1 citations · 1 across the 12 of their papers we have counts for
13 papers
SaD: A Scenario-Aware Discriminator for Speech Enhancement
Xihao Yuan, Siqi Liu, Yan Chen +4
Generative adversarial network-based models have shown remarkable performance in the field of speech enhancement. However, the current optimization strategies for these models pred…
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
Zhijun Tu, Jian Li, Yuanyuan Xi +5
1-bit LLM quantization offers significant advantages in reducing storage and computational costs. However, existing methods typically train 1-bit LLMs from scratch, failing to full…
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution
Haizhen Xie, Kunpeng Du, Qiangyu Yan +5
Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. While T2I models have traditionally…
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution
Miaomiao Cai, Simiao Li, Wei Li +4
Recent advances in diffusion models have improved Real-World Image Super-Resolution (Real-ISR), but existing methods lack human feedback integration, risking misalignment with huma…
Autoregressive Image Generation with Vision Full-view Prompt
Miaomiao Cai, Guanjie Wang, Wei Li +4
In autoregressive (AR) image generation, models based on the 'next-token prediction' paradigm of LLMs have shown comparable performance to diffusion models by reducing inductive bi…
Dynamic Frequency-Adaptive Knowledge Distillation for Speech Enhancement
Xihao Yuan, Siqi Liu, Hanting Chen +3
Deep learning-based speech enhancement (SE) models have recently outperformed traditional techniques, yet their deployment on resource-constrained devices remains challenging due t…
Learning Quantized Adaptive Conditions for Diffusion Models
Yuchen Liang, Yuchuan Tian, Lei Yu +4
The curvature of ODE trajectories in diffusion models hinders their ability to generate high-quality images in a few number of function evaluations (NFE). In this paper, we propose…
One Step Diffusion-based Super-Resolution with Time-Aware Distillation
Xiao He, Huaao Tang, Zhijun Tu +8
Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these a…
Multi-Granularity Semantic Revision for Large Language Model Distillation
Xiaoyu Liu, Yun Zhang, Wei Li +7
Knowledge distillation plays a key role in compressing the Large Language Models (LLMs), which boosts a small-size student model under large teacher models' guidance. However, exis…
GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
Yirui Chen, Xudong Huang, Quan Zhang +9
The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia…
Collaboration of Teachers for Semi-supervised Object Detection
Liyu Chen, Huaao Tang, Yi Wen +4
Recent semi-supervised object detection (SSOD) has achieved remarkable progress by leveraging unlabeled data for training. Mainstream SSOD methods rely on Consistency Regularizatio…
LIPT: Latency-aware Image Processing Transformer
Junbo Qiao, Wei Li, Haizhen Xie +5
Transformer is leading a trend in the field of image processing. Despite the great success that existing lightweight image processing transformers have achieved, they are tailored…
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
Simiao Li, Yun Zhang, Wei Li +5
Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher mo…