2 citations · 3 across the 9 of their papers we have counts for
12 papers
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…
Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering
Jieyuan Liu, Jianyang Gu, Shijie Chen +2
Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retriev…
Leveraging Latent Visual Reasoning in Silence
Dongyao Zhu, Zhen Wang, Xi Xiao +7
Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generation. However, the necessity of th…
TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Life
Mridul Khurana, Amin Karimi Monsefi, Justin Lee +9
Accurately generating images across the Tree of Life is difficult: there are over 10M distinct species on Earth, many of which differ only by subtle visual traits. Despite the rema…
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…
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…
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…
BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning
Jianyang Gu, Samuel Stevens, Elizabeth G Campolongo +13
Foundation models trained at scale exhibit remarkable emergent behaviors, learning new capabilities beyond their initial training objectives. We find such emergent behaviors in bio…
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…
Technical Report for ActivityNet Challenge 2022 -- Temporal Action Localization
Shimin Chen, Wei Li, Jianyang Gu +2
In the task of temporal action localization of ActivityNet-1.3 datasets, we propose to locate the temporal boundaries of each action and predict action class in untrimmed videos. W…