2 citations · 5 across the 10 of their papers we have counts for
12 papers
PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection
Kang Zhang, Wei Jian Lau, Shoushou Ren +3
Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks. However, we notice that they suffer from a…
Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions
Seongyu Kim, Seungwoo Lee, Hyeonggon Ryu +2
We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile metho…
Cinematic Audio Source Separation Using Visual Cues
Kang Zhang, Suyeon Lee, Arda Senocak +1
Cinematic Audio Source Separation (CASS) aims to decompose mixed film audio into speech, music, and sound effects, enabling applications like dubbing and remastering. Existing CASS…
Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision
Chenshuang Zhang, Kang Zhang, Joon Son Chung +3
Distinguishing visually similar objects by their motion remains a critical challenge in computer vision. Although supervised trackers show promise, contemporary self-supervised tra…
Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation
Kang Zhang, Trung X. Pham, Suyeon Lee +3
We present MGAudio, a novel flow-based framework for open-domain video-to-audio generation, which introduces model-guided dual-role alignment as a central design principle. Unlike…
MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence
Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar +31
Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio unde…
Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization
Sooyoung Park, Arda Senocak, Joon Son Chung
Large-scale vision-language models demonstrate strong multimodal alignment and generalization across diverse tasks. Among them, CLIP stands out as one of the most successful approa…
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…
Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment
Arda Senocak, Hyeonggon Ryu, Junsik Kim +3
Recent studies on learning-based sound source localization have mainly focused on the localization performance perspective. However, prior work and existing benchmarks overlook a c…
ElasticAST: An Audio Spectrogram Transformer for All Length and Resolutions
Jiu Feng, Mehmet Hamza Erol, Joon Son Chung +1
Transformers have rapidly overtaken CNN-based architectures as the new standard in audio classification. Transformer-based models, such as the Audio Spectrogram Transformers (AST),…
Audio Mamba: Bidirectional State Space Model for Audio Representation Learning
Mehmet Hamza Erol, Arda Senocak, Jiu Feng +1
Transformers have rapidly become the preferred choice for audio classification, surpassing methods based on CNNs. However, Audio Spectrogram Transformers (ASTs) exhibit quadratic s…
Can CLIP Help Sound Source Localization?
Sooyoung Park, Arda Senocak, Joon Son Chung
Large-scale pre-trained image-text models demonstrate remarkable versatility across diverse tasks, benefiting from their robust representational capabilities and effective multimod…