244 citations · 343 across the 26 of their papers we have counts for
27 papers
FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion
Yu Lu, Junjie Yang, Piotr Koniusz +2
Autoregressive video generators synthesize long videos by generating successive temporal segments, but their historical KV cache grows with video length. Existing bounded-cache met…
Uncertainty-DTW for Sequences and Visual Tokens
Lei Wang, Syuan-Hao Li, Yongsheng Gao +1
Aligning structured data is a fundamental problem in computer vision and machine learning, underlying tasks such as time series analysis, human action recognition, and visual repre…
Subspace Kernel Learning on Tensor Sequences
Lei Wang, Xi Ding, Yongsheng Gao +1
Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient.…
Learning Time in Static Classifiers
Xi Ding, Lei Wang, Piotr Koniusz +1
Real-world visual data rarely presents as isolated, static instances. Instead, it often evolves gradually over time through variations in pose, lighting, object state, or scene con…
Graph Your Own Prompt
Xi Ding, Lei Wang, Piotr Koniusz +1
We propose Graph Consistency Regularization (GCR), a novel framework that injects relational graph structures, derived from model predictions, into the learning process to promote…
Video Understanding by Design: How Datasets Shape Video Models
Lei Wang, Syuan-Hao Li, Piotr Koniusz +1
Research in video understanding has advanced rapidly, driven by increasingly diverse datasets and more powerful model architectures. While existing surveys typically organize progr…
Feature Hallucination for Self-supervised Action Recognition
Lei Wang, Piotr Koniusz
Understanding human actions in videos requires more than raw pixel analysis; it relies on high-level semantic reasoning and effective integration of multimodal features. We propose…
When Spatial meets Temporal in Action Recognition
Huilin Chen, Lei Wang, Yifan Chen +2
Video action recognition has made significant strides, but challenges remain in effectively using both spatial and temporal information. While existing methods often focus on eithe…
Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion
Dexuan Ding, Lei Wang, Liyun Zhu +2
In computer vision tasks, features often come from diverse representations, domains (e.g., indoor and outdoor), and modalities (e.g., text, images, and videos). Effectively fusing…
Motion meets Attention: Video Motion Prompts
Qixiang Chen, Lei Wang, Piotr Koniusz +1
Videos contain rich spatio-temporal information. Traditional methods for extracting motion, used in tasks such as action recognition, often rely on visual contents rather than prec…
Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment
Lei Wang, Jun Liu, Liang Zheng +2
Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment…
Flow Dynamics Correction for Action Recognition
Lei Wang, Piotr Koniusz
Various research studies indicate that action recognition performance highly depends on the types of motions being extracted and how accurate the human actions are represented. In…
Adaptive Multi-head Contrastive Learning
Lei Wang, Piotr Koniusz, Tom Gedeon +1
In contrastive learning, two views of an original image, generated by different augmentations, are considered a positive pair, and their similarity is required to be high. Similarl…
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation
Dahyun Kang, Piotr Koniusz, Minsu Cho +1
We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed met…
Learning Partial Correlation based Deep Visual Representation for Image Classification
Saimunur Rahman, Piotr Koniusz, Lei Wang +3
Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolut…
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition
Lei Wang, Piotr Koniusz
Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore…
Uncertainty-DTW for Time Series and Sequences
Lei Wang, Piotr Koniusz
Dynamic Time Warping (DTW) is used for matching pairs of sequences and celebrated in applications such as forecasting the evolution of time series, clustering time series or even m…
Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection
Shan Zhang, Naila Murray, Lei Wang +1
In this paper, we tackle the challenging problem of Few-shot Object Detection. Existing FSOD pipelines (i) use average-pooled representations that result in information loss; and/o…
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition
Lei Wang, Piotr Koniusz
We propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignment between query…
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve
Lei Wang, Jun Liu, Piotr Koniusz
In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-based action recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To factor out misalignmen…
High-order Tensor Pooling with Attention for Action Recognition
Lei Wang, Ke Sun, Piotr Koniusz
We aim at capturing high-order statistics of feature vectors formed by a neural network, and propose end-to-end second- and higher-order pooling to form a tensor descriptor. Tensor…
A Token-wise CNN-based Method for Sentence Compression
Weiwei Hou, Hanna Suominen, Piotr Koniusz +2
Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information. Its applications can benefit many fiel…
Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors
Lei Wang, Piotr Koniusz
In this paper, we build on a concept of self-supervision by taking RGB frames as input to learn to predict both action concepts and auxiliary descriptors e.g., object descriptors.…
A Comparative Review of Recent Kinect-based Action Recognition Algorithms
Lei Wang, Du Q. Huynh, Piotr Koniusz
Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recogni…