Atomizer-IO: Beyond Pixels, Patches and Grids

arXiv:2609.40320 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Aggregation — 26× in this paper · explained in 1 episode
• Transformer — 14× in this paper · explained in 2 episodes
• Point Clouds — 12× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Latent Representation — 9× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Decode — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Adam — 3× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 3× in this paper
• Foundation Models — 3× in this paper · explained in 7 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Adaptive Decoding — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Homogeneity — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Latent Reasoning — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Spectral Signature — 1× in this paper · explained in 2 episodes

Sources

• AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
• FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes
• Deep Learning for 3D Point Clouds: A Survey
• xBD: A Dataset for Assessing Building Damage from Satellite Imagery
• ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
• Perceiver IO: A General Architecture for Structured Inputs & Outputs
• Foundation Models for Generalist Geospatial Artificial Intelligence
• FlexiMo: A Flexible Remote Sensing Foundation Model
• PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
• Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
• Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
• Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models