103 citations · 316 across the 27 of their papers we have counts for
28 papers
MMOOC: A Comprehensive Benchmark for Out-of-Context Evaluation in Multimodal Large Language Models
Wenjie Zhu, Yabin Zhang, Wenjun Zeng +1
Multimodal Large Language Models (MLLMs) have achieved strong performance on a wide range of vision-language tasks, but often fail under imperfect or shifted contexts. A reliable M…
Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs
Wenjie Zhu, Yabin Zhang, Liang Xu +3
While test-time adaptation (TTA) empowers vision-language models to adapt without costly retraining, it remains highly vulnerable to out-of-distribution (OOD) outliers prevalent in…
Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision
Yunhe Gao, Yabin Zhang, Chong Wang +5
Foundation models have transformed vision and language by learning general-purpose representations from large-scale unlabeled data, yet 3D medical imaging lacks analogous approache…
ANTS: Adaptive Negative Textual Space Shaping for OOD Detection via Test-Time MLLM Understanding and Reasoning
Wenjie Zhu, Yabin Zhang, Xin Jin +2
The introduction of negative labels (NLs) has proven effective in enhancing Out-of-Distribution (OOD) detection. However, existing methods often lack an understanding of OOD images…
Knowledge Regularized Negative Feature Tuning of Vision-Language Models for Out-of-Distribution Detection
Wenjie Zhu, Yabin Zhang, Xin Jin +2
Out-of-distribution (OOD) detection is crucial for building reliable machine learning models. Although negative prompt tuning has enhanced the OOD detection capabilities of vision-…
AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models
Yabin Zhang, Lei Zhang
Recent research has shown that pre-trained vision-language models are effective at identifying out-of-distribution (OOD) samples by using negative labels as guidance. However, empl…
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Models
Yabin Zhang, Wenjie Zhu, Chenhang He +1
Out-of-distribution (OOD) detection is crucial for model reliability, as it identifies samples from unknown classes and reduces errors due to unexpected inputs. Vision-Language Mod…
ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation
Zhiyuan Ma, Yuxiang Wei, Yabin Zhang +3
By leveraging the text-to-image diffusion priors, score distillation can synthesize 3D contents without paired text-3D training data. Instead of spending hours of online optimizati…
Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
Yabin Zhang, Wenjie Zhu, Hui Tang +3
With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent resear…
VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations
Jiehong Lin, Zewei Wei, Yabin Zhang +1
Rotation estimation of high precision from an RGB-D object observation is a huge challenge in 6D object pose estimation, due to the difficulty of learning in the non-linear space o…
MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences
Chenhang He, Ruihuang Li, Yabin Zhang +2
Point cloud sequences are commonly used to accurately detect 3D objects in applications such as autonomous driving. Current top-performing multi-frame detectors mostly follow a Det…
SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Ruihuang Li, Chenhang He, Yabin Zhang +3
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image fea…
DynaMask: Dynamic Mask Selection for Instance Segmentation
Ruihuang Li, Chenhang He, Shuai Li +2
The representative instance segmentation methods mostly segment different object instances with a mask of the fixed resolution, e.g., 28*28 grid. However, a low-resolution mask los…
Adversarial Style Augmentation for Domain Generalization
Yabin Zhang, Bin Deng, Ruihuang Li +2
It is well-known that the performance of well-trained deep neural networks may degrade significantly when they are applied to data with even slightly shifted distributions. Recent…
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
Yongwei Chen, Rui Chen, Jiabao Lei +2
Creation of 3D content by stylization is a promising yet challenging problem in computer vision and graphics research. In this work, we focus on stylizing photorealistic appearance…
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning
Yabin Zhang, Jiehong Lin, Ruihuang Li +2
Masked autoencoder has demonstrated its effectiveness in self-supervised point cloud learning. Considering that masking is a kind of corruption, in this work we explore a more gene…
Masked Surfel Prediction for Self-Supervised Point Cloud Learning
Yabin Zhang, Jiehong Lin, Chenhang He +3
Masked auto-encoding is a popular and effective self-supervised learning approach to point cloud learning. However, most of the existing methods reconstruct only the masked points…
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
Yabin Zhang, Minghan Li, Ruihuang Li +2
Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. Wit…
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
Ruihuang Li, Shuai Li, Chenhang He +3
Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One p…
Gradual Domain Adaptation via Self-Training of Auxiliary Models
Yabin Zhang, Bin Deng, Kui Jia +1
Domain adaptation becomes more challenging with increasing gaps between source and target domains. Motivated from an empirical analysis on the reliability of labeled source data fo…
Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners
Yabin Zhang, Haojian Zhang, Bin Deng +3
Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are two typical strategies to reduce expensive manual annotations in machine learning. In order to learn eff…
On Universal Black-Box Domain Adaptation
Bin Deng, Yabin Zhang, Hui Tang +2
In this paper, we study an arguably least restrictive setting of domain adaptation in a sense of practical deployment, where only the interface of source model is available to the…
Unsupervised Domain Adaptation of Black-Box Source Models
Haojian Zhang, Yabin Zhang, Kui Jia +1
Unsupervised domain adaptation (UDA) aims to learn models for a target domain of unlabeled data by transferring knowledge from a labeled source domain. In the traditional UDA setti…
Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation
Yabin Zhang, Bin Deng, Kui Jia +1
Motivated by the problem relatedness between unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), many state-of-the-art UDA methods adopt SSL principles (e.g.,…