The paper introduces BiSID-AVS, a method that uses binaural audio cues to disambiguate which visual instance is actually producing sound when multiple same‑class objects are present. It grounds category‑consistent candidate regions with audio prompting, projects binaural interaural differences into a spatial response map, and refines the true sounding instance through dual‑level spatial conditioning. Experiments on binaurally extended AVS‑Synthetic and Habitat‑AVS datasets show significant gains in instance‑level mIoU and reduced false activation on non‑sounding same‑class regions.
arXiv:2609.32180v2 Announce Type: replace Abstract: Audio-visual segmentation (AVS) aims to segment sounding objects at the pixel level by integrating auditory and visual cues. However, existing meth...
arXiv:2608. 16285v1 Announce Type: cross Abstract: Audio-Visual Segmentation (AVS) is a fundamental task in multimodal perception that performs pixel-level segmentation of sounding objects in videos by leveraging both visual and audio cues.
BinauralVAE is an open‑source pipeline that reconstructs spatial audio using various Variational Autoencoder architectures, including complex‑valued variants, to learn latent representations of binaural signals. The project builds on realistic acoustic data from a simulated robot navigating an environment, providing a foundation for audio‑centric world models. It aims to map the causal link between navigational actions and their acoustic outcomes, positioning sound as a complementary modality for spatial awareness.
arXiv:2607. 15265v1 Announce Type: cross Abstract: We present SceneBind, an omni-modal representation of realistic scenes with joint semantic and 3D spatial understanding across vision, audio and language.
arXiv:2606. 02724v1 Announce Type: cross Abstract: Audio-visual speaker tracking aims to localize and track active speakers by leveraging auditory and visual cues, enabling fine-grained, human-centric scene understanding.
The paper introduces Shift-Aware Identity-guided Latent (SAIL) refinement for Referring Audio‑Visual Segmentation (Ref‑AVS), a task that requires precise segmentation of a specific object using both visual and auditory cues grounded in natural language. SAIL employs a dual‑token strategy to separate frame‑level dynamics from a global semantic anchor, a shift‑aware refinement module to correct target‑shift drift, and a decoder with continuous memory updates to preserve spatio‑temporal coherence and identity consistency. Experiments on two Ref‑AVS benchmarks show that SAIL outperforms existing AVS, Ref‑VOS, and Ref‑AVS methods.
The paper introduces SCR-V2A, a video-to-audio generation framework that focuses on Semantic-Critical Regions (SCRs)—localized visual cues directly related to sound events. SCR-V2A first identifies these regions via sound-related visual localization, then encodes them with an adapter-based dual visual encoder to produce a control representation that captures both global context and fine-grained sound evidence. This representation conditions an audio generation model, and experiments on in-domain and out-of-domain benchmarks show improved distribution matching and audio quality with few trainable parameters.
arXiv:2508. 00307v4 Announce Type: replace-cross Abstract: We introduce a U-net model for 360{\deg} acoustic source localization formulated as a spherical semantic segmentation task.
arXiv:2609.23407v1 Announce Type: cross Abstract: Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for em...
The paper introduces Frequency-Aware Audio-Visual Segmentation (FAVS), a framework that treats audio-visual segmentation as a frequency-domain decomposition and recomposition task. It comprises a Frequency-Domain Enhanced Decomposer (FDED) that iteratively separates modality-specific semantics and structural features, and a Synergistic Cross-Modal Consistency (SCMC) module that uses a mixture-of-experts architecture to maintain semantic consistency and preserve modality-specific features. Experiments on three benchmark datasets show that FAVS achieves state-of-the-art performance, with qualitative visualizations supporting the effectiveness of both modules.
arXiv:2609.23407v2 Announce Type: replace-cross Abstract: Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challengin...
arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has...