69 citations · 133 across the 34 of their papers we have counts for
13 papers · 1 filter
Parameter Aware Mamba Model for Multi-task Dense Prediction
Xinzhuo Yu, Yunzhi Zhuge, Sitong Gong +3
Understanding the inter-relations and interactions between tasks is crucial for multi-task dense prediction. Existing methods predominantly utilize convolutional layers and attenti…
VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning
Baolu Li, Yiming Zhang, Qinghe Wang +8
Visual effects (VFX) are crucial to the expressive power of digital media, yet their creation remains a major challenge for generative AI. Prevailing methods often rely on the one-…
FineRS: Fine-grained Reasoning and Segmentation of Small Objects with Reinforcement Learning
Lu Zhang, Jiazuo Yu, Haomiao Xiong +4
Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities across a wide range of vision-language tasks. However, due to the restricted input resolutions, MLLMs f…
Complementary and Contrastive Learning for Audio-Visual Segmentation
Sitong Gong, Yunzhi Zhuge, Lu Zhang +2
Audio-Visual Segmentation (AVS) aims to generate pixel-wise segmentation maps that correlate with the auditory signals of objects. This field has seen significant progress with num…
Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking
Zirui Zheng, Takashi Isobe, Tong Shen +12
Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the…
Reinforcing Video Reasoning Segmentation to Think Before It Segments
Sitong Gong, Lu Zhang, Yunzhi Zhuge +3
Video reasoning segmentation (VRS) endeavors to delineate referred objects in videos guided by implicit instructions that encapsulate human intent and temporal logic. Previous appr…
Regularizing Subspace Redundancy of Low-Rank Adaptation
Yue Zhu, Haiwen Diao, Shang Gao +8
Low-Rank Adaptation (LoRA) and its variants have delivered strong capability in Parameter-Efficient Transfer Learning (PETL) by minimizing trainable parameters and benefiting from…
Learning Universal Features for Generalizable Image Forgery Localization
Hengrun Zhao, Yunzhi Zhuge, Yifan Wang +3
In recent years, advanced image editing and generation methods have rapidly evolved, making detecting and locating forged image content increasingly challenging. Most existing imag…
Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge
Haomiao Xiong, Zongxin Yang, Jiazuo Yu +4
Recent advances in Large Language Models (LLMs) have enabled the development of Video-LLMs, advancing multimodal learning by bridging video data with language tasks. However, curre…
The Devil is in Temporal Token: High Quality Video Reasoning Segmentation
Sitong Gong, Yunzhi Zhuge, Lu Zhang +3
Existing methods for Video Reasoning Segmentation rely heavily on a single special token to represent the object in the keyframe or the entire video, inadequately capturing spatial…
3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene Understanding
Haomiao Xiong, Yunzhi Zhuge, Jiawen Zhu +2
Multi-modal Large Language Models (MLLMs) exhibit impressive capabilities in 2D tasks, yet encounter challenges in discerning the spatial positions, interrelations, and causal logi…
AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation
Sitong Gong, Yunzhi Zhuge, Lu Zhang +4
The essence of audio-visual segmentation (AVS) lies in locating and delineating sound-emitting objects within a video stream. While Transformer-based methods have shown promise, th…
Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
Yunzhi Zhuge, Hongyu Gu, Lu Zhang +2
In this paper, we address the challenges in unsupervised video object segmentation (UVOS) by proposing an efficient algorithm, termed MTNet, which concurrently exploits motion and…