collaborators

6 papers

cs.CV2025

CLASP: Cross-modal Salient Anchor-based Semantic Propagation for Weakly-supervised Dense Audio-Visual Event Localization

Jinxing Zhou, Ziheng Zhou, Yanghao Zhou +3

The Dense Audio-Visual Event Localization (DAVEL) task aims to temporally localize events in untrimmed videos that occur simultaneously in both the audio and visual modalities. Thi…

cs.CV2025

Autoregressive Image Generation with Linear Complexity: A Spatial-Aware Decay Perspective

Yuxin Mao, Zhen Qin, Jinxing Zhou +6

Autoregressive (AR) models have garnered significant attention in image generation for their ability to effectively capture both local and global structures within visual data. How…

cs.CV2025

Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptation

Jinxing Zhou, Zhihui Li, Yongqiang Yu +7

We present \textbf{Met}a-\textbf{T}oken \textbf{Le}arning (Mettle), a simple and memory-efficient method for adapting large-scale pretrained transformer models to downstream audio-…

cs.CV2025

Towards Open-Vocabulary Audio-Visual Event Localization

Jinxing Zhou, Dan Guo, Ruohao Guo +5

The Audio-Visual Event Localization (AVEL) task aims to temporally locate and classify video events that are both audible and visible. Most research in this field assumes a closed-…

cs.CL2025

MiniMax-01: Scaling Foundation Models with Lightning Attention

MiniMax, Aonian Li, Bangwei Gong +87

We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer conte…

cs.CV2024

Tri-Ergon: Fine-grained Video-to-Audio Generation with Multi-modal Conditions and LUFS Control

Bingliang Li, Fengyu Yang, Yuxin Mao +3

Video-to-audio (V2A) generation utilizes visual-only video features to produce realistic sounds that correspond to the scene. However, current V2A models often lack fine-grained co…