Search Results for author:
Found 37 papers, 15 papers with code
Particularity beyond Commonality: Unpaired Identity Transfer with Multiple References
Unpaired image-to-image translation aims to translate images from the source class to target one by providing sufficient data for these classes.
ZYJ123@DravidianLangTech-EACL2021: Offensive Language Identification based on XLM-RoBERTa with DPCNN
The development of online media platforms has given users more opportunities to post and comment freely, but the negative impact of offensive language has become increasingly apparent.
ZYJ@LT-EDI-EACL2021:XLM-RoBERTa-Based Model with Attention for Hope Speech Detection
We use the attention mechanism to adjust the weight of all the output layers of XLM-RoBERTa to make full use of the information extracted from each layer, and use the weighted sum of all the output layers to complete the classification task.
UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark
To further evaluate the IAA capability of MLLMs, we construct the UNIAA-Bench, which consists of three aesthetic levels: Perception, Description, and Assessment.
Perception-Oriented Video Frame Interpolation via Asymmetric Blending
In practice, motion estimates often prove to be error-prone, resulting in misaligned features.
Motion Inversion for Video Customization
In this research, we present a novel approach to motion customization in video generation, addressing the widespread gap in the thorough exploration of motion representation within video generative models.
DVIS++: Improved Decoupled Framework for Universal Video Segmentation
We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video instance segmentation (VIS), video semantic segmentation (VSS), and video panoptic segmentation (VPS).
Ranked #1 on
Video Semantic Segmentation
on VSPW
Stable Segment Anything Model
Thus, our solution, termed Stable-SAM, offers several advantages: 1) improved SAM's segmentation stability across a wide range of prompt qualities, while 2) retaining SAM's powerful promptable segmentation efficiency and generality, with 3) minimal learnable parameters (0. 08 M) and fast adaptation (by 1 training epoch).
1st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation
Video instance segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving.
Scene-Generalizable Interactive Segmentation of Radiance Fields
Existing methods for interactive segmentation in radiance fields entail scene-specific optimization and thus cannot generalize across different scenes, which greatly limits their applicability.
Feature Decoupling-Recycling Network for Fast Interactive Segmentation
First, our model decouples the learning of source image semantics from the encoding of user guidance to process two types of input domains separately.
1st Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation
In this report, we successfully validated the effectiveness of the decoupling strategy in video panoptic segmentation.
Compression-Aware Video Super-Resolution
Videos stored on mobile devices or delivered on the Internet are usually in compressed format and are of various unknown compression parameters, but most video super-resolution (VSR) methods often assume ideal inputs resulting in large performance gap between experimental settings and real-world applications.
H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions
The learnt deformable kernel is then utilized in convolving the input frames for predicting the interpolated frame.
DeViT: Deformed Vision Transformers in Video Inpainting
This paper proposes a novel video inpainting method.
Multi-criteria Decision-making of Intelligent Vehicles under Fault Condition Enhancing Public-private Partnership
With the proposed method, alternate decisions can be derived to reduce the risks of public time loss significantly with a low increase in the risk of mission delay.
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
Instead of directly feeding consecutive frames into a VSR model, we propose to compute the temporal difference between frames and divide those pixels into two subsets according to the level of difference.
Real-time decision-making for autonomous vehicles under faults
This paper addresses the challenges of decision-making for autonomous vehicles under faults during a transport mission.
Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review
The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research.
ZYJ at SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense with ALBERT-Based Model
This article introduces the submission of subtask 1 and subtask 2 that we participate in SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense, we use a model based on ALBERT that uses ALBERT as the module for extracting text features.
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
Therefore, high-quality correspondence matching is critical.
Short-term Maintenance Planning of Autonomous Trucks for Minimizing Economic Risk
We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company.
YNUtaoxin at SemEval-2020 Task 11: Identification Fragments of Propaganda Technique by Neural Sequence Labeling Models with Different Tagging Schemes and Pre-trained Language Model
We only participated in the first subtask, and a neural sequence model was used to perform the sequence tagging task.
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution
Motivated by these findings, we propose a temporal multi-correspondence aggregation strategy to leverage similar patches across frames, and a cross-scale nonlocal-correspondence aggregation scheme to explore self-similarity of images across scales.
VCNet: A Robust Approach to Blind Image Inpainting
Blind inpainting is a task to automatically complete visual contents without specifying masks for missing areas in an image.
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Conditional generative adversarial networks have shown exceptional generation performance over the past few years.
Attribute-Driven Spontaneous Motion in Unpaired Image Translation
Current image translation methods, albeit effective to produce high-quality results in various applications, still do not consider much geometric transform.
Landmark Assisted CycleGAN for Cartoon Face Generation
In this paper, we are interested in generating an cartoon face of a person by using unpaired training data between real faces and cartoon ones.
Image Inpainting via Generative Multi-column Convolutional Neural Networks
In this paper, we propose a generative multi-column network for image inpainting.
Facelet-Bank for Fast Portrait Manipulation
Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks.
Scale-recurrent Network for Deep Image Deblurring
In single image deblurring, the "coarse-to-fine" scheme, i. e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches.
Ranked #3 on
Image Deblurring
on GoPro
(Params (M) metric, using extra
training data)
Zero-order Reverse Filtering
In this paper, we study an unconventional but practically meaningful reversibility problem of commonly used image filters.
Detail-revealing Deep Video Super-resolution
In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results.
Ranked #11 on
Video Super-Resolution
on Vid4 - 4x upscaling
Convolutional Neural Pyramid for Image Processing
We propose a principled convolutional neural pyramid (CNP) framework for general low-level vision and image processing tasks.
High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits
Estimating correspondence between two images and extracting the foreground object are two challenges in computer vision.
Video Super-Resolution via Deep Draft-Ensemble Learning
We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution.
Handling Motion Blur in Multi-Frame Super-Resolution
Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR).
