12 papers · 1 filter
LoRA Superposition across Pretrained Models
Keigo Watanabe, Hiroshi Kera, Kazuhiko Kawamoto
Low-rank adaptation (LoRA) is usually tied to the pretrained backbone on which it is learned. We ask whether LoRAs trained independently for the same task on different pretrained b…
What Does Prompt Learning Change? -A Natural-Language Concept Analysis of Vision-Language Models
Ryo Kamiya, Hiroshi Kera, Kazuhiko Kawamoto
Prompt learning adapts vision-language models such as CLIP by optimizing continuous prompt vectors, but the learned prompts are difficult to interpret in natural language. We prese…
Zero-Shot Faithful Textual Explanations via Directional-Derivative Influence on Predictions
Toshinori Yamauchi, Hiroshi Kera, Kazuhiko Kawamoto
Zero-shot textual explanations aim to make image classifiers more transparent by probing their internal representations, without relying on task-specific supervision or LVLMs. Howe…
Towards Viewpoint-Robust End-to-End Autonomous Driving with 3D Foundation Model Priors
Hiroki Hashimoto, Hiromichi Goto, Hiroyuki Sugai +2
Robust trajectory planning under camera viewpoint changes is important for scalable end-to-end autonomous driving. However, existing models often depend heavily on the camera viewp…
Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning
Taishu Arashima, Hiroshi Kera, Kazuhiko Kawamoto
Human trajectory prediction plays a crucial role in applications such as autonomous navigation and video surveillance. While recent works have explored the integration of human ske…
Zero-Shot Textual Explanations via Translating Decision-Critical Features
Toshinori Yamauchi, Hiroshi Kera, Kazuhiko Kawamoto
Textual explanations make image classifier decisions transparent by describing the prediction rationale in natural language. Large vision-language models can generate captions but…
Cross-Model Transfer of Task Vectors via Few-Shot Orthogonal Alignment
Kazuhiko Kawamoto, Atsuhiro Endo, Hiroshi Kera
Task arithmetic enables efficient model editing by representing task-specific changes as vectors in parameter space. Task arithmetic typically assumes that the source and target mo…
Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning
Takuma Fukuda, Hiroshi Kera, Kazuhiko Kawamoto
We propose Adapter Merging with Centroid Prototype Mapping (ACMap), an exemplar-free framework for class-incremental learning (CIL) that addresses both catastrophic forgetting and…
Explaining Object Detectors via Collective Contribution of Pixels
Toshinori Yamauchi, Hiroshi Kera, Kazuhiko Kawamoto
Visual explanations for object detectors are crucial for enhancing their reliability. Object detectors identify and localize instances by assessing multiple visual features collect…
Low-Quality Image Detection by Hierarchical VAE
Tomoyasu Nanaumi, Kazuhiko Kawamoto, Hiroshi Kera
To make an employee roster, photo album, or training dataset of generative models, one needs to collect high-quality images while dismissing low-quality ones. This study addresses…
Matching Semantically Similar Non-Identical Objects
Yusuke Marumo, Kazuhiko Kawamoto, Satomi Tanaka +2
Not identical but similar objects are ubiquitous in our world, ranging from four-legged animals such as dogs and cats to cars of different models and flowers of various colors. Thi…
Identifying Important Group of Pixels using Interactions
Kosuke Sumiyasu, Kazuhiko Kawamoto, Hiroshi Kera
To better understand the behavior of image classifiers, it is useful to visualize the contribution of individual pixels to the model prediction. In this study, we propose a method,…