This work proposes GPERT, a framework to address the trade-off between accuracy and temporal resolution in event-based 3DGS, by decouple the rendering into two branches: event-by-event geometry (depth) rendering and snapshot-based radiance (intensity) rendering, by using ray-tracing and the image of warped events.
Kai Kohyama, Yoshimitsu Aoki, Guillermo Gallego et al.· arXiv.org· 0 citations
This work proposes the first Prompt-based Continual Compositional Zero-Shot Learning (PromptCCZSL) framework that retains prior knowledge through recency-weighted multi-teacher distillation, and introduces a comprehensive protocol that jointly measures catastrophic forgetting and compositional generalization.
Sauda Maryam, Sara Nadeem, Faisal Z. Qureshi et al.· arXiv.org· 1 citation
A novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors that enables planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs) offering greater flexibility and personalization is introduced.
Riqiang Gao, Simon Arberet, Martin Kraus et al.· arXiv.org· 4 citations
Consist-Retinex is proposed, which first uses a Retinex Transformer Decomposition Network to obtain paired reflectance and illumination maps, then trains two conditional consistency models with a Retinex-aware dual objective and adaptive noise-emphasized fixed-point sampling.
Jian Xu, Wei Chen, Shigui Li et al.· arXiv.org· 0 citations
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OC-GS, an object-centric Gaussian splatting that refines each image's angle while maintaining a shared camera, rotation axis, and pivot, jointly optimizes image-derived geometry and angles to reconstruct objects from sparse, irregular captures.
This work presents ClearGS for 3D Gaussian Splatting (3DGS), which achieves state-of-the-art overall performance, with consistent CLIP-IQA and MUSIQ gains and LPIPS reductions in most degradation settings, without paired sharp supervision or matched clean references.
Xuan-Zhi Liu, Xinyi Wu, Hang-Hai Pan et al.· 0 citations
Infant movement analysis provides valuable biomarkers for the early identification of neurodevelopmental disorders. Recent advances in deep learning have enabled automated analysis of infant movements from video-derived skeletal representations, achieving performance comparable to expert assessment for tasks such as Ge...
Federated learning (FL) data corruption can affect either inputs or labels, but it remains unclear whether input-conditional uncertainty and prediction-label loss expose these corruption modes equally. This paper compares two corruption-detection signals in FL: input-conditional uncertainty and prediction-label loss. T...
Bradley Scott, Zeqi Luo, Edmond S. L. Ho· 1 citation
Medical vision-language model (VLM) post-training is commonly evaluated through answer accuracy. We examine how changes in accuracy and training objectives relate to image-conditioned decisions in a controlled Qwen2.5-VL-3B study on PMC-VQA. We compare supervised fine-tuning (SFT) with low-rank adaptation (LoRA) restri...
This study proposes a novel extension of an existing RGB video compression model that improves tracking area under the curve and distance precision and improves tracking area under the curve by up to 23.42% and distance precision by up to 35.56% on examples from the HOT2026 dataset.
Alfredo Scalera, Paul Murray, J. Zabalza· 0 citations
The findings indicate that the evaluated VLMs exhibit a consistent behavioral limitation when processing typographic structures containing multiple spatial frequency layers.
Mert İncidelen, Yamen Kashkash, A. Berker et al.· 0 citations
Endpoint-Constrained Optimization (ECO), a lightweight postprocessing layer that anchors the trajectory to the vehicle's executed history, preserves the policy's predicted endpoint, and reshapes the intermediate waypoints to improve feasibility is introduced.
Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski et al.· 0 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
