DINOv3 Visual Representations for Blueberry Perception Toward Robotic Harvesting

• URL: http://arxiv.org/abs/2603.02419v2
• Date: Sat, 07 Mar 2026 17:43:17 GMT
• Title: DINOv3 Visual Representations for Blueberry Perception Toward Robotic Harvesting
• Abstract summary: This work evaluates DINOv3 as a frozen backbone for blueberry robotic harvesting-related visual tasks.<n>It includes fruit and bruise segmentation, as well as fruit and cluster detection.<n>Overall, DINOv3 is best viewed not as an end-to-end task model, but as a semantic backbone.
• Score: 10.977118601343797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision Foundation Models trained via large-scale self-supervised learning have demonstrated strong generalization in visual perception; however, their practical role and performance limits in agricultural settings remain insufficiently understood. This work evaluates DINOv3 as a frozen backbone for blueberry robotic harvesting-related visual tasks, including fruit and bruise segmentation, as well as fruit and cluster detection. Under a unified protocol with lightweight decoders, segmentation benefits consistently from stable patch-level representations and scales with backbone size. In contrast, detection is constrained by target scale variation, patch discretization, and localization compatibility. The failure of cluster detection highlights limitations in modeling relational targets defined by spatial aggregation. Overall, DINOv3 is best viewed not as an end-to-end task model, but as a semantic backbone whose effectiveness depends on downstream spatial modeling aligned with fruit-scale and aggregation structures, providing guidance for blueberry robotic harvesting. Code and dataset will be available upon acceptance.

Related papers

• Promptable Concept Segmentation from Above: Evaluating SAM 3's Zero-Shot and One-Shot Capabilities in Remote Sensing [0.42970700836450487]
  We present a comprehensive, multi-task empirical evaluation across remote sensing scene classification, object detection, and instance segmentation.<n>Our results demonstrate that SAM 3 avoids the overfitting commonly seen in legacy domain-adapted models.<n>It remains fundamentally constrained by sub-pixel resolution limits and overhead semantic blind spots.
  arXiv  Detail & Related papers  (2026-07-10T16:33:05Z)
• ViViD-5K: Vineyard vision dataset for field-based berry detection and segmentation and grape cluster closure estimation [7.764774369757902]
  Cluster closure is a key trait in vineyard management, impacting disease risk.<n>Traditional visual scoring methods are labor-intensive, subjective, and lack temporal resolution.<n>We present ViViD-5k, a large-scale in-field Vineyard Vision dataset with over 648,000 berry centroids and cluster segmentation masks spanning 13 grape varieties.<n>We introduce GrapeSAM, a two-stage visual pipeline that combines point-based berry localization with prompt-based segmentation using Segment Anything, followed by transformer-based cluster segmentation.
  arXiv  Detail & Related papers  (2026-05-23T02:30:02Z)
• Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization [65.37179698521766]
  Vision-Language-Action (VLA) models show strong promise for generalist robot control.<n>It remains unclear whether -- and under what conditions -- the standard "scale data" recipe translates to robotics.<n>We present a systematic, controlled study of VLA scaling that revisits core training choices for pretraining across diverse robots.
  arXiv  Detail & Related papers  (2026-02-10T12:25:43Z)
• Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges [1.9116784879310027]
  Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory.<n>This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation.<n>DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware.
  arXiv  Detail & Related papers  (2025-12-30T12:10:43Z)
• TSE-Net: Semi-supervised Monocular Height Estimation from Single Remote Sensing Images [10.375329759512702]
  TSE-Net is a self-training pipeline for semi-supervised monocular height estimation.<n>The pipeline integrates teacher, student, and exam networks.<n>We evaluate the proposed pipeline on three datasets spanning different resolutions and imaging modalities.
  arXiv  Detail & Related papers  (2025-11-17T16:22:38Z)
• Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy [73.75271615101754]
  We present Dita, a scalable framework that leverages Transformer architectures to directly denoise continuous action sequences.<n>Dita employs in-context conditioning -- enabling fine-grained alignment between denoised actions and raw visual tokens from historical observations.<n>Dita effectively integrates cross-embodiment datasets across diverse camera perspectives, observation scenes, tasks, and action spaces.
  arXiv  Detail & Related papers  (2025-03-25T15:19:56Z)
• Diffusion Transformer Policy [48.50988753948537]
  We propose a large multi-modal diffusion transformer, dubbed as Diffusion Transformer Policy, to model continuous end-effector actions.<n>By leveraging the scaling capability of transformers, the proposed approach can effectively model continuous end-effector actions across large diverse robot datasets.
  arXiv  Detail & Related papers  (2024-10-21T12:43:54Z)
• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping [0.24578723416255746]
  Feature selection assumes a pivotal role in enhancing model interpretability. The accuracy gained from aggregating decision trees comes at the expense of interpretability. The study introduces novel methods to construct feature graphs from unsupervised random forests.
  arXiv  Detail & Related papers  (2024-04-27T12:47:37Z)
• Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties [0.0]
  This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2D cluster images. The correlation between human-identified berries and SAM predictions was very strong.
  arXiv  Detail & Related papers  (2024-03-19T17:37:18Z)
• A Lightweight Clustering Framework for Unsupervised Semantic Segmentation [28.907274978550493]
  Unsupervised semantic segmentation aims to categorize each pixel in an image into a corresponding class without the use of annotated data. We propose a lightweight clustering framework for unsupervised semantic segmentation. Our framework achieves state-of-the-art results on PASCAL VOC and MS COCO datasets.
  arXiv  Detail & Related papers  (2023-11-30T15:33:42Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Semi-supervised 3D Object Detection with Proficient Teachers [114.54835359657707]
  Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples. Pseudo-Labeling methodology is commonly used for SSL frameworks, however, the low-quality predictions from the teacher model have seriously limited its performance. We propose a new Pseudo-Labeling framework for semi-supervised 3D object detection, by enhancing the teacher model to a proficient one with several necessary designs.
  arXiv  Detail & Related papers  (2022-07-26T04:54:03Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.