23 citations · 59 across the 14 of their papers we have counts for
15 papers
CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment
Edson Araujo, Andrew Rouditchenko, Yuan Gong +7
Recent advances in audio-visual learning have shown promising results in learning representations across modalities. However, most approaches rely on global audio representations t…
Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
Andrew Rouditchenko, Saurabhchand Bhati, Edson Araujo +4
We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni, on an audio question answering dataset with the reinforcement learning method GRPO. This leads to new St…
mWhisper-Flamingo for Multilingual Audio-Visual Noise-Robust Speech Recognition
Andrew Rouditchenko, Samuel Thomas, Hilde Kuehne +2
Audio-Visual Speech Recognition (AVSR) combines lip-based video with audio and can improve performance in noise, but most methods are trained only on English data. One limitation i…
Whisper-Flamingo: Integrating Visual Features into Whisper for Audio-Visual Speech Recognition and Translation
Andrew Rouditchenko, Yuan Gong, Samuel Thomas +4
Audio-Visual Speech Recognition (AVSR) uses lip-based video to improve performance in noise. Since videos are harder to obtain than audio, the video training data of AVSR models is…
Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages
Andrew Rouditchenko, Sameer Khurana, Samuel Thomas +6
Recent models such as XLS-R and Whisper have made multilingual speech technologies more accessible by pre-training on audio from around 100 spoken languages each. However, there ar…
What, when, and where? -- Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
Brian Chen, Nina Shvetsova, Andrew Rouditchenko +6
Spatio-temporal grounding describes the task of localizing events in space and time, e.g., in video data, based on verbal descriptions only. Models for this task are usually traine…
Contrastive Audio-Visual Masked Autoencoder
Yuan Gong, Andrew Rouditchenko, Alexander H. Liu +4
In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Vi…
C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval
Andrew Rouditchenko, Yung-Sung Chuang, Nina Shvetsova +7
Multilingual text-video retrieval methods have improved significantly in recent years, but the performance for other languages lags behind English. We propose a Cross-Lingual Cross…
UAVM: Towards Unifying Audio and Visual Models
Yuan Gong, Alexander H. Liu, Andrew Rouditchenko +1
Conventional audio-visual models have independent audio and video branches. In this work, we unify the audio and visual branches by designing a Unified Audio-Visual Model (UAVM). T…
CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification
Yuan Gong, Sameer Khurana, Andrew Rouditchenko +1
Audio classification is an active research area with a wide range of applications. Over the past decade, convolutional neural networks (CNNs) have been the de-facto standard buildi…
Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval
Nina Shvetsova, Brian Chen, Andrew Rouditchenko +6
Multi-modal learning from video data has seen increased attention recently as it allows to train semantically meaningful embeddings without human annotation enabling tasks like zer…
Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content
Matthew Katsaros, Kathy Yang, Lauren Fratamico
The proliferation of harmful and offensive content is a problem that many online platforms face today. One of the most common approaches for moderating offensive content online is…
Cascaded Multilingual Audio-Visual Learning from Videos
Andrew Rouditchenko, Angie Boggust, David Harwath +8
In this paper, we explore self-supervised audio-visual models that learn from instructional videos. Prior work has shown that these models can relate spoken words and sounds to vis…
Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset
Ian Palmer, Andrew Rouditchenko, Andrei Barbu +2
Visually-grounded spoken language datasets can enable models to learn cross-modal correspondences with very weak supervision. However, modern audio-visual datasets contain biases t…
Cross-Modal Discrete Representation Learning
Alexander H. Liu, SouYoung Jin, Cheng-I Jeff Lai +3
Recent advances in representation learning have demonstrated an ability to represent information from different modalities such as video, text, and audio in a single high-level emb…