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Dataset results
29 results for “Few-shot”
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
<p>A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object interactions (HOIs) from natural images. It is inspired by two desirable characteristics from the classical Bongard problems (BPs): 1) few-shot concept learning, and 2) context-dependent reasoning. We carefully curate the few-shot instances with hard negatives, where positive and negative images only disagree on action labels, making mere recognition of object categories insufficient to complete our benchmarks. We also design multiple test sets to systematically study the generalization of visual learning models, where we vary the overlap of the HOI concepts between the training and test sets of few-shot instances, from partial to no overlaps. Bongard-HOI presents a substantial challenge to today's visual recognition models. The state-of-the-art HOI detection model achieves only 62% accuracy on few-shot binary prediction while even amateur human testers on MTurk have 91% accuracy. With the Bongard-HOI benchmark, we hope to further advance research efforts in visual reasoning, especially in holistic perception-reasoning systems and better representation learning.</p>
LOLA - Evaluation Tables - Few-Shot
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few-shot dataset
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Few-Shot Meta-Learning Applied to Whole Brain Activity Maps Improves Systems Neuropharmacology and Drug Discovery
<p>Data and Code are all included</p>
Soundscape Datasets for Few-Shot Bird Sound Classification
<p>This repository provides easy access to open-source soundscape datasets of bird sounds, specifically optimized for few-shot classification.</p> <p><code>soundscapes.zip</code> contains evaluation soundscape datasets from the BIRB benchmark (https://arxiv.org/abs/2312.07439), downsampled to 16kHz, preprocessed using CNN14 from PANNs (https://arxiv.org/abs/1912.10211), to select a 6-second window with the highest bird activation, and converted to Pytorch (.pt) format to facilitate usability for evaluating deep neural networks. </p> <p>These preprocessed datasets are employed in the work "<em>Domain-Invariant Representation Learning of Bird Sounds</em>" (https://arxiv.org/abs/2409.08589), which evaluates the few-shot learning capabilities of deep learning models trained on focal recordings (e.g., Xeno-Canto) and tested on soundscape recordings.</p> <h2>Dataset Structure</h2> <h3>Validation Dataset</h3> <ul> <li><strong>POW (</strong><code>pow.pt</code><strong>): </strong>The validation dataset consists of 16,047 examples across 43 classes and is organized as a dictionary with <code>'data'</code> and<code> 'label'</code> keys representing bird sounds and their corresponding labels. Storing the entire validation dataset in a single tensor enables rapid loading and efficient processing, significantly accelerating the validation process. Classes with only one example are removed, as they are insufficient for one-shot classification tasks. Source: https://zenodo.org/records/4656848#.Y7ijhOxudhE</li> </ul> <h3>Test Datasets </h3> <p>Each test dataset is structured with multiple subfolders, each labeled with an eBird species code to represent data for a specific bird species.</p> <ul> <li><strong>SSW (</strong><code>ssw/</code><strong>):</strong> Contains 50,760 examples across 96 classes. Source: https://zenodo.org/records/7079380#.Y7ijHOxudhE</li> <li><strong>NES (</strong><code>coffee_farms/</code><strong>):</strong> Contains 6,952 examples across 89 classes. Source: https://zenodo.org/records/7525349#.ZB8z_-xudhE</li> <li><strong>UHH (</strong><code>hawaii/</code><strong>):</strong> Contains 59,583 examples across 27 classes. Source: https://zenodo.org/records/7078499#.Y7ijPuxudhE</li> <li><strong>HSN (</strong><code>high_sierras/</code><strong>):</strong> Contains 10,296 examples across 19 classes. Source: https://zenodo.org/records/7525805#.ZB8zsexudhE</li> <li><strong>SNE (</strong><code>sierras_kahl/</code><strong>):</strong> Contains 20,147 examples across 56 classes. Source: https://zenodo.org/records/7050014#.Y7ijWexudhE</li> <li><strong>PER (</strong><code>peru/</code><strong>):</strong> Contains 14,768 examples across 132 classes. Source: https://zenodo.org/records/7079124#.Y7iis-xudhE</li> </ul> <p>Code and detailed instructions, including data loading, model implementation, and few-shot evaluation, can be found at: https://github.com/ilyassmoummad/ProtoCLR</p>
Discovery of antimicrobial peptides targeting Acinetobacter baumannii via a pre-trained and fine-tuned few-shot learning-based pipeline
GEO Series GSE306268. Acinetobacter baumannii. 6 samples. Type: Expression profiling by high throughput sequencing.
Replication Package - Illicit Content Recognition with Few-shot Learning
<p>Replication package and dataset for the paper: When the Few Outweigh the Many: Illicit Content Recognition with Few-shot Learning</p>
PAN23 Profiling Cryptocurrency Influencers with Few-shot Learning
<p>This is the dataset for the shared task on <a href="https://pan.webis.de/clef23/pan23-web/author-profiling.html#">Profiling Cryptocurrency Influencers with Few-shot Learning</a>. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code.</p> <p> </p> <p><strong>Task</strong>: In this shared task we aim to profile cryptocurrency influencers in social media, from a low-resource perspective. Moreover, we propose to categorize other related aspects of the influencers, also using a low-resource setting. Specifically, we focus on English Twitter posts for three different sub-tasks:</p> <ol> <li><strong>Low-resource influencer profiling (subtask1):</strong> <ul> <li>Input:<br> 32 users per label with a maximum of 10 English tweets each.<br> Classes: (1) null, (2) nano, (3) micro, (4) macro, (5) mega</li> <li>Official evaluation metric: Macro F1</li> <li>Submission: TIRA.</li> <li>Baselines: User-character Logistic Regression; <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (bi-encoders) - zero shot [7], <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (label tuning) - few shot [7]</li> </ul> </li> <li><strong>Low-resource influencer interest identification (subtask2):</strong> <ul> <li>Input:<br> 64 users per label with 1 English tweet each.<br> Classes: (1) technical information, (2) price update, (3) trading matters, (4) gaming, (5) other</li> <li>Official evaluation metric: Macro F1</li> <li>Submission: TIRA.</li> <li>Baselines: User-character Logistic Regression; <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (bi-encoders) - zero shot [7], <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (label tuning) - few shot [7]</li> </ul> </li> <li><strong>Low-resource influencer intent identification (subtask3):</strong> <ul> <li>Input:<br> 64 users per label with 1 English tweets each.<br> Classes: (1) subjective opinion, (2) financial information, (3) advertising, (4) announcement</li> <li>Official evaluation metric: Macro F1</li> <li>Submission: TIRA.</li> <li>Baselines: User-character Logistic Regression; <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (bi-encoders) - zero shot [7], <a href="https://huggingface.co/sentence-transformers/sentence-t5-large">t5-large</a> (label tuning) - few shot [7]</li> </ul> </li> </ol> <p><strong>Versioning:</strong> </p> <ul> <li>1.0: initial upload</li> <li>1.1 fixed a minor bug where some users contained some non-English text. Since English is the target language in the competition, all non-English texts have been replaced or removed. </li> </ul>
Predicting the tensile properties of Wood Plastic Composites using material extrusion with Meta-based Few-Shot Learning
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.