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20222026
most citedVQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering

4 citations · 6 across the 18 of their papers we have counts for

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7 papers · 1 filter

cs.MM2026

OlfactProfile: Profile-Conditioned Odor Prediction from Audiovisual Content

Zhengyu Lou, Bosheng Qin, Yanan Wang +3

Automated video-odor matching predicts scents aligned with audiovisual content for scent-enhanced media. Existing methods usually treat odor labels as determined only by scene cont…

cs.CV2026

Qwen-Image-2.0 Technical Report

Bing Zhao, Chenfei Wu, Deqing Li +72

We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…

cs.AI2026

Logics-Parsing-Omni Technical Report

Xin An, Jingyi Cai, Xiangyang Chen +22

Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This fram…

cs.RO2026

LILAC: Language-Conditioned Object-Centric Optical Flow for Open-Loop Trajectory Generation

Motonari Kambara, Koki Seno, Tomoya Kaichi +2

We address language-conditioned robotic manipulation using flow-based trajectory generation, which enables training on human and web videos of object manipulation and requires only…

cs.LG2026

Training and Benchmarking Code Generation for Physics-Inspired Animations

Yanan Wang, Renxi Wang, Yongxin Wang +5

Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving. However, their ability to generate ex…

cs.CV2026

SpatialV2A: Visual-Guided High-fidelity Spatial Audio Generation

Yanan Wang, Linjie Ren, Zihao Li +2

While video-to-audio generation has achieved remarkable progress in semantic and temporal alignment, most existing studies focus solely on these aspects, paying limited attention t…

cs.MM2026

Learning Audio-Visual Embeddings with Inferred Latent Interaction Graphs

Donghuo Zeng, Hao Niu, Yanan Wang +1

Learning robust audio-visual embeddings requires bringing genuinely related audio and visual signals together while filtering out incidental co-occurrences - background noise, unre…