2 citations · 3 across the 6 of their papers we have counts for
10 papers
RealBirdID: Benchmarking Bird Species Identification in the Era of MLLMs
Logan Lawrence, Mustafa Chasmai, Rangel Daroya +8
Fine-grained bird species identification in the wild is frequently unanswerable from a single image: key cues may be non-visual (e.g. vocalization), or obscured due to occlusion, c…
Merlin L48 Spectrogram Dataset
Aaron Sun, Subhransu Maji, Grant Van Horn
In the single-positive multi-label (SPML) setting, each image in a dataset is labeled with the presence of a single class, while the true presence of other classes remains unknown.…
You May Speak Freely: Improving the Fine-Grained Visual Recognition Capabilities of Multimodal Large Language Models with Answer Extraction
Logan Lawrence, Oindrila Saha, Megan Wei +3
Despite the renewed interest in zero-shot visual classification due to the rise of Multimodal Large Language Models (MLLMs), the problem of evaluating free-form responses of auto-r…
Consensus-Driven Active Model Selection
Justin Kay, Grant Van Horn, Subhransu Maji +2
The widespread availability of off-the-shelf machine learning models poses a challenge: which model, of the many available candidates, should be chosen for a given data analysis ta…
Moment Sampling in Video LLMs for Long-Form Video QA
Mustafa Chasmai, Gauri Jagatap, Gouthaman KV +3
Recent advancements in video large language models (Video LLMs) have significantly advanced the field of video question answering (VideoQA). While existing methods perform well on…
Feedforward Few-shot Species Range Estimation
Christian Lange, Max Hamilton, Elijah Cole +6
Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts. By mapping the spatial ranges of all species, we wou…
Generate, Transduct, Adapt: Iterative Transduction with VLMs
Oindrila Saha, Logan Lawrence, Grant Van Horn +1
Transductive zero-shot learning with vision-language models leverages image-image similarities within the dataset to achieve better classification accuracy compared to the inductiv…
WildSAT: Learning Satellite Image Representations from Wildlife Observations
Rangel Daroya, Elijah Cole, Oisin Mac Aodha +2
Species distributions encode valuable ecological and environmental information, yet their potential for guiding representation learning in remote sensing remains underexplored. We…
Combining Observational Data and Language for Species Range Estimation
Max Hamilton, Christian Lange, Elijah Cole +5
Species range maps (SRMs) are essential tools for research and policy-making in ecology, conservation, and environmental management. However, traditional SRMs rely on the availabil…
Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions
Oindrila Saha, Grant Van Horn, Subhransu Maji
The zero-shot performance of existing vision-language models (VLMs) such as CLIP is limited by the availability of large-scale, aligned image and text datasets in specific domains.…