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Found 36 papers, 7 papers with code
LLMs are Good Sign Language Translators
Sign Language Translation (SLT) is a challenging task that aims to translate sign videos into spoken language.
Action Detection via an Image Diffusion Process
Action detection aims to localize the starting and ending points of action instances in untrimmed videos, and predict the classes of those instances.
Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation
Class activation maps (CAMs) are commonly employed in weakly supervised semantic segmentation (WSSS) to produce pseudo-labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping
However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to consistently produce vertical noise under unsupervised constraints.
Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval.
3D Points Splatting for Real-Time Dynamic Hand Reconstruction
This is followed by a self-adaptive deformation that deforms the hand from the canonical space to the target pose, adapting to the dynamic changing of canonical points which, in contrast to the common practice of subdividing the MANO model, offers greater flexibility and results in improved geometry fitting.
AI-Generated Content (AIGC) for Various Data Modalities: A Survey
Due to its wide range of applications and the demonstrated potential of recent works, AIGC developments have been attracting lots of attention recently, and AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape (as voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human avatar (body and head), 3D motion, and audio -- each presenting different characteristics and challenges.
Distribution-Aligned Diffusion for Human Mesh Recovery
Inspired by their capability, we explore a diffusion-based approach for human mesh recovery, and propose a Human Mesh Diffusion (HMDiff) framework which frames mesh recovery as a reverse diffusion process.
A Probabilistic Attention Model with Occlusion-aware Texture Regression for 3D Hand Reconstruction from a Single RGB Image
The experimental results demonstrate our probabilistic model's state-of-the-art accuracy in 3D hand and texture reconstruction from a single image in both training schemes, including in the presence of severe occlusions.
Ranked #2 on
3D Hand Pose Estimation
on HO-3D
Token Boosting for Robust Self-Supervised Visual Transformer Pre-training
Pre-training VTs on such corrupted data can be challenging, especially when we pre-train via the masked autoencoding approach, where both the inputs and masked ``ground truth" targets can potentially be unreliable in this case.
Progressive Channel-Shrinking Network
Currently, salience-based channel pruning makes continuous breakthroughs in network compression.
GradMDM: Adversarial Attack on Dynamic Networks
Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input.
Unified Pose Sequence Modeling
We propose a Unified Pose Sequence Modeling approach to unify heterogeneous human behavior understanding tasks based on pose data, e. g., action recognition, 3D pose estimation and 3D early action prediction.
DiffPose: Toward More Reliable 3D Pose Estimation
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy.
Ranked #11 on
3D Human Pose Estimation
on MPI-INF-3DHP
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
We design a novel Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of specialized neurons that are only activated for a subset of samples that are highly similar.
IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition
Human interaction recognition is very important in many applications.
Ranked #2 on
Human Interaction Recognition
on SBU
ERA: Expert Retrieval and Assembly for Early Action Prediction
Early action prediction aims to successfully predict the class label of an action before it is completely performed.
Graph-Context Attention Networks for Size-Varied Deep Graph Matching
Deep learning for graph matching has received growing interest and developed rapidly in the past decade.
Ranked #4 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Meta Agent Teaming Active Learning for Pose Estimation
The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire.
Recent Advances of Continual Learning in Computer Vision: An Overview
In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order.
The Multi-Modal Video Reasoning and Analyzing Competition
In this paper, we introduce the Multi-Modal Video Reasoning and Analyzing Competition (MMVRAC) workshop in conjunction with ICCV 2021.
Multi-Branch with Attention Network for Hand-Based Person Recognition
In this paper, we propose a novel hand-based person recognition method for the purpose of criminal investigations since the hand image is often the only available information in cases of serious crime such as sexual abuse.
Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning
Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features.
Else-Net: Elastic Semantic Network for Continual Action Recognition From Skeleton Data
This makes online continual action recognition a challenging task.
Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19
Secondly, we propose a deep learning model for screening COVID-19 using our proposed CT dataset and report the baseline results.
Human Action Recognition from Various Data Modalities: A Review
Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action.
DL-Reg: A Deep Learning Regularization Technique using Linear Regression
This linear constraint, which is further adjusted by a regularization factor, prevents the network from the risk of overfitting.
Learning Latent Global Network for Skeleton-based Action Prediction
Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes.
Ranked #4 on
Skeleton Based Action Recognition
on SYSU 3D
Learning Action Recognition Model From Depth and Skeleton Videos
Depth sensors open up possibilities of dealing with the human action recognition problem by providing 3D human skeleton data and depth images of the scene.
3D Action Recognition From Novel Viewpoints
We propose a human pose representation model that transfers human poses acquired from different unknown views to a view-invariant high-level space.
Learning a Deep Model for Human Action Recognition from Novel Viewpoints
The strength of our technique is that we learn a single R-NKTM for all actions and all viewpoints for knowledge transfer of any real human action video without the need for re-training or fine-tuning the model.
Learning a Non-Linear Knowledge Transfer Model for Cross-View Action Recognition
We propose unsupervised learning of a non-linear model that transfers knowledge from multiple views to a canonical view.
Histogram of Oriented Principal Components for Cross-View Action Recognition
We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations.
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition
In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations.
Action Classification with Locality-constrained Linear Coding
We use the Histogram of Oriented Gradient (HOG3D) feature to encode the information in each cell.
A Novel Scheme for Intelligent Recognition of Pornographic Images
To test the proposed method, performance of system was evaluated over 18354 download images from internet.
