most citedGenerative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond

1 citations · 2 across the 5 of their papers we have counts for

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5 papers

cs.MM20241 cited

Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond

Yongqi Li, Wenjie Wang, Leigang Qu +3

The recent advancements in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effective…

cs.CL2024

Distillation Enhanced Generative Retrieval

Yongqi Li, Zhen Zhang, Wenjie Wang +3

Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful…

cs.IR2024

Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction

Jinqiu Jin, Sihao Ding, Wenjie Wang +1

Common click-through rate (CTR) prediction recommender models tend to exhibit feature-level bias, which leads to unfair recommendations among item groups and inaccurate recommendat…

cs.LG20241 cited

GOODAT: Towards Test-time Graph Out-of-Distribution Detection

Luzhi Wang, Dongxiao He, He Zhang +5

Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in scenarios where the testing data shares the distri…

cs.LG2023

Wasserstein Adversarial Examples on Univariant Time Series Data

Wenjie Wang, Li Xiong, Jian Lou

Adversarial examples are crafted by adding indistinguishable perturbations to normal examples in order to fool a well-trained deep learning model to misclassify. In the context of…