ARGenSeg introduces an autoregressive generation-based approach for image segmentation that integrates seamlessly with multimodal large language models (MLLMs). Unlike prior methods that use boundary points or dedicated segmentation heads, ARGenSeg generates dense masks directly through visual token output and detokenization via a universal VQ‑VAE, enabling fine‑grained pixel‑level perception. The framework employs a next‑scale‑prediction strategy to parallelize token generation, resulting in faster inference while outperforming state‑of‑the‑art segmentation models on multiple datasets.
By Xiaolong Wang, Lixiang Ru, Ziyuan Huang, Kaixiang Ji, Dandan Zheng, Jingdong Chen, Jun Zhou
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.
By Yaoting Wang, Yun Zhou, Zipei Zhang, Henghui Ding
arXiv:2501.04001v4 Announce Type: replace
Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...
By Haobo Yuan, Xiangtai Li, Tao Zhang, Yueyi Sun, Zilong Huang, Shilin Xu, Shunping Ji, Yunhai Tong, Lu Qi, Jiashi Feng, Ming-Hsuan Yang
arXiv:2608.22337v1 Announce Type: cross
Abstract: Speech-guided referring video object segmentation aims to recover the mask tracks of objects specified by a spoken motion description. Here, speech c...
By Jinxing Zhou, Suiyi Zhao, Yanghao Zhou, Ruohao Guo
The paper introduces a new task called video object segmentation‑aware audio generation, which conditions sound synthesis on object‑level segmentation maps. It presents SAGANet, a multimodal generative model that uses visual segmentation masks, video, and textual cues to produce controllable audio for musical instruments, offering fine‑grained, visually localized control. The authors also release the Segmented Music Solos dataset of instrument performance videos with segmentation information to support this task and demonstrate that SAGANet outperforms current state‑of‑the‑art methods in controllable, high‑fidelity Foley synthesis.
By Ilpo Viertola, Vladimir Iashin, Esa Rahtu
Audio Description (AD) provides spoken narration of visual events during dialogue gaps, making movies accessible to visually impaired audiences. The problem requires determining both what (which visua...
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.
By Kaiying Yan, Luoyi Sun, Xiao Zhou, Weidi Xie
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.
By Zhaojin Fu, Yuyang Hong, Qi Yang, Zili Wang, Kun Ding, Shiming Xiang, Bin Fan
The paper introduces Cue2Narrate, a two‑stage pipeline that jointly predicts what visual events to narrate and when to insert the narration in long, untrimmed movie clips. It uses a dual‑head audio‑visual localizer to identify visual cue and narration windows, followed by a LoRA‑adapted vision‑language model that generates concise audio descriptions, trained with a Description Ranking Loss. The authors also present the LongLSMDC benchmark, comprising up to 8‑minute clips, and show that Cue2Narrate outperforms video‑only and audio‑only baselines by 5–12 points in average mAP and improves AD generation over fine‑tuned base VLMs.
By Akshita Gupta, Aditya Arora, Federico Tombari, Marcus Rohrbach, Anna Rohrbach
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.
By Yaoting Wang, Ziyi Zhang, Wenming Tu, Shaoxuan Xu, Wenjie Du, Cheng Liang, Weijun Wang, Yuanchao Li, Guangyao Li, Hao Fei, Yuanchun Li, Henghui Ding, Yunxin Liu
Recent advances in generative video modeling have enabled diverse generation, reference-based synthesis, extension, and editing, but existing approaches often rely on fragmented task-specific models. A general model must distinguish heterogeneous target, source, and reference signals to determine what to generate, preserve, or use as guidance, while reducing interference among tasks.
arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.
By Bingyu Li, Da Zhang, Tao Huo, Zhiyuan Zhao, Junyu Gao, Xuelong Li