Yuhai Deng

Yuhai Deng, Huimin She, Wei Shen et al.

Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-quality large-scale triplet datasets with stylized ground truth forces existing methods to rely on self-supervised or proxy objectives, which limits model capability. To mitigate this gap, we design a data construction pipeline to build TST100K, a large-scale dataset of 100,000 content-reference-stylized triplets. At the core of this pipeline, we train a tone style scorer to ensure strict stylistic consistency for each triplet. In addition, existing methods typically extract content and reference features independently and then fuse them in a decoder, which may cause semantic loss and lead to inappropriate color transfer and degraded visual aesthetics. Instead, we propose ICTone, a diffusion-based framework that performs tone transfer in an in-context manner by jointly conditioning on both images, leveraging the semantic priors of generative models for semantic-aware transfer. Reward feedback learning using the tone style scorer is further incorporated to improve stylistic fidelity and visual quality. Experiments demonstrate the effectiveness of TST100K, and ICTone achieves state-of-the-art performance on both quantitative metrics and human evaluations.

Shichao Kan, Yuhai Deng, Jiale Fu et al.

Retrieval-augmented generation (RAG) with large language models (LLMs) plays a crucial role in question answering, as LLMs possess limited knowledge and are not updated with continuously growing information. Most recent work on RAG has focused primarily on text-based or large-image retrieval, which constrains the broader application of RAG models. We recognize that object-level retrieval is essential for addressing questions that extend beyond image content. To tackle this issue, we propose a task of object retrieval for visual question answering with outside knowledge (OR-OK-VQA), aimed to extend image-based content understanding in conjunction with LLMs. A key challenge in this task is retrieving diverse objects-related images that contribute to answering the questions. To enable accurate and robust general object retrieval, it is necessary to learn embeddings for local objects. This paper introduces a novel unsupervised deep feature embedding technique called multi-scale group collaborative embedding learning (MS-GCEL), developed to learn embeddings for long-tailed objects at different scales. Additionally, we establish an OK-VQA evaluation benchmark using images from the BelgaLogos, Visual Genome, and LVIS datasets. Prior to the OK-VQA evaluation, we construct a benchmark of challenges utilizing objects extracted from the COCO 2017 and VOC 2007 datasets to support the training and evaluation of general object retrieval models. Our evaluations on both general object retrieval and OK-VQA demonstrate the effectiveness of the proposed approach. The code and dataset will be publicly released for future research.