3 citations · 9 across the 12 of their papers we have counts for
12 papers
Spatial Temporal Synergy: Balancing Change and Invariance in Text Driven 3D Human Motion Editing
Shaohui Lin, Zhenwu Shi, Jingyu Gong +5
Text-driven human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the structural consistency of the original mo…
CSD: Content-aware Speculative Decoding for Efficient Image Generation
Mingcheng Wang, Junbo Qiao, Yunchen Li +8
Speculative decoding (SD) has emerged as a key solution to accelerate the inference of autoregressive models. However, in the field of image generation, it faces the challenge of l…
Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning
Zhenwu Shi, Jingyu Gong, Peiwei Wang +7
Text-based human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the consistency of the original motion. Existi…
The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Jiatong Li, Zheng Chen, Kai Liu +91
This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge…
Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression
Yuntian Tang, Bohan Jia, Wenxuan Huang +7
Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computational overhead for inference. E…
MASA: Rethinking the Representational Bottleneck in LoRA with Multi-A Shared Adaptation
Qin Dong, Yuntian Tang, Heming Jia +7
Low-Rank Adaptation (LoRA) has emerged as a dominant method in Parameter-Efficient Fine-Tuning (PEFT) for large language models, which augments the transformer layer with one down-…
RealSR-R1: Reinforcement Learning for Real-World Image Super-Resolution with Vision-Language Chain-of-Thought
Junbo Qiao, Miaomiao Cai, Wei Li +5
Real-World Image Super-Resolution is one of the most challenging task in image restoration. However, existing methods struggle with an accurate understanding of degraded image cont…
Dynamic Contrastive Knowledge Distillation for Efficient Image Restoration
Yunshuai Zhou, Junbo Qiao, Jincheng Liao +6
Knowledge distillation (KD) is a valuable yet challenging approach that enhances a compact student network by learning from a high-performance but cumbersome teacher model. However…
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution
Junbo Qiao, Jincheng Liao, Wei Li +7
State Space Models (SSM), such as Mamba, have shown strong representation ability in modeling long-range dependency with linear complexity, achieving successful applications from h…
A General and Efficient Training for Transformer via Token Expansion
Wenxuan Huang, Yunhang Shen, Jiao Xie +5
The remarkable performance of Vision Transformers (ViTs) typically requires an extremely large training cost. Existing methods have attempted to accelerate the training of ViTs, ye…
Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler
Shaohui Lin, Wenxuan Huang, Jiao Xie +5
Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this pape…
Training convolutional neural networks with cheap convolutions and online distillation
Jiao Xie, Shaohui Lin, Yichen Zhang +1
The large memory and computation consumption in convolutional neural networks (CNNs) has been one of the main barriers for deploying them on resource-limited systems. To this end,…