1 citations · 2 across the 8 of their papers we have counts for
8 papers
Read or Ignore? A Unified Benchmark for Typographic-Attack Robustness and Text Recognition in Vision-Language Models
Futa Waseda, Shojiro Yamabe, Daiki Shiono +2
Large vision-language models (LVLMs) are vulnerable to typographic attacks, where misleading text inserted into an image can override visual understanding. However, existing evalua…
Hierarchical Reasoning with Vision-Language Models for Incident Reports from Dashcam Videos
Shingo Yokoi, Kento Sasaki, Yu Yamaguchi
Recent advances in end-to-end (E2E) autonomous driving have been enabled by training on diverse large-scale driving datasets, yet autonomous driving models still struggle in out-of…
Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness
Tsubasa Takahashi, Shojiro Yamabe, Futa Waseda +1
Differential Attention (DA) has been proposed as a refinement to standard attention, suppressing redundant or noisy context through a subtractive structure and thereby reducing con…
STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes
Keishi Ishihara, Kento Sasaki, Tsubasa Takahashi +2
Vision-Language Models (VLMs) have been applied to autonomous driving to support decision-making in complex real-world scenarios. However, their training on static, web-sourced ima…
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang +7
Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing ben…
One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression
Keita Miwa, Kento Sasaki, Hidehisa Arai +2
Current image tokenization methods require a large number of tokens to capture the information contained within images. Although the amount of information varies across images, mos…
CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving
Hidehisa Arai, Keita Miwa, Kento Sasaki +4
Autonomous driving, particularly navigating complex and unanticipated scenarios, demands sophisticated reasoning and planning capabilities. While Multi-modal Large Language Models…
Heron-Bench: A Benchmark for Evaluating Vision Language Models in Japanese
Yuichi Inoue, Kento Sasaki, Yuma Ochi +3
Vision Language Models (VLMs) have undergone a rapid evolution, giving rise to significant advancements in the realm of multimodal understanding tasks. However, the majority of the…