CODA is a Highlight at @iccv.bsky.social next week!
Poster Session 1 Tuesday 10/21 11:45am, and Demo Session 6 Thursday 10/23 at 2:30pm.

Paper: www.arxiv.org/abs/2507.23771
Demo: huggingface.co/spaces/justi...
Code: github.com/justinkay/coda
@csail.mit.edu News:
bit.ly/48s6yS2

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Thanks to MIT CSAIL @csail.mit.edu for covering CODA and how it supports ML practitioners in environmental conservation: t.co/SNJnnGfKvn

Try it yourself! We built a fun demo for finding the best wildlife classification model with @hf.co and @gradio-hf.bsky.social: huggingface.co/spaces/justi...

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CODA is exceptionally label-efficient. On a benchmark suite of 26 different datasets, we show that CODA identifies the optimal or near-optimal model with fewer than 25 labeled examples over 50% of the time, and with fewer than 100 labeled examples over 80% of the time. 6/

CODA constructs a probabilistic model of which model is best at any labeling budget. To do this, we estimate confusion matrices for each candidate that we can 1) integrate over at any time to estimate which is best, and 2) update with new labels via Bayesian inference. 5/

To do this efficiently, CODA leverages the *wisdom of the crowd* (of AI models), using the ensemble predictions from candidate models as a prior over the true labels of your unlabeled data. This helps identify high-value data points where top-performing models disagree. 4/

Typically, answering the model selection question requires collecting a large test dataset to determine which candidate model is the best for you. CODA instead makes the process *active* – interactive, iterative, and guided by the models themselves. 3/

Using AI for data analysis has typically meant training your own model. Large public model zoos like @hf.co Models are changing this paradigm, but pose a new challenge: which model, of the millions available, should you use to analyze your data? 2/

BirdCLEF25: Audio-based species identification focused on birds, amphibians, mammals, and insects in Colombia.
👉 www.kaggle.com/competitions...
@cvprconference.bsky.social @kaggle.com
#FGVC #CVPR #CVPR2025 #LifeCLEF
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Project webpage: aldi-daod.github.io
Paper: arxiv.org/abs/2403.12029
Codebase (consider giving us a star!): github.com/justinkay/aldi
Dataset: github.com/visipedia/ca...

Team: @timm.haucke.xyz, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, @sarameghanbeery.bsky.social, and Grant Van Horn

Contributions:
- SOTA results across architectures (Faster R-CNN, DETR, YOLO), backbones (ResNet, ConvNeXt, ViT), datasets
- Unified benchmarking & implementation framework, making it easy to develop and test new adaptation methods
- A new real-world adaptation dataset sourced from fisheries sonar