74 citations · 315 across the 165 of their papers we have counts for
43 papers · 1 filter
Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown Exploration
Lucas Fernando Alvarenga e Silva, Samuel Felipe dos Santos, Nicu Sebe +1
Convolutional neural networks (CNNs) can learn directly from raw data, resulting in exceptional performance across various research areas. However, factors present in non-controlla…
Hierarchical Information Flow for Generalized Efficient Image Restoration
Yawei Li, Bin Ren, Jingyun Liang +5
While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To…
Hierarchical Cross-Attention Network for Virtual Try-On
Hao Tang, Bin Ren, Pingping Wu +1
In this paper, we present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet). HCANet is crafted with two…
Anti-Forgetting Adaptation for Unsupervised Person Re-identification
Hao Chen, Francois Bremond, Nicu Sebe +1
Regular unsupervised domain adaptive person re-identification (ReID) focuses on adapting a model from a source domain to a fixed target domain. However, an adapted ReID model can h…
Face Anonymization Made Simple
Han-Wei Kung, Tuomas Varanka, Sanjay Saha +2
Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable. Additionally, many methods requir…
LESS: Label-Efficient and Single-Stage Referring 3D Segmentation
Xuexun Liu, Xiaoxu Xu, Jinlong Li +4
Referring 3D Segmentation is a visual-language task that segments all points of the specified object from a 3D point cloud described by a sentence of query. Previous works perform…
RMLR: Extending Multinomial Logistic Regression into General Geometries
Ziheng Chen, Yue Song, Rui Wang +2
Riemannian neural networks, which extend deep learning techniques to Riemannian spaces, have gained significant attention in machine learning. To better classify the manifold-value…
Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery
Haiyang Zheng, Nan Pu, Wenjing Li +2
In this paper, we study a practical yet challenging task, On-the-fly Category Discovery (OCD), aiming to online discover the newly-coming stream data that belong to both known and…
Discriminative Anchor Learning for Efficient Multi-view Clustering
Yalan Qin, Nan Pu, Hanzhou Wu +1
Multi-view clustering aims to study the complementary information across views and discover the underlying structure. For solving the relatively high computational cost for the exi…
Optimizing Resource Consumption in Diffusion Models through Hallucination Early Detection
Federico Betti, Lorenzo Baraldi, Rita Cucchiara +1
Diffusion models have significantly advanced generative AI, but they encounter difficulties when generating complex combinations of multiple objects. As the final result heavily de…
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance
Xiaoxu Xu, Yitian Yuan, Jinlong Li +6
In this paper, we propose 3DSS-VLG, a weakly supervised approach for 3D Semantic Segmentation with 2D Vision-Language Guidance, an alternative approach that a 3D model predicts den…
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models
Moreno D'Incà, Elia Peruzzo, Massimiliano Mancini +3
Recent progress in Text-to-Image (T2I) generative models has enabled high-quality image generation. As performance and accessibility increase, these models are gaining significant…
PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection
Yidi Li, Jiahao Wen, Bin Ren +5
The integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. However, this combination often struggles with capturing semantic inf…
Large Language Models for Multimodal Deformable Image Registration
Mingrui Ma, Weijie Wang, Jie Ning +3
The challenge of Multimodal Deformable Image Registration (MDIR) lies in the conversion and alignment of features between images of different modalities. Generative models (GMs) ca…
Global-Local Distillation Network-Based Audio-Visual Speaker Tracking with Incomplete Modalities
Yidi Li, Yihan Li, Yixin Guo +5
In speaker tracking research, integrating and complementing multi-modal data is a crucial strategy for improving the accuracy and robustness of tracking systems. However, tracking…
ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining
Qi Ma, Yue Li, Bin Ren +5
3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks. This calls for the 3D understanding directly in this representation space. To…
When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding
Pingping Zhang, Jinlong Li, Kecheng Chen +6
Existing codecs are designed to eliminate intrinsic redundancies to create a compact representation for compression. However, strong external priors from Multimodal Large Language…
Masked Image Modeling: A Survey
Vlad Hondru, Florinel Alin Croitoru, Shervin Minaee +2
In this work, we survey recent studies on masked image modeling (MIM), an approach that emerged as a powerful self-supervised learning technique in computer vision. The MIM task in…
Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning
Xuri Ge, Junchen Fu, Fuhai Chen +3
Facial action units (AUs), as defined in the Facial Action Coding System (FACS), have received significant research interest owing to their diverse range of applications in facial…
Towards Localized Fine-Grained Control for Facial Expression Generation
Tuomas Varanka, Huai-Qian Khor, Yante Li +4
Generative models have surged in popularity recently due to their ability to produce high-quality images and video. However, steering these models to produce images with specific a…
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning
Bin Ren, Guofeng Mei, Danda Pani Paudel +6
Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cl…
Restore Anything Model via Efficient Degradation Adaptation
Bin Ren, Eduard Zamfir, Zongwei Wu +6
With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful. Traditional approaches ty…
Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
Ziheng Chen, Yue Song, Xiao-Jun Wu +2
Global Covariance Pooling (GCP) has been demonstrated to improve the performance of Deep Neural Networks (DNNs) by exploiting second-order statistics of high-level representations.…
Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization
Jinlong Li, Dong Zhao, Zequn Jie +3
Efficient fine-tuning of vision-language models (VLMs) like CLIP for specific downstream tasks is gaining significant attention. Previous works primarily focus on prompt learning t…