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Found 27 papers, 16 papers with code
BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning
We find such emergent behaviors in biological vision models via large-scale contrastive vision-language training.
Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens
This paper aims to bridge the gap between current imaging practices and the potential for automated analysis by presenting key considerations for creating biological specimen images optimized for computer vision applications.
Building Machine Learning Challenges for Anomaly Detection in Science
We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR).
Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation
Despite its simplicity and computational efficiency, CAM often struggles to identify discriminative regions that distinguish visually similar fine-grained classes.
Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis
We present a simple usage of pre-trained Vision Transformers (ViTs) for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as different bird species or dog breeds.
Explainable Artificial Intelligence (XAI)
Explainable Models
+3
Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation
Powered by Segment Anything Model 2 (SAM~2) initially developed for video segmentation, we show that SST can achieve high-quality trait and part segmentation with merely one labeled image per species -- a breakthrough for analyzing specimen images.
Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bird species.
Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge
With emerging remote sensing hardware, e. g., camera traps and drones, and sophisticated AI models in situ, edge computing will be more significant in future AI-driven animal ecology (ADAE) studies.
Fine-Tuning is Fine, if Calibrated
For example, fine-tuning a pre-trained classifier capable of recognizing a large number of classes to master a subset of classes at hand is shown to drastically degrade the model's accuracy in the other classes it had previously learned.
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs).
BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos
This study presents a novel dataset from drone videos for baboon detection, tracking, and behavior recognition.
Modeling Access Differences to Reduce Disparity in Resource Allocation
Motivated by COVID-19 vaccine allocation, where vulnerable subpopulations are simultaneously more impacted in terms of health and more disadvantaged in terms of access to the vaccine, we formalize and study the problem of resource allocation when there are inherent access differences that correlate with advantage and disadvantage.
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
In this work, we exploit the structured context linked to camera trap images to boost out-of-distribution generalization for species classification tasks in camera traps.
Ranked #1 on
Image Classification
on iWildCam2020-WILDS
(using extra training data)
BioCLIP: A Vision Foundation Model for the Tree of Life
We then develop BioCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge.
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
We present a novel usage of Transformers to make image classification interpretable.
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how organisms diversify and evolve.
Seeing biodiversity: perspectives in machine learning for wildlife conservation
Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices.
Fairness-aware Summarization for Justified Decision-Making
In other words, decision-relevant features should provide sufficient information for the predicted outcome and should be independent of the membership of individuals in protected groups such as race and gender.
Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions
Cannabis legalization has been welcomed by many U. S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear.
Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research
Machine learning, artificial intelligence, and deep learning have advanced significantly over the past decade.
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models
Recently, a few studies have focused on learning temporal information in addition to the topology of a graph.
Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis
We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets.
Framework for Inferring Following Strategies from Time Series of Movement Data
Given a set of time series that includes coordinated movement and a set of candidate strategies as inputs, we provide the first methodology (to the best of our knowledge) to infer whether each individual uses local-agreement-system or dictatorship-like strategy to achieve movement coordination at the group level.
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"
In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other.
Animal Wildlife Population Estimation Using Social Media Images Collections
Social media is the rich source of wildlife images, which come with a huge bias, thus thwarting traditional population size estimate approaches.
A Framework For Identifying Group Behavior Of Wild Animals
Activity recognition and, more generally, behavior inference tasks are gaining a lot of interest.
