Search Results for author:
Found 23 papers, 19 papers with code
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm
For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information.
Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings.
Modular Blind Video Quality Assessment
Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services.
VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations
We apply the proposed VI-Net to the challenging task of category-level 6D object pose estimation for predicting the poses of unknown objects without available CAD models; experiments on the benchmarking datasets confirm the efficacy of our method, which outperforms the existing ones with a large margin in the regime of high precision.
MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences
Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame.
SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention.
DynaMask: Dynamic Mask Selection for Instance Segmentation
The representative instance segmentation methods mostly segment different object instances with a mask of the fixed resolution, e. g., 28*28 grid.
Adversarial Style Augmentation for Domain Generalization
By updating the model against the adversarial statistics perturbation during training, we allow the model to explore the worst-case domain and hence improve its generalization performance.
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning
We also validate the effectiveness of affine transformation corruption with the Transformer backbones, where we decompose the reconstruction of the complete point cloud into the reconstructions of detailed local patches and rough global shape, alleviating the position leakage problem in the reconstruction.
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
Creation of 3D content by stylization is a promising yet challenging problem in computer vision and graphics research.
Masked Surfel Prediction for Self-Supervised Point Cloud Learning
In this work, we make the first attempt, to the best of our knowledge, to consider the local geometry information explicitly into the masked auto-encoding, and propose a novel Masked Surfel Prediction (MaskSurf) method.
Perceptual Quality Assessment of Virtual Reality Videos in the Wild
Investigating how people perceive virtual reality (VR) videos in the wild (i. e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex authentic distortions localized in space and time.
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for training.
Ranked #9 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
In this work, we, for the first time to our best knowledge, propose to perform Exact Feature Distribution Matching (EFDM) by exactly matching the empirical Cumulative Distribution Functions (eCDFs) of image features, which could be implemented by applying the Exact Histogram Matching (EHM) in the image feature space.
Gradual Domain Adaptation via Self-Training of Auxiliary Models
Domain adaptation becomes more challenging with increasing gaps between source and target domains.
Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners
Especially, state-of-the-art SSL methods significantly outperform existing UDA methods on the challenging UDA benchmark of DomainNet, and state-of-the-art UDA methods could be further enhanced with SSL techniques.
On Universal Black-Box Domain Adaptation
The great promise that UB$^2$DA makes, however, brings significant learning challenges, since domain adaptation can only rely on the predictions of unlabeled target data in a partially overlapped label space, by accessing the interface of source model.
Unsupervised Domain Adaptation of Black-Box Source Models
Unsupervised domain adaptation (UDA) aims to learn models for a target domain of unlabeled data by transferring knowledge from a labeled source domain.
Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation
To make the proposed A$^2$LP useful for UDA, we propose empirical schemes to generate such virtual instances.
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
By using MCSD as a measure of domain distance, we develop a new domain adaptation bound for multi-class UDA; its data-dependent, probably approximately correct bound is also developed that naturally suggests adversarial learning objectives to align conditional feature distributions across source and target domains.
Domain-Symmetric Networks for Adversarial Domain Adaptation
Since target samples are unlabeled, we also propose a scheme of cross-domain training to help learn the target classifier.
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data
Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples.
Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network
In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization.
