activity
20172024
most citedEvaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness

59 citations · 158 across the 35 of their papers we have counts for

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5 papers · 1 filter

cs.CV20223 cited

Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

Jiaxi Wu, Jiaxin Chen, Mengzhe He +7

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a…

cs.CV202212 cited

MHMS: Multimodal Hierarchical Multimedia Summarization

Jielin Qiu, Jiacheng Zhu, Mengdi Xu +6

Multimedia summarization with multimodal output can play an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or p…

cs.CV20221 cited

Unsupervised Learning of Accurate Siamese Tracking

Qiuhong Shen, Lei Qiao, Jinyang Guo +7

Unsupervised learning has been popular in various computer vision tasks, including visual object tracking. However, prior unsupervised tracking approaches rely heavily on spatial s…

cs.CV202218 cited

Domain Generalization using Pretrained Models without Fine-tuning

Ziyue Li, Kan Ren, Xinyang Jiang +3

Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks. However, fine-tuning is usually computationally expensive due to the ever-growing size of pr…

cs.CV201717 cited

HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval

Xi Zhang, Siyu Zhou, Jiashi Feng +5

As the rapid growth of multi-modal data, hashing methods for cross-modal retrieval have received considerable attention. Deep-networks-based cross-modal hashing methods are appeali…