5 citations · 15 across the 13 of their papers we have counts for
13 papers
Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment
Jerry Jiang, Haowen Sun, Denis Gudovskiy +4
Spatial intelligence in vision-language models (VLMs) attracts research interest with the practical demand to reason in the 3D world.Despite promising results, most existing method…
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM
Dongwei Wang, Jinhee Kim, Seokho Han +8
Dynamic runtime latency and memory constraints necessitate flexible large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based o…
ODE(ODE): Shortcutting the Time and the Length in Diffusion and Flow Models for Faster Sampling
Denis Gudovskiy, Wenzhao Zheng, Tomoyuki Okuno +2
Continuous normalizing flows (CNFs) and diffusion models (DMs) generate high-quality data from a noise distribution. However, their sampling process demands multiple iterations to…
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…
Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation
Rongyu Zhang, Yulin Luo, Jiaming Liu +8
The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adve…
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
Anthony Chen, Huanrui Yang, Yulu Gan +7
Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce…
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow
Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata
Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is…
MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer
Jinze Yu, Jiaming Liu, Xiaobao Wei +7
Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline, has achieved promising performance. However, it requires large-scale labeled data and suffers from…
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Stefano Alletto, Shenyang Huang, Vincent Francois-Lavet +2
Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it finds. Should it be the only metric for a g…