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Tomoyuki Okuno

7 accepted papers

2026

Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment

CVPR 2026

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 methods follow the conventional 2D pipeline in VLMs and use pixel-aligned representations for the vision modality. However, c

Cited by 0SourceScholar
2025

SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference

ICML 2025poster

In vision-language models (VLMs), visual tokens usually consume a significant amount of computational overhead, despite their sparser information density compared to text tokens. To address this, most existing methods learn a network to prune redundant visual tokens and require additional training d…

2024

ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding

UAI 2024poster

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 to improve their expressivity. However, conditioning on a context is largely overlooked area in the bijective flow rese…

2024

Efficient Deweahter Mixture-of-Experts with Uncertainty-Aware Feature-Wise Linear Modulation

AAAI 2024technical

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 adverse weather effects. However, the conventional MoE architecture with parallel Feed Forward Network (FFN) experts leads t…

Cited by 21SourcePDFScholar
2024

Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting

ICML 2024poster

Uncertainty estimation is crucial for deep learning models to detect out-of-distribution (OOD) inputs. However, the naive deep learning classifiers produce uncalibrated uncertainty for OOD data. Improving the uncertainty estimation typically requires external data for OOD-aware training or considera…

Cited by 0SourcePDFScholar
2023

Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow

UAI 2023poster

Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is not robust in practical data setups with distributional shifts and out-of-distribution (OOD) classes. As a result, the…

2022

MTTrans: Cross-Domain Object Detection with Mean Teacher Transformer

ECCV 2022poster

"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 domain shift, especially when no labeled data is available in the target domain. To solve this problem, we propose an e…