most citedBOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval

5 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Semantic Alignment for Multimodal Large Language Models

Tao Wu, Mengze Li, Jingyuan Chen +6

Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly…

cs.AI20241 cited

Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence

Zhengqing Fang, Shuowen Zhou, Zhouhang Yuan +9

Although data-driven artificial intelligence (AI) in medical image diagnosis has shown impressive performance in silico, the lack of interpretability makes it difficult to incorpor…

cs.MM20224 cited

HERO: HiErarchical spatio-tempoRal reasOning with Contrastive Action Correspondence for End-to-End Video Object Grounding

Mengze Li, Tianbao Wang, Haoyu Zhang +6

Video Object Grounding (VOG) is the problem of associating spatial object regions in the video to a descriptive natural language query. This is a challenging vision-language task t…

cs.AI20225 cited

BOSS: Bottom-up Cross-modal Semantic Composition with Hybrid Counterfactual Training for Robust Content-based Image Retrieval

Wenqiao Zhang, Jiannan Guo, Mengze Li +5

Content-Based Image Retrieval (CIR) aims to search for a target image by concurrently comprehending the composition of an example image and a complementary text, which potentially…

cs.CV20221 cited

Enhancing Fairness of Visual Attribute Predictors

Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger +4

The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We…