2 citations · 3 across the 12 of their papers we have counts for
13 papers
TTTIR: Unlocking Instance-Specific State Evolution via Test-Time Training for Image Restoration
Kaihang Zheng, Jun Li, Hang Guo +5
Image restoration is inherently challenging due to the diverse and highly input-dependent nature of real-world degradations. While recent architectures like Transformers and state-…
PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding
Xiang Liu, Jinxiang Wang, Bin Chen +5
Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years.Compared to conventional end-to-end image codec…
LoKiFormer: Locality-aware Attention with Decoupled Knowledge Memory for Efficient Large Language Model Pretraining
Qiuwu Chen, Zimo Liu, Yuchen Li +8
Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limi…
Zeta: Dual Whitening for Matrix Optimization via Coordinate-Adaptive Preconditioning
Kaiwen Chen, Shuhai Zhang, Zimo Liu +7
Large-scale neural network training increasingly relies on matrix-aware optimizers that exploit the structure of weight parameters beyond element-wise adaptation. However, existing…
CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks
Mingshuang Luo, Ruibing Hou, Bo Chao +4
Human-centric visual analysis plays a pivotal role in diverse applications, including surveillance, healthcare, and human-computer interaction. With the emergence of large-scale un…
UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person Retrieval
Yating Liu, Yaowei Li, Xiangyuan Lan +3
Text-based Person Retrieval (TPR) as a multi-modal task, which aims to retrieve the target person from a pool of candidate images given a text description, has recently garnered co…
DM-Adapter: Domain-Aware Mixture-of-Adapters for Text-Based Person Retrieval
Yating Liu, Zimo Liu, Xiangyuan Lan +3
Text-based person retrieval (TPR) has gained significant attention as a fine-grained and challenging task that closely aligns with practical applications. Tailoring CLIP to person…
Morph: A Motion-free Physics Optimization Framework for Human Motion Generation
Zhuo Li, Mingshuang Luo, Ruibing Hou +5
Human motion generation has been widely studied due to its crucial role in areas such as digital humans and humanoid robot control. However, many current motion generation approach…
Pre-Trained Vision-Language Models as Partial Annotators
Qian-Wei Wang, Yuqiu Xie, Letian Zhang +2
Pre-trained vision-language models learn massive data to model unified representations of images and natural languages, which can be widely applied to downstream machine learning t…
MGPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
Mingshuang Luo, Ruibing Hou, Zhuo Li +4
This paper presents MGPT, an advanced ultimodal, ultitask framework for otion comprehension and generation. MGPT operates on three funda…
An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding
Xiang Liu, Jiahong Chen, Bin Chen +4
Displaying high-quality images on edge devices, such as augmented reality devices, is essential for enhancing the user experience. However, these devices often face power consumpti…
CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval
Yating Liu, Yaowei Li, Zimo Liu +3
Text-based Person Retrieval (TPR) aims to retrieve the target person images given a textual query. The primary challenge lies in bridging the substantial gap between vision and lan…
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data
Qian-Wei Wang, Bowen Zhao, Mingyan Zhu +3
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid. In recent years, benefiting from the stron…