32 citations · 43 across the 13 of their papers we have counts for
13 papers
OSV: One Step is Enough for High-Quality Image to Video Generation
Xiaofeng Mao, Zhengkai Jiang, Fu-Yun Wang +5
Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus. However, their inherent iterative nature leads to su…
MambaGesture: Enhancing Co-Speech Gesture Generation with Mamba and Disentangled Multi-Modality Fusion
Chencan Fu, Yabiao Wang, Jiangning Zhang +7
Co-speech gesture generation is crucial for producing synchronized and realistic human gestures that accompany speech, enhancing the animation of lifelike avatars in virtual enviro…
MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation
Xiaofeng Mao, Zhengkai Jiang, Qilin Wang +7
Recent advancements in the field of Diffusion Transformers have substantially improved the generation of high-quality 2D images, 3D videos, and 3D shapes. However, the effectivenes…
NoiseBoost: Alleviating Hallucination with Noise Perturbation for Multimodal Large Language Models
Kai Wu, Boyuan Jiang, Zhengkai Jiang +5
Multimodal large language models (MLLMs) contribute a powerful mechanism to understanding visual information building on large language models. However, MLLMs are notorious for suf…
VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation
Qilin Wang, Zhengkai Jiang, Chengming Xu +7
Human image animation involves generating a video from a static image by following a specified pose sequence. Current approaches typically adopt a multi-stage pipeline that separat…
AdapNet: Adaptive Noise-Based Network for Low-Quality Image Retrieval
Sihe Zhang, Qingdong He, Jinlong Peng +6
Image retrieval aims to identify visually similar images within a database using a given query image. Traditional methods typically employ both global and local features extracted…
Efficient Multimodal Large Language Models: A Survey
Yizhang Jin, Jian Li, Yexin Liu +10
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.…
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Xiaojun Hou, Jiazheng Xing, Yijie Qian +8
Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers, which was i…
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
Liren He, Zhengkai Jiang, Jinlong Peng +7
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of "learning shortcuts", where…
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
Qingdong He, Jinlong Peng, Zhengkai Jiang +2
Recent success of vision foundation models have shown promising performance for the 2D perception tasks. However, it is difficult to train a 3D foundation network directly due to t…
DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
Xiaobin Hu, Xu Peng, Donghao Luo +7
Due to the difficulty and labor-consuming nature of getting highly accurate or matting annotations, there only exists a limited amount of highly accurate labels available to the pu…
UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation
Qingdong He, Jinlong Peng, Zhengkai Jiang +7
3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space. However, existing works not only fail to fully utilize all the avai…
Density Matters: Improved Core-set for Active Domain Adaptive Segmentation
Shizhan Liu, Zhengkai Jiang, Yuxi Li +3
Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation. However, existing works…