Paper 1:
■書誌情報
“MultiBanana: A Challenging Benchmark for Multi-Reference Text-to-Image Generation”.
Yuta Oshima, Daiki Miyake, Kohsei Matsutani, Yusuke Iwasawa, Masahiro Suzuki, Yutaka Matsuo, Hiroki Furuta.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026).
■概要
Recent text-to-image generation models have acquired the ability of multi-reference generation and editing; the ability to inherit the appearance of subjects from multiple reference images and re-render them under new contexts. However, the existing benchmark datasets often focus on the generation with single or a few reference images, which prevents us from measuring the progress on how model performance advances or pointing out their weaknesses, under different multi-reference conditions. In addition, their task definitions are still vague, typically limited to axes such as “what to edit” or “how many references are given”, and therefore fail to capture the intrinsic difficulty of multi-reference settings. To address this gap, we introduce MultiBanana, which is carefully designed to assesses the edge of model capabilities by widely covering multi-reference-specific problems at scale: (1) varying the number of references, (2) domain mismatch among references (e.g., photo vs. anime), (3) scale mismatch between reference and target scenes, (4) references containing rare concepts (e.g., a red banana), and (5) multilingual textual references for rendering. Our analysis among a variety of text-to-image models reveals their superior performances, typical failure modes, and areas for improvement. MultiBanana will be released as an open benchmark to push the boundaries and establish a standardized basis for fair comparison in multi-reference image generation. Our data and code are available at https://github.com/matsuolab/multibanana
Paper 2:
■書誌情報
“CLIP-like Model as a Foundational Density Ratio Estimator”.
Fumiya Uchiyama*, Rintaro Yanagi, Shohei Taniguchi, Shota Takashiro, Masahiro Suzuki, Hirokatsu Kataoka, Yusuke Iwasawa, Yutaka Matsuo.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026).
■概要
Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weighting, divergence estimation, and likelihood-free inference, but its potential in vision and language models has not been fully explored. Modern vision-language encoders such as CLIP and SigLIP are trained with contrastive objectives that implicitly optimize log density ratios between joint and marginal image–text distributions, which implicitly learn similarity scores proportional to log density ratios. However, prior work has largely focused on their embedding utility, and the density-ratio structure induced by contrastive learning has not been systematically examined or exploited in multimodal applications. To address this gap, we reinterpret CLIP-style models as pretrained and general-purpose density ratio estimators and show that this perspective enables new algorithmic capabilities. We present a unified explanation of how contrastive objectives estimate density ratios and propose two practical applications: Importance Weight Learning and KL divergence estimation Our Importance Weight Learning method requires only a single additional prompt and improves F1 scores by up to 7 points. We further show that CLIP-based density ratios support estimation of KL divergences that quantify how conditioning on an image or text alters the distribution of the other modality. Through qualitative examples and an N-gram analysis of captions, we find that these divergences capture semantic diversity and mode structure in multimodal data. Leveraging this property, we introduce a simple KL-guided data curation method that achieves performance competitive with LAION2B filtering. Our code will be publicly available.
Paper 3:
■書誌情報
“Towards High-resolution and Disentangled Reference-based Sketch Colorization”.
Dingkun Yan, Xinrui Wang, Ru Wang, Zhuoru Li, Jinze Yu, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo.
The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026).
■概要
Sketch colorization models have been widely studied to automate and assist in the creation of animation frames and digital illustrations. However, current methods are still not satisfactory for industrial standard applications in high-resolution synthesis and precise controllability of details. To further enhance the synthesis quality and controllability, we propose an image-referenced sketch colorization method based on the powerful SDXL backbone and leverage sketches as spatial guidance and RGB images as color references. A split cross-attention mechanism is coupled with spatial masks to separately colorize the foreground and background regions to avoid spatial entanglement. A tagger network trained on a massive anime-style image dataset is employed to extract attribution-level information from reference images and integrated into the pipeline to provide precise control signals for synthesis. However, the increased resolution and number of attention layers in the SDXL backbone and precise reference information from the tagger network cause severe entanglement during colorization. We consequently combine a foreground encoder and a background encoder for disentanglement and better synthesis quality. Furthermore, a high-quality annotated and paired sketch colorization dataset is collected for fine-tuning. The proposed method is the first to achieve high resolution high quality sketch colorization with precise control, and obviously outperforms existing methods in quantitative and qualitative validations, as well as user studies in both quality and controllability. Ablation study reveals the influence of each component. Code and dataset will be made publicly available upon paper acceptance.