arXiv Machine Learning
Jul 21
Chebyshev Manifold Adaptation
arXiv:2607. 17377v1 Announce Type: new Abstract: The paper presents a new parameter-efficient adaptation method called ChebyMA (Chebyshev Manifold Adaptation).
Computer vision
Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.
arXiv AI
Jul 21
DepthART: Scaling Foundation Monocular Depth to Tiny Models
arXiv:2607. 17099v1 Announce Type: cross Abstract: Recent geometric foundation models (e.
arXiv AI
Jul 21
DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
arXiv:2607. 16203v1 Announce Type: cross Abstract: Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and layout information.
arXiv Machine Learning
Jul 21
SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition
arXiv:2607. 16293v1 Announce Type: cross Abstract: Noisy-label learning in small-scale medical image classification is challenging and hinders the superiority of deep neural networks.
arXiv AI
Jul 21
Seg2Grasp: A Robust Modular Suction Grasping in Bin Picking
arXiv:2607. 17757v1 Announce Type: cross Abstract: Current bin picking methods that rely heavily on end-to-end learning often falter when confronted with unfamiliar or complex objects in unstructured environments.
arXiv AI
Jul 21
IoUCert: Robustness Verification for Anchor-based Object Detectors
arXiv:2603. 03043v3 Announce Type: replace-cross Abstract: While formal robustness verification has seen significant success in image classification, scaling these guarantees to object detection remains notoriously difficult due to complex non-linear coordinate transformations and Intersection-over-Union (IoU) metrics.
arXiv AI
Jul 21
Medical Imaging Fusing Vision Transformer: Laryngeal Cancer Screening with Explanation
arXiv:2607. 17789v1 Announce Type: cross Abstract: Early and timely screening of laryngeal cancer is crucial for improving clinical outcomes.
arXiv Machine Learning
Jul 21
A Benchmark of Generative Methods for Zero-Shot Environmental Sound Classification
arXiv:2412. 03771v4 Announce Type: replace-cross Abstract: Zero-shot learning enables models to generalise to unseen classes using semantic information, bridging the gap between training classes and previously unseen test classes.
arXiv Machine Learning
Jul 21
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
arXiv:2411. 10921v2 Announce Type: replace Abstract: Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems.
arXiv Machine Learning
Jul 21
Feature-Guided Diffusion for Non-Differentiable Inverse Rendering
arXiv:2607. 17411v1 Announce Type: cross Abstract: Inverse rendering is traditionally solved via differentiable renderers and gradient descent, which requires substantial problem-specific engineering and is prone to getting stuck in local minima due to ambiguities.
arXiv Machine Learning
Jul 21
RegionFM: Interpretable Region-Based Brain MRI Classification Using Foundation Model Embeddings
arXiv:2607. 16325v1 Announce Type: cross Abstract: Foundation models provide powerful representations for brain MRI analysis, but their predictions remain difficult to interpret in anatomically meaningful terms.
Hugging Face Trending Papers
Jul 21
Norm or Direction? Decoding Vision Mambas for High-Resolution Vision
Vision Mamba models replace quadratic self-attention with linear complexity selective state space models (SSMs), emerging as efficient visual backbones. However, MambaOut demonstrates that a Gated CNN block can match or exceed VMamba on image classification, questioning the necessity of SSMs for vision.
Hugging Face Trending Papers
Jul 20
AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling
Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation.
Hugging Face Trending Papers
Jul 20
SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens
Foundation models such as Segment Anything Model 2 (SAM2) have transformed natural-image and video segmentation, and recent work has begun adapting them to medical imaging. These adaptations, however, are largely general-purpose models that treat MRI as one modality among many; large-scale, MRI-specific modelling and benchmarking remain limited, even though MRI's low soft-tissue contrast leaves many boundaries effectively invisible on individual slices.
Hugging Face Trending Papers
Jul 20
Toward Optimal Adenovirus Detection Using YOLO26
This study systematically benchmarks different data augmentation setups across YOLO26 model size variants to determine the most effective setup for adenovirus detection in TEM images. The benchmarked setups include NAS, GAS, GMAS and DAS, all evaluated under identical training conditions.
Hugging Face Trending Papers
Jul 20
ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video
Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment. A holistic and efficient multimodal model capable of reconstructing this 4D representation is therefore highly desirable.
Hugging Face Trending Papers
Jul 20
Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption.
Hugging Face Trending Papers
Jul 20
CDIS: Cross-Dimensional Class-Agnostic 3D Instance Segmentation via 2D Mask Tracking and 3D-2D Projection Merging
Class-agnostic 3D instance segmentation is critical for robotic systems operating in unknown environments, enabling perception of previously unseen objects for reliable manipulation and navigation. Existing approaches typically project per-frame 2D instance masks into 3D and merge them, which often breaks object identities across time and yields fragmented 3D instances.
Hugging Face Trending Papers
Jul 20
DA-Fusion: Deformable Attention-Based RGB-D Fusion Transformer for Unseen Object Instance Segmentation
In logistics automation, precise segmentation of unseen objects is crucial for efficient robotic manipulation in cluttered environments. Tasks such as bin-picking and shelf-picking require robust perception to handle occlusions, varying object shapes, and complex spatial arrangements.
arXiv AI
Jul 20
Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation
arXiv:2607. 15396v1 Announce Type: cross Abstract: Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used.