73 citations · 198 across the 24 of their papers we have counts for
25 papers
SAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language Segmentation
Chengxi Zeng, Yuxuan Jiang, Ge Gao +6
Vision-language segmentation models such as SAM3 enable flexible, prompt-driven visual grounding, but inherit large, general-purpose text encoders originally designed for open-ende…
MoonSeg3R: Monocular Online Zero-Shot Segment Anything in 3D with Reconstructive Foundation Priors
Zhipeng Du, Duolikun Danier, Jan Eric Lenssen +1
In this paper, we focus on online zero-shot monocular 3D instance segmentation, a novel practical setting where existing approaches fail to perform because they rely on posed RGB-D…
View-Consistent Diffusion Representations for 3D-Consistent Video Generation
Duolikun Danier, Ge Gao, Steven McDonagh +3
Video generation models have made significant progress in generating realistic content, enabling applications in simulation, gaming, and film making. However, current generated vid…
GFix: Perceptually Enhanced Gaussian Splatting Video Compression
Siyue Teng, Ge Gao, Duolikun Danier +5
3D Gaussian Splatting (3DGS) enhances 3D scene reconstruction through explicit representation and fast rendering, demonstrating potential benefits for various low-level vision task…
Attentive Feature Aggregation or: How Policies Learn to Stop Worrying about Robustness and Attend to Task-Relevant Visual Cues
Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier +4
The adoption of pre-trained visual representations (PVRs), leveraging features from large-scale vision models, has become a popular paradigm for training visuomotor policies. Howev…
The Temporal Trap: Entanglement in Pre-Trained Visual Representations for Visuomotor Policy Learning
Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier +2
The integration of pre-trained visual representations (PVRs) has significantly advanced visuomotor policy learning. However, effectively leveraging these models remains a challenge…
DepthCues: Evaluating Monocular Depth Perception in Large Vision Models
Duolikun Danier, Mehmet Aygün, Changjian Li +2
Large-scale pre-trained vision models are becoming increasingly prevalent, offering expressive and generalizable visual representations that benefit various downstream tasks. Recen…
MVAD: A Multiple Visual Artifact Detector for Video Streaming
Chen Feng, Duolikun Danier, Fan Zhang +3
Visual artifacts are often introduced into streamed video content, due to prevailing conditions during content production and delivery. Since these can degrade the quality of the u…
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content
Tianhao Peng, Chen Feng, Duolikun Danier +4
With recent advances in deep learning, numerous algorithms have been developed to enhance video quality, reduce visual artifacts, and improve perceptual quality. However, little re…
Full-reference Video Quality Assessment for User Generated Content Transcoding
Zihao Qi, Chen Feng, Duolikun Danier +4
Unlike video coding for professional content, the delivery pipeline of User Generated Content (UGC) involves transcoding where unpristine reference content needs to be compressed r…
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Chen Feng, Duolikun Danier, Haoran Wang +4
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlati…
BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos
Chen Feng, Duolikun Danier, Fan Zhang +3
Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience. These artefacts arise from different stages of the…
Score Normalization for a Faster Diffusion Exponential Integrator Sampler
Guoxuan Xia, Duolikun Danier, Ayan Das +4
Recently, Zhang et al. have proposed the Diffusion Exponential Integrator Sampler (DEIS) for fast generation of samples from Diffusion Models. It leverages the semi-linear nature o…
LDMVFI: Video Frame Interpolation with Latent Diffusion Models
Duolikun Danier, Fan Zhang, David Bull
Existing works on video frame interpolation (VFI) mostly employ deep neural networks that are trained by minimizing the L1, L2, or deep feature space distance (e.g. VGG loss) betwe…
ST-MFNet Mini: Knowledge Distillation-Driven Frame Interpolation
Crispian Morris, Duolikun Danier, Fan Zhang +2
Currently, one of the major challenges in deep learning-based video frame interpolation (VFI) is the large model sizes and high computational complexity associated with many high p…
BVI-VFI: A Video Quality Database for Video Frame Interpolation
Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) is a fundamental research topic in video processing, which is currently attracting increased attention across the research community. While the deve…
Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
Chen Feng, Zihao Qi, Duolikun Danier +4
It is well known that high dynamic range (HDR) video can provide more immersive visual experiences compared to conventional standard dynamic range content. However, HDR content is…
FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation
Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) serves as a useful tool for many video processing applications. Recently, it has also been applied in the video compression domain for enhancing bot…
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing
Duolikun Danier, Chen Feng, Fan Zhang +1
This paper describes a CNN-based multi-frame post-processing approach based on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN. This method has been int…
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training
Chen Feng, Duolikun Danier, Fan Zhang +1
In recent years, deep learning techniques have shown significant potential for improving video quality assessment (VQA), achieving higher correlation with subjective opinions compa…
Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN
Duolikun Danier, Fan Zhang, David Bull
This paper presents a new deformable convolution-based video frame interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the multi-flow prediction. This model first…
A Subjective Quality Study for Video Frame Interpolation
Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) is one of the fundamental research areas in video processing and there has been extensive research on novel and enhanced interpolation algorithms. T…
ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing
Chen Feng, Duolikun Danier, Charlie Tan +2
This paper presents a deep learning-based video compression framework (ViSTRA3). The proposed framework intelligently adapts video format parameters of the input video before encod…
ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding. VFI can be extremely chal…