6 citations · 54 across the 65 of their papers we have counts for
32 papers · 1 filter
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning
Chunpu Liu, Guanglei Yang, Wangmeng Zuo +1
Deep metric learning aims to learn features relying on the consistency or divergence of class labels. However, in monocular depth estimation, the absence of a natural definition of…
Multi-Modality Driven LoRA for Adverse Condition Depth Estimation
Guanglei Yang, Rui Tian, Yongqiang Zhang +3
The autonomous driving community is increasingly focused on addressing corner case problems, particularly those related to ensuring driving safety under adverse conditions (e.g., n…
Unprejudiced Training Auxiliary Tasks Makes Primary Better: A Multi-Task Learning Perspective
Yuanze Li, Chun-Mei Feng, Qilong Wang +2
Human beings can leverage knowledge from relative tasks to improve learning on a primary task. Similarly, multi-task learning methods suggest using auxiliary tasks to enhance a neu…
Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation
Chun-Mei Feng, Yuanyang He, Jian Zou +6
Existing test-time prompt tuning (TPT) methods focus on single-modality data, primarily enhancing images and using confidence ratings to filter out inaccurate images. However, whil…
MR-GDINO: Efficient Open-World Continual Object Detection
Bowen Dong, Zitong Huang, Guanglei Yang +2
Open-world (OW) recognition and detection models show strong zero- and few-shot adaptation abilities, inspiring their use as initializations in continual learning methods to improv…
UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity
Jingbo Lin, Zhilu Zhang, Wenbo Li +4
Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are…
Deciphering the Chaos: Enhancing Jailbreak Attacks via Adversarial Prompt Translation
Qizhang Li, Xiaochen Yang, Wangmeng Zuo +1
Automatic adversarial prompt generation provides remarkable success in jailbreaking safely-aligned large language models (LLMs). Existing gradient-based attacks, while demonstratin…
LPT++: Efficient Training on Mixture of Long-tailed Experts
Bowen Dong, Pan Zhou, Wangmeng Zuo
We introduce LPT++, a comprehensive framework for long-tailed classification that combines parameter-efficient fine-tuning (PEFT) with a learnable model ensemble. LPT++ enhances fr…
AutoDirector: Online Auto-scheduling Agents for Multi-sensory Composition
Minheng Ni, Chenfei Wu, Huaying Yuan +6
With the advancement of generative models, the synthesis of different sensory elements such as music, visuals, and speech has achieved significant realism. However, the approach to…
Multi-modal Crowd Counting via a Broker Modality
Haoliang Meng, Xiaopeng Hong, Chenhao Wang +2
Multi-modal crowd counting involves estimating crowd density from both visual and thermal/depth images. This task is challenging due to the significant gap between these distinct m…
CPT: Consistent Proxy Tuning for Black-box Optimization
Yuanyang He, Zitong Huang, Xinxing Xu +5
Black-box tuning has attracted recent attention due to that the structure or inner parameters of advanced proprietary models are not accessible. Proxy-tuning provides a test-time o…
LayerMatch: Do Pseudo-labels Benefit All Layers?
Chaoqi Liang, Guanglei Yang, Lifeng Qiao +4
Deep neural networks have achieved remarkable performance across various tasks when supplied with large-scale labeled data. However, the collection of labeled data can be time-cons…
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
Hang Yao, Ming Liu, Haolin Wang +4
Diffusion models have shown superior performance on unsupervised anomaly detection tasks. Since trained with normal data only, diffusion models tend to reconstruct normal counterpa…
DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors
Tianyu Huang, Haoze Zhang, Yihan Zeng +4
Dynamic 3D interaction has been attracting a lot of attention recently. However, creating such 4D content remains challenging. One solution is to animate 3D scenes with physics-bas…
Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed Observations
Zhilu Zhang, Ruohao Wang, Hongzhi Zhang +1
In this paper, we consider two challenging issues in reference-based super-resolution (RefSR) for smartphone, (i) how to choose a proper reference image, and (ii) how to learn RefS…
Improved Generation of Adversarial Examples Against Safety-aligned LLMs
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
Adversarial prompts generated using gradient-based methods exhibit outstanding performance in performing automatic jailbreak attacks against safety-aligned LLMs. Nevertheless, due…
LLM as a Complementary Optimizer to Gradient Descent: A Case Study in Prompt Tuning
Zixian Guo, Ming Liu, Zhilong Ji +3
Mastering a skill generally relies on both hands-on experience from doers and insightful, high-level guidance by mentors. Will this strategy also work well for solving complex non-…
Decomposed Global Optimization for Robust Point Matching with Low-Dimensional Branching
Wei Lian, Zhesen Cui, Fei Ma +3
Numerous applications require algorithms that can align partially overlapping point sets while maintaining invariance to geometric transformations (e.g., similarity, affine, rigid)…
SmartControl: Enhancing ControlNet for Handling Rough Visual Conditions
Xiaoyu Liu, Yuxiang Wei, Ming Liu +4
Human visual imagination usually begins with analogies or rough sketches. For example, given an image with a girl playing guitar before a building, one may analogously imagine how…
MV-VTON: Multi-View Virtual Try-On with Diffusion Models
Haoyu Wang, Zhilu Zhang, Donglin Di +2
The goal of image-based virtual try-on is to generate an image of the target person naturally wearing the given clothing. However, existing methods solely focus on the frontal try-…
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks
Zitong Huang, Ze Chen, Bowen Dong +3
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models. This paper first empirically finds that 1…
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset
Rongjian Xu, Zhilu Zhang, Renlong Wu +1
Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments. Lev…
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising
Junyi Li, Zhilu Zhang, Wangmeng Zuo
Blind-spot networks (BSN) have been prevalent neural architectures in self-supervised image denoising (SSID). However, most existing BSNs are conducted with convolution layers. Alt…
Dual-Camera Smooth Zoom on Mobile Phones
Renlong Wu, Zhilu Zhang, Yu Yang +1
When zooming between dual cameras on a mobile, noticeable jumps in geometric content and image color occur in the preview, inevitably affecting the user's zoom experience. In this…