The paper introduces a novel four‑stream complex‑valued architecture for Open‑Vocabulary Audio‑Visual Event Localization (OV‑AVEL). By embedding visual, audio, and textual modalities into a shared space and computing complex‑valued similarities—real parts from standard embeddings and imaginary parts from companion streams (iHSV for vision and CycleGAN‑translated phase for audio)—the method learns fusion via a complex‑valued neural network while keeping encoders frozen. This approach achieves new state‑of‑the‑art results on OV‑AVEBench and a modified AVE dataset, outperforming previous fine‑tuned baselines by significant margins. "whyItMatters":"The proposed complex‑valued fusion strategy demonstrates that incorporating phase information and learning fusion in the complex domain can substantially improve open‑vocabulary event localization performance, setting new benchmarks for the task."
arXiv:2609.23376v1 Announce Type: cross Abstract: Open-vocabulary audio-visual event localization (OV-AVEL) grounds a text-queried event in time from video, audio, and language. The supervision sourc...
arXiv:2607. 10299v1 Announce Type: new Abstract: Recent advances in large-scale multimodal models have drivenremarkable progress in vision-language tasks; however, comprehensiveomni-modal understanding remains under-explored, largely due to thescarcity of datasets with rich, explicitly aligned auditory cues.
TiTok is an audio‑visual large language model designed for multi‑segment temporal grounding in untrimmed videos. It introduces Time Token Interleaving (TTI) to align temporal perception with prediction, and uses decoupled reinforcement‑learning rewards (global, local, count, precision, format) optimized via GDPO. Evaluated on a new UnAV‑100 protocol with the CountF1 metric, TiTok achieves state‑of‑the‑art results (65.7 mIoU, 0.58 CountF1).
The paper introduces ACF-Net, an optical flow‑guided framework for asymmetric audio‑visual fine‑grained visual categorization (FGVC), addressing challenges where video and audio are not strictly synchronized or matched. ACF-Net comprises Optical Flow‑Guided Motion (OFGM) to capture motion‑sensitive visual cues and suppress background noise, and Asymmetric Cross‑Modal Adaptive Fusion (ACAF) to estimate modality reliability and perform uncertainty‑aware fusion. The authors also present BirdPro, a new bird‑oriented audio‑visual benchmark with 1,919 audio recordings and 11,965 videos across 194 species, and report that ACF‑Net outperforms baselines by 2.97% in fused and 1.92% in mismatched settings.
arXiv:2610.08192v1 Announce Type: new Abstract: Audio-visual models improve egocentric action recognition by exploiting complementary cues, yet typically assume that both streams remain available at...
arXiv:2606. 25225v1 Announce Type: cross Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning.
arXiv:2606. 07033v1 Announce Type: new Abstract: Open-vocabulary audio-visual event localization (OV-AVEL) jointly models audio-visual cues to recognize and temporally localize events, including categories unseen during training.
arXiv:2608. 09435v1 Announce Type: new Abstract: Understanding dynamic sound sources requires jointly determining what produces a sound, where the source is located, and how it moves over time.
arXiv:2606. 02679v1 Announce Type: new Abstract: Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed.
arXiv:2606. 07643v1 Announce Type: cross Abstract: Recent advances in Omni-Multimodal Large Language Models (Omni-MLLMs) have enabled strong integration of vision, audio, and language.
LEAP is a framework for long audio‑video question answering that avoids encoding entire recordings by dividing them into fixed‑duration blocks. It performs a lightweight localization pass on each block to score short candidate windows, then pools the highest‑ranked windows for a single bounded answer pass, keeping the answer input and peak context independent of recording length. The method trains both a localization LoRA and an answer LoRA, supports causal streaming queries, and achieves significant performance gains over baseline models on multiple AVQA benchmarks.
SONIC‑O1 is a new benchmark designed to evaluate multimodal large language models on audio‑video understanding. It contains 60 hours of 231 clips across 13 real‑world conversational domains, with 4,958 human‑verified annotations and demographic metadata. The benchmark tests open‑ended summarization, multiple‑choice question answering, and temporally grounded reasoning, revealing performance gaps between model families and across demographic groups.