# Research Radar ยท 2026-05-24 (Sun) _Auto-generated daily arXiv monitoring dashboard_ ## ๐Ÿ”ฌ Fine-Grained CV (5 papers) ### 1. Training-Free Fine-Grained Semantic Segmentations in Low Data Regimes: A FungiTastic Baseline - **arXiv:** `2605.22492` | [Abstract](https://arxiv.org/abs/2605.22492v1) | [PDF](https://arxiv.org/pdf/2605.22492v1) - **Authors:** Sebastian Cavada, Francesco Pelosin, Lapo Faggi - **Published:** 2026-05-21 Fine-grained semantic segmentation requires both precise localization and discrimination between visually similar classes. In FungiTastic, this problem is further complicated by a long-tailed distribution and strong variation in image acquisition conditions. We propose a training-free two-stage fram --- ### 2. Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light - **arXiv:** `2605.22455` | [Abstract](https://arxiv.org/abs/2605.22455v1) | [PDF](https://arxiv.org/pdf/2605.22455v1) - **Authors:** Valeria Pais, Malena Mendilaharzu, Daniele Faccio, Luis Oala, Christoph Clausen et al. - **Published:** 2026-05-21 Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven: long-tailed or unbalanced distributions hinder generalization, and the low number of samples in low density regions makes it hard to run evaluatio --- ### 3. FruitEnsemble: MLLM-Guided Arbitration for Heterogeneous ensemble in Fine-Grained Fruit Recognition - **arXiv:** `2605.20892` | [Abstract](https://arxiv.org/abs/2605.20892v1) | [PDF](https://arxiv.org/pdf/2605.20892v1) - **Authors:** Enhui Yu, Junhui Li, Ruitong Lu, Jialu Li, Youshan Zhang - **Published:** 2026-05-20 Fine-grained fruit classification is a critical yet challenging task in agricultural computer vision, primarily hindered by a severe shortage of high-quality datasets and the high visual similarity between classes. To address these challenges, we first constructed a comprehensive dataset comprising --- ### 4. AttriStory: Fine-grained Attribute Realization for Visual Storytelling with Diffusion Models - **arXiv:** `2605.20777` | [Abstract](https://arxiv.org/abs/2605.20777v1) | [PDF](https://arxiv.org/pdf/2605.20777v1) - **Authors:** Manogna Sreenivas, Rohit Kumar, Soma Biswas - **Published:** 2026-05-20 Visual storytelling with diffusion models has made impressive strides in maintaining character consistency across narrative scenes. However, a critical gap remains: while these methods ensure a character remains consistent across scenes, they provide no systematic method to ensure if fine-grained at --- ### 5. Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation - **arXiv:** `2605.19986` | [Abstract](https://arxiv.org/abs/2605.19986v1) | [PDF](https://arxiv.org/pdf/2605.19986v1) - **Authors:** He-Yang Xu, Pengyuan Zhang, Zongyuan Ge, Xiaoshuai Hao, Serge Belongie et al. - **Published:** 2026-05-19 Fine-grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute grounding, high-fidelity spatial perception, and constraint-respecting motor execution. However, current embodied AI benchmarks collapse these capacitie --- ## ๐Ÿ“Š CTR / Ranking / IR (0 papers) _No new papers._ ## ๐Ÿค– Agent / Auto Research (0 papers) _No new papers._ _Report generated at 2026-05-24 14:00:54 CST_