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20172025
most citedStructured Attentions for Visual Question Answering

26 citations · 46 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound

Zhengpeng Shi, Yanpeng Zhao, Jianqun Zhou +6

AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions. To gauge and diagnose the capacity of multimoda…

cs.CV2025

MILR: Improving Multimodal Image Generation via Test-Time Latent Reasoning

Yapeng Mi, Yanpeng Zhao, Hengli Li +6

Reasoning-augmented machine learning systems have shown improved performance in various domains, including image generation. However, existing reasoning-based methods for image gen…

cs.CV2025

NEP: Autoregressive Image Editing via Next Editing Token Prediction

Huimin Wu, Xiaojian Ma, Haozhe Zhao +2

Text-guided image editing involves modifying a source image based on a language instruction and, typically, requires changes to only small local regions. However, existing approach…

cs.CV2022

MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound

Rowan Zellers, Jiasen Lu, Ximing Lu +7

As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce MERLOT Reserve, a model that represents videos jointly over time -- t…

cs.CV2017★ 26 cited

Structured Attentions for Visual Question Answering

Chen Zhu, Yanpeng Zhao, Shuaiyi Huang +2

Visual attention, which assigns weights to image regions according to their relevance to a question, is considered as an indispensable part by most Visual Question Answering models…