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20232026
most citedAVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models

1 citations · 1 across the 6 of their papers we have counts for

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cs.CV2026

Hear, Localize, and Reason: Spatially Aware Scene Understanding for Audio-Visual LLMs

Kim Sung-Bin, Lee Jung-Mok, Jinwoo Jung +4

Audio-visual large language models (AV-LLMs) have made strong progress in multimodal understanding, yet most treat audio as monaural semantic content, limiting their ability to rea…

cs.CV2026

FloorSAV: Elucidating Spatial Audio-Visual Context with 2D Floormap for AV-LLMs

Kyeong-Rae Kim, Sungnyun Kim, Tae-Hyun Oh

While 3D spatial reasoning in dynamic egocentric environments is crucial for embodied intelligence, audio-visual large language models (AV-LLMs) lack explicit mechanisms to process…

cs.CV2025

VoiceCraft-Dub: Automated Video Dubbing with Neural Codec Language Models

Kim Sung-Bin, Jeongsoo Choi, Puyuan Peng +3

We present VoiceCraft-Dub, a novel approach for automated video dubbing that synthesizes high-quality speech from text and facial cues. This task has broad applications in filmmaki…

cs.CV2025

AlignDiT: Multimodal Aligned Diffusion Transformer for Synchronized Speech Generation

Jeongsoo Choi, Ji-Hoon Kim, Kim Sung-Bin +2

In this paper, we address the task of multimodal-to-speech generation, which aims to synthesize high-quality speech from multiple input modalities: text, video, and reference audio…

cs.CV2024

Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal Latent Alignment

Kim Sung-Bin, Arda Senocak, Hyunwoo Ha +1

How does audio describe the world around us? In this work, we propose a method for generating images of visual scenes from diverse in-the-wild sounds. This cross-modal generation t…

cs.CV2024★ 1 cited

AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models

Kim Sung-Bin, Oh Hyun-Bin, JungMok Lee +3

Following the success of Large Language Models (LLMs), expanding their boundaries to new modalities represents a significant paradigm shift in multimodal understanding. Human perce…

cs.CV2023

Sound Source Localization is All about Cross-Modal Alignment

Arda Senocak, Hyeonggon Ryu, Junsik Kim +3

Humans can easily perceive the direction of sound sources in a visual scene, termed sound source localization. Recent studies on learning-based sound source localization have mainl…