# QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition

arXiv: https://arxiv.org/abs/2310.00132 · HTML: https://mlsanity.com/paper/2310.00132/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 82 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 30 of 57)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions](https://mlsanity.com/paper/2308.08544/) (2023)
- [Associating Objects with Transformers for Video Object Segmentation](https://mlsanity.com/paper/2106.02638/) (2021)
- [Emergence of Invariance and Disentanglement in Deep Representations](https://mlsanity.com/paper/1706.01350/) (2017)
- [Segment and Track Anything](https://mlsanity.com/paper/2305.06558/) (2023)
- [Learning to Localize Sound Source in Visual Scenes](https://mlsanity.com/paper/1803.03849/) (2018)
- [Self-Supervised Learning of Audio-Visual Objects from Video](https://mlsanity.com/paper/2008.04237/) (2020)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Language as Queries for Referring Video Object Segmentation](https://mlsanity.com/paper/2201.00487/) (2022)
- [Mask2Former for Video Instance Segmentation](https://mlsanity.com/paper/2112.10764/) (2021)
- [Localizing Visual Sounds the Hard Way](https://mlsanity.com/paper/2104.02691/) (2021)
- [Audio-Visual Segmentation](https://mlsanity.com/paper/2207.05042/) (2022)
- [End-to-End Referring Video Object Segmentation with Multimodal Transformers](https://mlsanity.com/paper/2111.14821/) (2021)
- [Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion](https://mlsanity.com/paper/2103.07941/) (2021)
- [Multiple Sound Sources Localization from Coarse to Fine](https://mlsanity.com/paper/2007.06355/) (2020)
- [Kernelized Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2007.08270/) (2020)
- [SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation](https://mlsanity.com/paper/2101.08833/) (2021)
- [Efficient Video Object Segmentation via Network Modulation](https://mlsanity.com/paper/1802.01218/) (2018)

## Built on by (most cited first, 20 of 20)

- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [Image Tokenizer Needs Post-Training](https://mlsanity.com/paper/2509.12474/) (2025)
- [CPM: Class-conditional Prompting Machine for Audio-visual Segmentation](https://mlsanity.com/paper/2407.05358/) (2024)
- [Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis](https://mlsanity.com/paper/2503.08354/) (2025)
- [Dynamic Derivation and Elimination: Audio Visual Segmentation with Enhanced Audio Semantics](https://mlsanity.com/paper/2503.12840/) (2025)
- [Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment](https://mlsanity.com/paper/2503.12847/) (2025)
- [R2-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations](https://mlsanity.com/paper/2403.04924/) (2024)
- [AVESFormer: Efficient Transformer Design for Real-Time Audio-Visual Segmentation](https://mlsanity.com/paper/2408.01708/) (2024)
- [Do Audio-Visual Segmentation Models Truly Segment Sounding Objects?](https://mlsanity.com/paper/2502.00358/) (2025)
- [Revisiting Audio-Visual Segmentation with Vision-Centric Transformer](https://mlsanity.com/paper/2506.23623/) (2025)
- [Implicit Counterfactual Learning for Audio-Visual Segmentation](https://mlsanity.com/paper/2507.20740/) (2025)
- [Delayed Bidirectional Alignment via Disentangled Audio Semantics for Audio-Visual Segmentation](https://mlsanity.com/paper/2512.20117/) (2025)
- [Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation](https://mlsanity.com/paper/2509.18912/) (2025)
- [SeaVIS: Sound-Enhanced Association for Online Audio-Visual Instance Segmentation](https://mlsanity.com/paper/2603.01431/) (2026)
- [Selective Noise Suppression and Discriminative Mutual Interaction for Robust Audio-Visual Segmentation](https://mlsanity.com/paper/2603.14203/) (2026)
- [Training-free CryoET Tomogram Segmentation](https://mlsanity.com/paper/2407.06833/) (2024)
- [The 3D Mirage: Probing and Taming 3D Hallucinations](https://mlsanity.com/paper/2512.15423/) (2025)
- [PRIMED: Adaptive Modality Suppression for Referring Audio-Visual Segmentation via Biased Competition](https://mlsanity.com/paper/2605.07154/) (2026)
- [Audio-Visual Segmentation via Depth-Guided Collaborative Modeling](https://mlsanity.com/paper/2608.16285/) (2026)
- [SlotAVS: Object-Centric Audio-Visual Segmentation via Bi-Modal Slot Attention](https://mlsanity.com/paper/2610.15764/) (2026)

## Abstract (arXiv)

Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and background disturbances involved, establishing robust correspondences between audio and visual contents poses unique challenges due to (1) complex entanglement across sound sources and (2) frequent changes in the occurrence of distinct sound events. Assuming sound events occur independently, the multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We are motivated to decompose the multi-source audio semantics into single-source semantics for more effective interactions with visual content. We propose a semantic decomposition method based on product quantization, where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics. Furthermore, we introduce a global-to-local quantization mechanism, which distills knowledge from stable global (clip-level) features into local (frame-level) ones, to handle frequent changes in audio semantics. Extensive experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance, e.g., +21.2% mIoU on the challenging AVS-Semantic benchmark with ResNet50 backbone. https://github.com/lxa9867/QSD.