with Critical-category Objectives
Huanrui Yang, Yafeng Huang, Zhen Dong +6
The impact of quantization on the overall performance of deep learning models is a well-studied problem. However, understanding and mitigating its effects on a more fine-grained le…
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding
Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata
Normalizing flow-based generative models have been widely used in applications where the exact density estimation is of major importance. Recent research proposes numerous methods…
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness
Rongyu Zhang, Zefan Cai, Huanrui Yang +9
Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks. However, the conventional finetuning process with randomly sampled data point…