Real-world fine-grained visual retrieval often requires discovering a rare concept from large unlabeled collections with minimal supervision. This is especially critical in biodiversity monitoring, ecological studies, and long-tailed visual domains, where the target may represent only a tiny fraction of the data, creating highly imbalanced binary problems. Interactive retrieval with relevance feedback offers a practical solution: starting from a small query, the system selects candidates for binary user annotation and iteratively refines a lightweight classifier. While Active Learning (AL) is commonly used to guide selection, conventional AL assumes symmetric class priors and large annotation budgets, limiting effectiveness in imbalanced, low-budget, low-latency settings. We introduce Positive-First Most Ambiguous (PF-MA), a simple yet effective AL criterion that explicitly addresses the class imbalance asymmetry: it prioritizes near-boundary samples while favoring likely positives, enabling rapid discovery of subtle visual categories while maintaining informativeness. Unlike standard methods that oversample negatives, PF-MA consistently returns small batches with a high proportion of relevant samples, improving early retrieval and user satisfaction. To capture retrieval diversity, we also propose a class coverage metric that measures how well selected positives span the visual variability of the target class. Experiments on long-tailed datasets, including fine-grained botanical data, demonstrate that PF-MA consistently outperforms strong baselines in both coverage and classifier performance, across varying class sizes and descriptors. Our results highlight that aligning AL with the asymmetric and user-centric objectives of interactive fine-grained retrieval enables simple yet powerful solutions for retrieving rare and visually subtle categories in realistic human-in-the-loop settings.
Recent advances in large-scale visual representation learning have significantly improved performance in plant species and plant disease recognition tasks. However, state-of-the-art models, often based on high-capacity vision transformers or multimodal foundation models, remain computationally expensive and difficult to deploy in resource-constrained environments such as mobile or edge devices. This limitation hinders the scalability of automated biodiversity monitoring and precision agriculture systems, where efficiency is as critical as accuracy. In this work, we investigate knowledge distillation as an effective approach to transfer the representational capacity of large pretrained models into smaller, more efficient architectures. We focus on plant species and disease recognition, and conduct an extensive empirical study on two challenging benchmarks: Pl@ntNet300K-v2 and Deep-Plant-Disease. We evaluate four representative architectures, including two ConvNeXt models and two vision transformers, under multiple training regimes: from-scratch training and pretrained initialization, each with and without distillation. In total, we train and evaluate 70 models. Our results show that knowledge distillation consistently improves performance across tasks and architectures. Distilled models are able to match the performance of significantly larger models while maintaining substantially lower computational cost. These findings demonstrate the potential of knowledge distillation techniques to enable efficient and scalable deployment of plant recognition systems in real-world environmental applications.
Automated plant recognition plays a crucial role in biodiversity monitoring and conservation, yet current approaches rely heavily on supervised learning, which is limited by the availability of expert-labeled data. Self-supervised learning (SSL) offers a scalable alternative, but existing methods and training protocols are largely designed for coarse-grained visual tasks and may not transfer well to fine-grained domains such as plant species recognition. In this work, we investigate SSL for plant image representation learning. We show that commonly used augmentations in SSL pipelines - such as Gaussian blur, grayscale conversion, and solarization - are detrimental in the context of plant images, as they remove subtle discriminative cues essential for fine-grained recognition. We instead identify alternative transformations, including affine and posterization, that are better suited to this domain. We further demonstrate that training SimDINOv2 on the iNaturalist 2021 Plantae subset yields significantly stronger representations than training on ImageNet-1K, highlighting the importance of domain-specific data for SSL. Our findings are consistent across both ViT-Base and ViT-Large architectures. Moreover, our models achieve competitive performance and sometimes outperform strong supervised baselines Pl@ntCLEF and BioCLIP on downstream plant recognition tasks in few-shot settings. Overall, our results highlight the critical importance of domain-adapted augmentation strategies and dataset selection in self-supervised learning, and provide practical guidelines for building scalable models for biodiversity monitoring.
Although the gut microbiome is associated with cancer development and progression, little is known about the effects of the gut microbiome landscape and the efficacy of immune checkpoint inhibitors (ICI) across cancer types. We investigated the association between the microbiome, clinical features, and ICI efficacy across cancer types in a large nationwide screening project for solid tumors. Among 2,180 patients with advanced solid tumors enrolled in the SCRUM-Japan MONSTAR-SCREEN between October 2019 and September 2021, in the chemotherapy-naïve cohort (n = 817), a high prevalence of oral bacteria was observed in patients using proton pump inhibitors (PPI) and those with upper gastrointestinal cancers, particularly postoperative patients with gastric or pancreatic cancer. Among patients treated with ICIs (n = 333), a high abundance of sequence variants in the gut microbiome was not significantly associated with ICI efficacy across cancer types (HR = 0.94; 95% confidence interval, 0.73-1.21). However, high oral bacteria in feces significantly correlated with a shorter progression-free survival compared with low oral bacteria (median, 4.34 vs. 6.97 months; HR = 1.38; 95% confidence interval, 1.07-1.78). Notably, in patients using PPIs, a higher proportion of oral bacteria influenced progression-free survival outcomes of ICI treatment (median, 3.15 vs. 2.04 months; P = 0.08), unlike in PPI nonusers (median, 7.13 vs. 5.55 months; P = 0.74). This study of the gut microbiome has unveiled significant insights into its landscape and potential impact on ICI efficacy. It highlights that the abundance of oral bacteria in feces may play a critical role in diminishing ICI efficacy among patients using PPIs. SIGNIFICANCE:As part of the MONSTAR-SCREEN, a prospective nationwide project for patients with solid tumors, we found that although gut microbiome diversity does not consistently predict ICI efficacy across cancer types, a high level of oral bacteria in the gut is linked to reduced ICI effectiveness, especially in patients using PPIs. These findings highlight the potential clinical impact of microbiome variations on cancer treatment outcomes.
