arXiv Computer Vision
Insights from Autoresearch for Solar Panel Segmentation
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arXiv Machine Learning
Sep 10
SolarBench: A global solar energy nowcasting benchmark
SolarBench is an open global benchmark for image-based solar nowcasting that consolidates over six million sky and satellite images from 11 sites across a decade, paired with irradiance, PV output, and atmospheric data. The benchmark includes a toolbox for reproducible data access, processing, model development, and evaluation. Using SolarBench, the authors benchmark representative models, uncover a gap between average forecasting accuracy and the capture of rapid solar fluctuations, quantify predictability across cloud regimes, and demonstrate data‑efficient adaptation to new PV systems.
arXiv Computer Vision
Sep 23
Separators in Enhancing Autoregressive Pretraining for Vision Mamba
arXiv:2603.03806v2 Announce Type: replace Abstract: The state space model Mamba has recently emerged as a promising paradigm in computer vision, attracting considerable attention for its efficient ha...
arXiv AI
Jun 16
ActiveSAM: Image-Conditional Class Pruning for Fast and Accurate Open-Vocabulary Segmentation
arXiv:2606. 16996v1 Announce Type: cross Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabulary semantic segmentation (OVSS) is inefficient: full-resolution decoding is typically run over the entire dataset vocabulary, whereas each image contains only a small active subset of classes.
arXiv AI
Sep 30
ActiveSAM: Fast and Accurate Open-Vocabulary Semantic Segmentation with Frozen SAM 3
arXiv:2606.16996v2 Announce Type: replace-cross Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabu...
arXiv Machine Learning
Sep 22
SolarFlowRefiner: Refinement-Aware Flow Matching for Surface Solar Radiation Downscaling
SolarFlowRefiner is a refinement‑aware flow‑matching framework designed to downscale high‑resolution surface solar radiation (SSR) fields from coarse ERA5 radiative variables and satellite channels. It first uses a conditional FlowMatch generator to predict a normalized correction to an upsampled ERA5 baseline, then trains a refiner on prediction‑conditioned states between the generator’s output and the target residual, exposing the refiner to the generator’s structured errors. The refinement objective is backpropagated through the FlowMatch sampler, enabling joint optimization of generation and correction, and experiments on an ERA5–SolarCube benchmark demonstrate consistent improvements over standalone generation and post‑hoc refinement.
arXiv Machine Learning
Jul 31
ReToken: One Token to Improve Vision-Language Models for Visual Retrieval
arXiv:2607. 28627v1 Announce Type: cross Abstract: Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at once is computationally infeasible under GPU memory constraints.