Mind the (Data) Gap: Evaluating Vision Systems in Small Data Applications
Fuente:
arXiv
Saved in:
| Main Authors: | Stevens, Samuel, Rayeed, S M, Kline, Jenna |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
BeetleVerse: A Study on Taxonomic Classification of Ground Beetles
by: Rayeed, S M, et al.
Published: (2025)
by: Rayeed, S M, et al.
Published: (2025)
Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching
by: Liu, Yuhan, et al.
Published: (2025)
by: Liu, Yuhan, et al.
Published: (2025)
Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
by: Stogiannidis, Ilias, et al.
Published: (2025)
by: Stogiannidis, Ilias, et al.
Published: (2025)
BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks
by: Stevens, Samuel
Published: (2025)
by: Stevens, Samuel
Published: (2025)
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data
by: Hennicke, Leonhard, et al.
Published: (2024)
by: Hennicke, Leonhard, et al.
Published: (2024)
Mind the Gap: Evaluating LLM Understanding of Human-Taught Road Safety Principles
by: Kranti, Chalamalasetti
Published: (2025)
by: Kranti, Chalamalasetti
Published: (2025)
Game-invariant Features Through Contrastive and Domain-adversarial Learning
by: Kline, Dylan
Published: (2025)
by: Kline, Dylan
Published: (2025)
Mind the Gap: Bridging Occlusion in Gait Recognition via Residual Gap Correction
by: Gupta, Ayush, et al.
Published: (2025)
by: Gupta, Ayush, et al.
Published: (2025)
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment
by: Zavras, Angelos, et al.
Published: (2024)
by: Zavras, Angelos, et al.
Published: (2024)
Interpretable and Testable Vision Features via Sparse Autoencoders
by: Stevens, Samuel, et al.
Published: (2025)
by: Stevens, Samuel, et al.
Published: (2025)
Mind the Gap: Analyzing Lacunae with Transformer-Based Transcription
by: Borkar, Jaydeep, et al.
Published: (2024)
by: Borkar, Jaydeep, et al.
Published: (2024)
Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views
by: Wilczynski, Grzegorz, et al.
Published: (2026)
by: Wilczynski, Grzegorz, et al.
Published: (2026)
Understanding Graphical Perception in Data Visualization through Zero-shot Prompting of Vision-Language Models
by: Guo, Grace, et al.
Published: (2024)
by: Guo, Grace, et al.
Published: (2024)
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers
by: Badisa, Lokesh, et al.
Published: (2024)
by: Badisa, Lokesh, et al.
Published: (2024)
Bridging the Applicator Gap with Data-Doping:Dual-Domain Learning for Precise Bladder Segmentation in CT-Guided Brachytherapy
by: Das, Suresh, et al.
Published: (2026)
by: Das, Suresh, et al.
Published: (2026)
MMLA: Multi-Environment, Multi-Species, Low-Altitude Drone Dataset
by: Kline, Jenna, et al.
Published: (2025)
by: Kline, Jenna, et al.
Published: (2025)
Generalization Gap in Data Augmentation: Insights from Illumination
by: Xiao, Jianqiang, et al.
Published: (2024)
by: Xiao, Jianqiang, et al.
Published: (2024)
Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection
by: Bogdoll, Daniel, et al.
Published: (2025)
by: Bogdoll, Daniel, et al.
Published: (2025)
Mind the Exit Pupil Gap: Revisiting the Intrinsics of a Standard Plenoptic Camera
by: Michels, Tim, et al.
Published: (2024)
by: Michels, Tim, et al.
Published: (2024)
Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer
by: Yardi, Yash, et al.
Published: (2025)
by: Yardi, Yash, et al.
Published: (2025)
Mind the Gap: Preserving and Compensating for the Modality Gap in CLIP-Based Continual Learning
by: Huang, Linlan, et al.
Published: (2025)
by: Huang, Linlan, et al.
Published: (2025)
Don't Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysis
by: Kahrs, Bennet, et al.
Published: (2026)
by: Kahrs, Bennet, et al.
Published: (2026)
Spectral Gaps and Spatial Priors: Studying Hyperspectral Downstream Adaptation Using TerraMind
by: Leonardi, Julia Anna, et al.
Published: (2026)
by: Leonardi, Julia Anna, et al.
Published: (2026)
Data Augmentation in Human-Centric Vision
by: Jiang, Wentao, et al.
Published: (2024)
by: Jiang, Wentao, et al.
Published: (2024)
SADGE: Structure and Appearance Domain Gap Estimation of Synthetic and Real Data
by: Bartkowiak, Patryk, et al.
Published: (2026)
by: Bartkowiak, Patryk, et al.
Published: (2026)
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models
by: Irawan, Patrick Amadeus, et al.
Published: (2024)
by: Irawan, Patrick Amadeus, et al.
Published: (2024)
DAViD: Data-efficient and Accurate Vision Models from Synthetic Data
by: Saleh, Fatemeh, et al.
Published: (2025)
by: Saleh, Fatemeh, et al.
Published: (2025)
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
by: Chen, Boqi, et al.
Published: (2025)
by: Chen, Boqi, et al.
Published: (2025)
Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation
by: Fang, Lexin, et al.
Published: (2025)
by: Fang, Lexin, et al.
Published: (2025)
Learning Vision from Models Rivals Learning Vision from Data
by: Tian, Yonglong, et al.
Published: (2023)
by: Tian, Yonglong, et al.
Published: (2023)
kabr-tools: Automated Framework for Multi-Species Behavioral Monitoring
by: Kline, Jenna, et al.
Published: (2025)
by: Kline, Jenna, et al.
Published: (2025)
VisionTrap: Unanswerable Questions On Visual Data
by: Saadat, Asir, et al.
Published: (2025)
by: Saadat, Asir, et al.
Published: (2025)
Scaling Up Forest Vision with Synthetic Data
by: She, Yihang, et al.
Published: (2025)
by: She, Yihang, et al.
Published: (2025)
Convolutional Initialization for Data-Efficient Vision Transformers
by: Zheng, Jianqiao, et al.
Published: (2024)
by: Zheng, Jianqiao, et al.
Published: (2024)
How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?
by: Ulmer, Maximilian, et al.
Published: (2024)
by: Ulmer, Maximilian, et al.
Published: (2024)
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning
by: Tian, Hongduan, et al.
Published: (2024)
by: Tian, Hongduan, et al.
Published: (2024)
Mind the Gap: Continuous Magnification Sampling for Pathology Foundation Models
by: Möllers, Alexander, et al.
Published: (2026)
by: Möllers, Alexander, et al.
Published: (2026)
Domain Generalization with Small Data
by: Chen, Kecheng, et al.
Published: (2024)
by: Chen, Kecheng, et al.
Published: (2024)
Bridging the Domain Gap for Flight-Ready Spaceborne Vision
by: Park, Tae Ha, et al.
Published: (2024)
by: Park, Tae Ha, et al.
Published: (2024)
MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
by: Lin, Honglin, et al.
Published: (2026)
by: Lin, Honglin, et al.
Published: (2026)
Similar Items
-
BeetleVerse: A Study on Taxonomic Classification of Ground Beetles
by: Rayeed, S M, et al.
Published: (2025) -
Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching
by: Liu, Yuhan, et al.
Published: (2025) -
Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models
by: Stogiannidis, Ilias, et al.
Published: (2025) -
BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks
by: Stevens, Samuel
Published: (2025) -
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data
by: Hennicke, Leonhard, et al.
Published: (2024)