45 citations · 82 across the 16 of their papers we have counts for
9 papers · 1 filter
MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models
Xiaomin Li, Xu Jia, Qinghe Wang +5
Existing pretrained text-to-video (T2V) models have demonstrated impressive abilities in generating realistic videos with basic motion or camera movement. However, these models exh…
Autoregressive Video Generation without Vector Quantization
Haoge Deng, Ting Pan, Haiwen Diao +6
This paper presents a novel approach that enables autoregressive video generation with high efficiency. We propose to reformulate the video generation problem as a non-quantized au…
LLMs Can Evolve Continually on Modality for X-Modal Reasoning
Jiazuo Yu, Haomiao Xiong, Lu Zhang +7
Multimodal Large Language Models (MLLMs) have gained significant attention due to their impressive capabilities in multimodal understanding. However, existing methods rely heavily…
GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning
Haiwen Diao, Ying Zhang, Shang Gao +3
Cross-modal metric learning is a prominent research topic that bridges the semantic heterogeneity between vision and language. Existing methods frequently utilize simple cosine or…
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning
Haiwen Diao, Bo Wan, Xu Jia +4
Parameter-efficient transfer learning (PETL) has emerged as a flourishing research field for adapting large pre-trained models to downstream tasks, greatly reducing trainable param…
DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
Xiaotong Li, Fan Zhang, Haiwen Diao +3
Existing Multimodal Large Language Models (MLLMs) increasingly emphasize complex understanding of various visual elements, including multiple objects, text information, and spatial…
Unveiling Encoder-Free Vision-Language Models
Haiwen Diao, Yufeng Cui, Xiaotong Li +3
Existing vision-language models (VLMs) mostly rely on vision encoders to extract visual features followed by large language models (LLMs) for visual-language tasks. However, the vi…
Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
Haiwen Diao, Ying Zhang, Shang Gao +2
Image-text matching remains a challenging task due to heterogeneous semantic diversity across modalities and insufficient distance separability within triplets. Different from prev…
Exploring Dynamic Transformer for Efficient Object Tracking
Jiawen Zhu, Xin Chen, Haiwen Diao +6
The speed-precision trade-off is a critical problem for visual object tracking which usually requires low latency and deployment on constrained resources. Existing solutions for ef…