1 citations · 2 across the 12 of their papers we have counts for
12 papers
VIBE: Video Instruction-aligned Background music gEneration
Aryan Vijay Bhosale, Vaibhavi Lokegaonkar, Vishnu Raj +5
Current video-to-music (V2M) models lack semantic control and fail to penalize instruction violations, largely due to their reliance on reconstruction objectives and the representa…
Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation
Vaibhavi Lokegaonkar, Aryan Vijay Bhosale, Vishnu Raj +5
Video-to-music (V2M) is the fundamental task of creating background music for an input video. Recent V2M models achieve audiovisual alignment by typically relying on visual conditi…
Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection
Meng Chen, Kun Wang, Li Lu +2
Modern Large audio-language models (LALMs) power intelligent voice interactions by tightly integrating audio and text. This integration, however, expands the attack surface beyond…
Variable-Length Audio Fingerprinting
Hongjie Chen, Hanyu Meng, Huimin Zeng +3
Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints.…
Towards Sparse Video Understanding and Reasoning
Chenwei Xu, Zhen Ye, Shang Wu +8
We present \revise (\underline{Re}asoning with \underline{Vi}deo \underline{S}parsity), a multi-round agent for video question answering (VQA). Instead of uniformly sampling frames…
SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models
S Sakshi, Vaibhavi Lokegaonkar, Neil Zhang +4
Spatial perception is central to auditory intelligence, enabling accurate understanding of real-world acoustic scenes and advancing human-level perception of the world around us. W…
Transformation of audio embeddings into interpretable, concept-based representations
Alice Zhang, Edison Thomaz, Lie Lu
Advancements in audio neural networks have established state-of-the-art results on downstream audio tasks. However, the black-box structure of these models makes it difficult to in…
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
Yixin Liu, Lie Lu, Jihui Jin +2
The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data provenance, and the spread of misinfor…
Semi-Supervised Contrastive Learning for Controllable Video-to-Music Retrieval
Shanti Stewart, Gouthaman KV, Lie Lu +1
Content creators often use music to enhance their videos, from soundtracks in movies to background music in video blogs and social media content. However, identifying the best musi…
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning
Eloy Geenjaar, Lie Lu
Transfer learning for bio-signals has recently become an important technique to improve prediction performance on downstream tasks with small bio-signal datasets. Recent works have…
Audio Match Cutting: Finding and Creating Matching Audio Transitions in Movies and Videos
Dennis Fedorishin, Lie Lu, Srirangaraj Setlur +1
A "match cut" is a common video editing technique where a pair of shots that have a similar composition transition fluidly from one to another. Although match cuts are often visual…
RapVerse: Coherent Vocals and Whole-Body Motions Generations from Text
Jiaben Chen, Xin Yan, Yihang Chen +7
In this work, we introduce a challenging task for simultaneously generating 3D holistic body motions and singing vocals directly from textual lyrics inputs, advancing beyond existi…