1 citations · 2 across the 15 of their papers we have counts for
21 papers
WovenAnchor Matcher: Specialized Intra- and Inter-Image Context Modeling for Feature Matching
Zhiyang Li, Ruijiang Jin, Thibaut Klenke +2
Semi-dense local feature matching commonly aggregates contextual information before coarse-to-fine correspondence estimation. However, intra-image and inter-image aggregation serve…
LongEgoRefer: A Benchmark for Long-Form Egocentric Video Referring Expression Comprehension
Shunya Kato, Taiki Miyanishi, Shuhei Kurita +3
Egocentric videos capture rich and diverse human-object interactions and have emerged as a fundamental resource for understanding human activities related to objects. In this conte…
BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment
Risa Shinoda, Kaede Shiohara, Nakamasa Inoue +3
Understanding animal species from multimodal data poses an emerging challenge at the intersection of computer vision and ecology. While recent biological models, such as BioCLIP, h…
PhysQuantAgent: An Inference Pipeline of Mass Estimation for Vision-Language Models
Hisayuki Yokomizo, Taiki Miyanishi, Yan Gang +3
Vision-Language Models (VLMs) are increasingly applied to robotic perception and manipulation, yet their ability to infer physical properties required for manipulation remains limi…
Diagnosing Long-Video Quantitative Reasoning in Multimodal LLMs via Enumeration and Counting
Fumihiko Tsuchiya, Taiki Miyanishi, Shunsuke Yasuki +5
Final-answer video QA can show whether a model predicts the right number, but not which instances it counted, when the supporting evidence occurs, or why it failed. We diagnose lon…
Autoregressive Direct Preference Optimization
Masanari Oi, Mahiro Ukai, Masahiro Kaneko +2
Direct preference optimization (DPO) has emerged as a promising approach for aligning large language models (LLMs) with human preferences. However, the widespread reliance on the r…
From Correspondence to Actions: Human-Like Multi-Image Spatial Reasoning in Multi-modal Large Language Models
Masanari Oi, Koki Maeda, Ryuto Koike +3
While multimodal large language models (MLLMs) have made substantial progress in single-image spatial reasoning, multi-image spatial reasoning, which requires integration of inform…
DISCODE: Distribution-Aware Score Decoder for Robust Automatic Evaluation of Image Captioning
Nakamasa Inoue, Kanoko Goto, Masanari Oi +4
Large vision-language models (LVLMs) have shown impressive performance across a broad range of multimodal tasks. However, robust image caption evaluation using LVLMs remains challe…
DF-Mamba: Deformable State Space Modeling for 3D Hand Pose Estimation in Interactions
Yifan Zhou, Takehiko Ohkawa, Guwenxiao Zhou +4
Modeling daily hand interactions often struggles with severe occlusions, such as when two hands overlap, which highlights the need for robust feature learning in 3D hand pose estim…
STATUS Bench: A Rigorous Benchmark for Evaluating Object State Understanding in Vision-Language Models
Mahiro Ukai, Shuhei Kurita, Nakamasa Inoue
Object state recognition aims to identify the specific condition of objects, such as their positional states (e.g., open or closed) and functional states (e.g., on or off). While r…
Referring Expression Comprehension for Small Objects
Kanoko Goto, Takumi Hirose, Mahiro Ukai +2
Referring expression comprehension (REC) aims to localize the target object described by a natural language expression. Recent advances in vision-language learning have led to sign…
GeoProg3D: Compositional Visual Reasoning for City-Scale 3D Language Fields
Shunsuke Yasuki, Taiki Miyanishi, Nakamasa Inoue +5
The advancement of 3D language fields has enabled intuitive interactions with 3D scenes via natural language. However, existing approaches are typically limited to small-scale envi…
Masked Gated Linear Unit
Yukito Tajima, Nakamasa Inoue, Yusuke Sekikawa +2
Gated Linear Units (GLUs) have become essential components in the feed-forward networks of state-of-the-art Large Language Models (LLMs). However, they require twice as many memory…
Free Random Projection for In-Context Reinforcement Learning
Tomohiro Hayase, Benoît Collins, Nakamasa Inoue
Hierarchical inductive biases are hypothesized to promote generalizable policies in reinforcement learning, as demonstrated by explicit hyperbolic latent representations and archit…
Rectified Lagrangian for Out-of-Distribution Detection in Modern Hopfield Networks
Ryo Moriai, Nakamasa Inoue, Masayuki Tanaka +3
Modern Hopfield networks (MHNs) have recently gained significant attention in the field of artificial intelligence because they can store and retrieve a large set of patterns with…
Multi-Point Positional Insertion Tuning for Small Object Detection
Kanoko Goto, Takumi Karasawa, Takumi Hirose +2
Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object dete…
Multi-modal, Multi-task, Multi-criteria Automatic Evaluation with Vision Language Models
Masanari Ohi, Masahiro Kaneko, Naoaki Okazaki +1
Vision-language models (VLMs) have shown impressive abilities across a range of multi-modal tasks. However, existing metrics for evaluating the quality of text generated by VLMs ty…
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis
Yuto Nishimura, Takumi Hirose, Masanari Ohi +2
Recently, Text-to-speech (TTS) models based on large language models (LLMs) that translate natural language text into sequences of discrete audio tokens have gained great research…
AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering
Mahiro Ukai, Shuhei Kurita, Atsushi Hashimoto +2
Visual question answering aims to provide responses to natural language questions given visual input. Recently, visual programmatic models (VPMs), which generate executable program…
CityNav: A Large-Scale Dataset for Real-World Aerial Navigation
Jungdae Lee, Taiki Miyanishi, Shuhei Kurita +4
Vision-and-language navigation (VLN) aims to develop agents capable of navigating in realistic environments. While recent cross-modal training approaches have significantly improve…
CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data
Taiki Miyanishi, Fumiya Kitamori, Shuhei Kurita +3
City-scale 3D point cloud is a promising way to express detailed and complicated outdoor structures. It encompasses both the appearance and geometry features of segmented city comp…