Zobrazeno 1 - 10
of 171
pro vyhledávání: '"In, Yeonjun"'
Autor:
In, Yeonjun, Kim, Sungchul, Rossi, Ryan A., Tanjim, Md Mehrab, Yu, Tong, Sinha, Ritwik, Park, Chanyoung
The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages. However, our prelimi
Externí odkaz:
http://arxiv.org/abs/2409.02361
Graph neural networks (GNN) are vulnerable to adversarial attacks, which aim to degrade the performance of GNNs through imperceptible changes on the graph. However, we find that in fact the prevalent meta-gradient-based attacks, which utilizes the gr
Externí odkaz:
http://arxiv.org/abs/2407.19155
Recently, interpreting complex charts with logical reasoning has emerged as challenges due to the development of vision-language models. A prior state-of-the-art (SOTA) model has presented an end-to-end method that leverages the vision-language model
Externí odkaz:
http://arxiv.org/abs/2405.00021
We investigate the replay buffer in rehearsal-based approaches for graph continual learning (GCL) methods. Existing rehearsal-based GCL methods select the most representative nodes for each class and store them in a replay buffer for later use in tra
Externí odkaz:
http://arxiv.org/abs/2402.13711
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend against such attacks, robust graph structure refinement (GSR) methods aim at minimizing the effect of adversarial edges based on node features, graph structure, o
Externí odkaz:
http://arxiv.org/abs/2402.11837
Scene graph generation (SGG) models have suffered from inherent problems regarding the benchmark datasets such as the long-tailed predicate distribution and missing annotation problems. In this work, we aim to alleviate the long-tailed problem of SGG
Externí odkaz:
http://arxiv.org/abs/2401.09786
This paper presents an automated method for optimizing parameters in analog/high-frequency circuits, aiming to maximize performance parameters of a radio-frequency (RF) receiver. The design target includes a reduction of power consumption and noise f
Externí odkaz:
http://arxiv.org/abs/2403.17938
Autor:
Kim, Kibum, Yoon, Kanghoon, Jeon, Jaehyeong, In, Yeonjun, Moon, Jinyoung, Kim, Donghyun, Park, Chanyoung
Publikováno v:
CVPR (2024), 28306-28316
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard, studies on WSSGG have utilized image captions to obtain unloc
Externí odkaz:
http://arxiv.org/abs/2310.10404
Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a node is anomalous or not. One common observation we made from previous unsupervised methods is that they not only assume the absence of such anomaly labels, but also the abse
Externí odkaz:
http://arxiv.org/abs/2308.11669
Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features. Among various GNN models, graph contrastive learning (GCL) based methods specifically
Externí odkaz:
http://arxiv.org/abs/2306.13854