2 citations · 3 across the 7 of their papers we have counts for
7 papers · 1 filter
Mind the Tail: Data Selection Through Sparse Concepts
Ziheng Zhang, Neha Hulkund, Colin Lee +7
Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation strategies may produce vari…
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…
Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification
Zhiyuan Tao, Srikumar Sastry, Matthew J Thompson +9
Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories. However, in hierarchically structured label spaces, existing…
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-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…