5 citations · 9 across the 8 of their papers we have counts for
9 papers
Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time
Sooyoung Jeon, Hongjie Tian, Lemeng Wang +7
Camera traps are vital for large-scale biodiversity monitoring, yet accurate automated analysis remains challenging due to diverse deployment environments. While the computer visio…
BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models
Ziheng Zhang, Xinyue Ma, Arpita Chowdhury +9
This work investigates descriptive captions as an additional source of supervision for biological multimodal foundation models. Images and captions can be viewed as complementary s…
AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models
Zheda Mai, Arpita Chowdhury, Zihe Wang +5
The rise of vision foundation models (VFMs) calls for systematic evaluation. A common approach pairs VFMs with large language models (LLMs) as general-purpose heads, followed by ev…
Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation
Ziheng Zhang, Jianyang Gu, Arpita Chowdhury +5
Class activation map (CAM) has been widely used to highlight image regions that contribute to class predictions. Despite its simplicity and computational efficiency, CAM often stru…
Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai +10
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 vis…
Static Segmentation by Tracking: A Label-Efficient Approach for Fine-Grained Specimen Image Segmentation
Zhenyang Feng, Zihe Wang, Jianyang Gu +22
We study image segmentation in the biological domain, particularly trait segmentation from specimen images (e.g., butterfly wing stripes, beetle elytra). This fine-grained task is…
Fine-Tuning is Fine, if Calibrated
Zheda Mai, Arpita Chowdhury, Ping Zhang +8
Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing…
MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs
Jihyung Kil, Zheda Mai, Justin Lee +6
The ability to compare objects, scenes, or situations is crucial for effective decision-making and problem-solving in everyday life. For instance, comparing the freshness of apples…
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
Dipanjyoti Paul, Arpita Chowdhury, Xinqi Xiong +11
We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully connected layer to incorporate cla…