6 citations · 11 across the 5 of their papers we have counts for
5 papers
StructXLIP: Enhancing Vision-language Models with Multimodal Structural Cues
Zanxi Ruan, Songqun Gao, Qiuyu Kong +2
Edge-based representations are fundamental cues for visual understanding, a principle rooted in early vision research and still central today. We extend this principle to vision-la…
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…
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
Ziru Chen, Shijie Chen, Yuting Ning +17
The advancements of large language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked…
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
Vardaan Pahuja, Weidi Luo, Yu Gu +7
Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalizati…
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
Cheng-Hao Tu, Hong-You Chen, Zheda Mai +7
We propose a learning problem involving adapting a pre-trained source model to the target domain for classifying all classes that appeared in the source data, using target data tha…