18 papers
Probing Identity-Specific Motion Signatures: A Controlled Diagnostic Study
Yingtie Lei, Fangxun Liu, Baicheng Wu +13
Identity recognition (e.g., person, animal re-identification) has traditionally relied heavily on static appearance cues. Yet motion--consistent, individual-specific dynamics--can…
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…
BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing
Fangxun Liu, S M Rayeed, Samuel Stevens +21
In entomology and ecology research, biologists often need to collect a large number of insects, among which beetles are the most common species. A common practice for biologists to…
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…
Finer-Personalization Rank: Fine-Grained Retrieval Examines Identity Preservation for Personalized Generation
Connor Kilrain, David Carlyn, Julia Chae +3
The rise of personalized generative models raises a central question: how should we evaluate identity preservation? Given a reference image (e.g., one's pet), we expect the generat…
Interpretable and Testable Vision Features via Sparse Autoencoders
Samuel Stevens, Wei-Lun Chao, Tanya Berger-Wolf +1
To truly understand vision models, we must not only interpret their learned features but also validate these interpretations through controlled experiments. While earlier work offe…