5 citations · 7 across the 10 of their papers we have counts for
10 papers
Towards Vision-Free CIR: Attribute-Augmented Scoring and LLM-Based Reranking for Zero-Shot Composed Image Retrieval
Ryotaro Shimada, Yu-Chieh Lin, Yuji Nozawa +3
Recent work has shown that "Vision-Free'' approaches (representing images as text) can be effective for standard image retrieval tasks. However, it remains unclear whether this par…
CIRCLED: A Multi-turn CIR Dataset with Consistent Dialogues across Domains
Tomohisa Takeda, Yu-Chieh Lin, Yuji Nozawa +3
Existing Multi-Turn Composed Image Retrieval (MTCIR) datasets lack dialogue-historyconsistency and are restricted to the fashion domain. To address these limitations, we construct…
Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval
Yuji Nozawa, Yu-Chieh Lin, Kazumoto Nakamura +1
The goal of this paper is to enhance pretrained Vision Transformer (ViT) models for focus-oriented image retrieval with visual prompting. In real-world image retrieval scenarios, b…
Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering
Kazumoto Nakamura, Yuji Nozawa, Yu-Chieh Lin +2
The goal of this paper is to improve the performance of pretrained Vision Transformer (ViT) models, particularly DINOv2, in image clustering task without requiring re-training or f…
Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models
Kengo Nakata, Daisuke Miyashita, Youyang Ng +2
In this paper, we rethink sparse lexical representations for image retrieval. By utilizing multi-modal large language models (M-LLMs) that support visual prompting, we can extract…
Revisiting Relevance Feedback for CLIP-based Interactive Image Retrieval
Ryoya Nara, Yu-Chieh Lin, Yuji Nozawa +4
Many image retrieval studies use metric learning to train an image encoder. However, metric learning cannot handle differences in users' preferences, and requires data to train an…
SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool
Youyang Ng, Daisuke Miyashita, Yasuto Hoshi +4
Large Language Model (LLM) based Generative AI systems have seen significant progress in recent years. Integrating a knowledge retrieval architecture allows for seamless integratio…
RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
Yasuto Hoshi, Daisuke Miyashita, Youyang Ng +4
Retrieval-augmented large language models (R-LLMs) combine pre-trained large language models (LLMs) with information retrieval systems to improve the accuracy of factual question-a…
Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?
Yasuto Hoshi, Daisuke Miyashita, Yasuhiro Morioka +3
Neural document retrievers, including dense passage retrieval (DPR), have outperformed classical lexical-matching retrievers, such as BM25, when fine-tuned and tested on specific q…
Revisiting a kNN-based Image Classification System with High-capacity Storage
Kengo Nakata, Youyang Ng, Daisuke Miyashita +3
In existing image classification systems that use deep neural networks, the knowledge needed for image classification is implicitly stored in model parameters. If users want to upd…