Zobrazeno 1 - 10
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pro vyhledávání: '"Zhang,Jinghao"'
Diffusion models have made compelling progress on facilitating high-throughput daily production. Nevertheless, the appealing customized requirements are remain suffered from instance-level finetuning for authentic fidelity. Prior zero-shot customizat
Externí odkaz:
http://arxiv.org/abs/2409.17740
Autor:
Liu, Yuting, Zhang, Jinghao, Dang, Yizhou, Liang, Yuliang, Liu, Qiang, Guo, Guibing, Zhao, Jianzhe, Wang, Xingwei
Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens into a unified sequence input an
Externí odkaz:
http://arxiv.org/abs/2408.10645
Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use of multimodal information is expected to provide more comprehensive in
Externí odkaz:
http://arxiv.org/abs/2408.06360
Diffusion models have made remarkable progress in solving various inverse problems, attributing to the generative modeling capability of the data manifold. Posterior sampling from the conditional score function enable the precious data consistency ce
Externí odkaz:
http://arxiv.org/abs/2407.09768
The rapid spread of information through mobile devices and media has led to the widespread of false or deceptive news, causing significant concerns in society. Among different types of misinformation, image repurposing, also known as out-of-context m
Externí odkaz:
http://arxiv.org/abs/2406.04756
Algorithmic trading refers to executing buy and sell orders for specific assets based on automatically identified trading opportunities. Strategies based on reinforcement learning (RL) have demonstrated remarkable capabilities in addressing algorithm
Externí odkaz:
http://arxiv.org/abs/2407.01577
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS). However, while these systems have flourished, their susceptibility to security threats has been largely overlooked. In
Externí odkaz:
http://arxiv.org/abs/2402.14836
Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the obje
Externí odkaz:
http://arxiv.org/abs/2402.11622
Graph Neural Networks (GNNs) have made significant advancements in node classification, but their success relies on sufficient labeled nodes per class in the training data. Real-world graph data often exhibits a long-tail distribution with sparse lab
Externí odkaz:
http://arxiv.org/abs/2402.00450
Autor:
Zhang, Jinghao, Zhao, Feng
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications. Nevertheless, existing works typically concentrate on regarding each degradation independently, while their relationship has been l
Externí odkaz:
http://arxiv.org/abs/2308.00759