Search Results for author:
Found 134 papers, 55 papers with code
AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation
Additionally, compared to ViT, our approach achieves a reduction in FLOPs of up to 60 times.
DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation
Moreover, a simulated annealing strategy is embedded into IDU to endow our model with the power of addressing local optima issues.
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation
Standard clothing asset generation involves creating forward-facing flat-lay garment images displayed on a clear background by extracting clothing information from diverse real-world contexts, which presents significant challenges due to highly standardized sampling distributions and precise structural requirements in the generated images.
MSSIDD: A Benchmark for Multi-Sensor Denoising
Furthermore, we propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features, thereby facilitating the generalization of the consistent model to unseen sensors.
AU-vMAE: Knowledge-Guide Action Units Detection via Video Masked Autoencoder
Current Facial Action Unit (FAU) detection methods generally encounter difficulties due to the scarcity of labeled video training data and the limited number of training face IDs, which renders the trained feature extractor insufficient coverage for modeling the large diversity of inter-person facial structures and movements.
Learning Unsupervised Gaze Representation via Eye Mask Driven Information Bottleneck
Appearance-based supervised methods with full-face image input have made tremendous advances in recent gaze estimation tasks.
AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario
While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios, as their models suffer from issues of ill-fitted garment styles and quality degrading during the training process, not to mention the lack of support for various combinations of attire.
Toward Tiny and High-quality Facial Makeup with Data Amplify Learning
The core idea of DAL lies in employing a Diffusion-based Data Amplifier (DDA) to "amplify" limited images for the model training, thereby enabling accurate pixel-to-pixel supervision with merely a handful of annotations.
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
Vector Graphics Generation via Mutually Impulsed Dual-domain Diffusion
Intelligent generation of vector graphics has very promising applications in the fields of advertising and logo design artistic painting animation production etc.
Differentiable Micro-Mesh Construction
Micro-mesh (u-mesh) is a new graphics primitive for compact representation of extreme geometry consisting of a low-polygon base mesh enriched by per micro-vertex displacement.
Towards High-fidelity Artistic Image Vectorization via Texture-Encapsulated Shape Parameterization
We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts.
Real-Time Neural BRDF with Spherically Distributed Primitives
We propose a novel compact and efficient neural BRDF offering highly versatile material representation, yet with very-light memory and neural computation consumption towards achieving real-time rendering.
SieveNet: Selecting Point-Based Features for Mesh Networks
Meshes are widely used in 3D computer vision and graphics, but their irregular topology poses challenges in applying them to existing neural network architectures.
FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly
While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation.
Omni Aggregation Networks for Lightweight Image Super-Resolution
While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions.
Inferring Fluid Dynamics via Inverse Rendering
Humans have a strong intuitive understanding of physical processes such as fluid falling by just a glimpse of such a scene picture, i. e., quickly derived from our immersive visual experiences in memory.
3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process
We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc.
Frequency-Modulated Point Cloud Rendering with Easy Editing
We develop an effective point cloud rendering pipeline for novel view synthesis, which enables high fidelity local detail reconstruction, real-time rendering and user-friendly editing.
USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency
To address these issues, we propose an unsupervised separated 3D garments and human reconstruction model (USR), which reconstructs the human body and authentic textured clothes in layers without 3D models.
AudioEar: Single-View Ear Reconstruction for Personalized Spatial Audio
To this end, we propose to achieve personalized spatial audio by reconstructing 3D human ears with single-view images.
Learning Shape Primitives via Implicit Convexity Regularization
In this work, a novel regularization term named Implicit Convexity Regularization (ICR) imposed on implicit primitive learning is proposed to tackle this problem.
Multiplicative Fourier Level of Detail
We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network.
Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process
We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc.
Exploring and Utilizing Pattern Imbalance
In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation.
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance Pursuit
The ability of scale-equivariance processing blocks plays a central role in arbitrary-scale image super-resolution tasks.
Editable Image Geometric Abstraction via Neural Primitive Assembly
This work explores a novel image geometric abstraction paradigm based on assembly out of a pool of pre-defined simple parametric primitives (i. e., triangle, rectangle, circle and semicircle), facilitating controllable shape editing in images.
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features.
Learning Continuous Depth Representation via Geometric Spatial Aggregator
Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision.
GARF:Geometry-Aware Generalized Neural Radiance Field
To address these issues, we propose Geometry-Aware Generalized Neural Radiance Field (GARF) with a geometry-aware dynamic sampling (GADS) strategy to perform real-time novel view rendering and unsupervised depth estimation on unseen scenes without per-scene optimization.
Boosting Point Clouds Rendering via Radiance Mapping
In this work, we focus on boosting the image quality of point clouds rendering with a compact model design.
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Based on the RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction.
Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening
Stage I introduces a test time adaptation strategy, which improves the physical plausibility of synthesized human skeleton motions by optimizing skeleton joint locations.
Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis
Besides, we develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR, where each case consists of a 3D shape of adrenal gland and its diagnosis label (normal vs. abnormal) assigned by experts.
Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision
Projection map (PM) from optical coherence tomography (OCT) B-scan is an important tool to diagnose retinal diseases, which typically requires retinal layer segmentation.
HIRL: A General Framework for Hierarchical Image Representation Learning
This framework aims to learn multiple semantic representations for each image, and these representations are structured to encode image semantics from fine-grained to coarse-grained.
Representation-Agnostic Shape Fields
In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning.
Gradient Correction beyond Gradient Descent
GCGD consists of two plug-in modules: 1) inspired by the idea of gradient prediction, we propose a \textbf{GC-W} module for weight gradient correction; 2) based on Neural ODE, we propose a \textbf{GC-ODE} module for hidden states gradient correction.
HCSC: Hierarchical Contrastive Selective Coding
In this framework, a set of hierarchical prototypes are constructed and also dynamically updated to represent the hierarchical semantic structures underlying the data in the latent space.
Contrastive Regression for Domain Adaptation on Gaze Estimation
Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance.
ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging
Deep implicit shape models have become popular in the computer vision community at large but less so for biomedical applications.
Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid
We train the prior learner and the image generator as a unified model without any post-processing.
MovieNet-PS: A Large-Scale Person Search Dataset in the Wild
Person search aims to jointly localize and identify a query person from natural, uncropped images, which has been actively studied over the past few years.
Ranked #3 on
Person Search
on CUHK-SYSU
Joint Modeling of Visual Objects and Relations for Scene Graph Generation
We further propose an efficient and effective algorithm for inference based on mean-field variational inference, in which we first provide a warm initialization by independently predicting the objects and their relations according to the current model, followed by a few iterations of relational reasoning.
TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification
In the meantime, we design an adaptive BN layer in the domain-invariant stream, to approximate the statistics of various unseen domains.
Domain Generalization
Generalizable Person Re-identification
+1
Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation
We consider a new problem of adapting a human mesh reconstruction model to out-of-domain streaming videos, where performance of existing SMPL-based models are significantly affected by the distribution shift represented by different camera parameters, bone lengths, backgrounds, and occlusions.
Ranked #1 on
3D Absolute Human Pose Estimation
on Surreal
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses.
Ranked #1 on
3D-Aware Image Synthesis
on FFHQ 256 x 256
RainNet: A Large-Scale Imagery Dataset for Spatial Precipitation Downscaling
Spatial precipitation downscaling is one of the most important meteorological problems.
Asymmetric 3D Context Fusion for Universal Lesion Detection
The proposed A3D consistently outperforms symmetric context fusion operators by considerable margins, and establishes a new \emph{state of the art} on DeepLesion.
RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans
Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes.
Energy Attack: On Transferring Adversarial Examples
Base on the eigenvalues, we can model the energy distribution of adversarial perturbations.
Cross-category Video Highlight Detection via Set-based Learning
For tackling such practical problem, we propose a Dual-Learner-based Video Highlight Detection (DL-VHD) framework.
Object Wake-up: 3D Object Rigging from a Single Image
A dataset of generic 3D objects with ground-truth annotated skeletons is collected.
Exploring Visual Context for Weakly Supervised Person Search
This paper inventively considers weakly supervised person search with only bounding box annotations.
Context-Aware Image Inpainting with Learned Semantic Priors
Based on the semantic priors, we further propose a context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator.
SimSwap: An Efficient Framework For High Fidelity Face Swapping
In contrast to previous approaches that either lack the ability to generalize to arbitrary identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the target face.
Ranked #2 on
Face Swapping
on FaceForensics++
Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement
For a given unsupervised task, we design multilevel tasks and define different learning stages for the deep network.
Self-supervised Graph-level Representation Learning with Local and Global Structure
This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material discovery.
X-volution: On the unification of convolution and self-attention
Convolution and self-attention are acting as two fundamental building blocks in deep neural networks, where the former extracts local image features in a linear way while the latter non-locally encodes high-order contextual relationships.
Ranked #84 on
Instance Segmentation
on COCO minival
3D Human Action Representation Learning via Cross-View Consistency Pursuit
In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary supervision signal.
Learning Multi-Attention Context Graph for Group-Based Re-Identification
Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks.
Graphical Modeling for Multi-Source Domain Adaptation
Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem compared to the conventional single-source domain adaptation.
Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction
This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos.
Ranked #49 on
3D Human Pose Estimation
on 3DPW
Shape Self-Correction for Unsupervised Point Cloud Understanding
Then the destroyed shape and the normal shape are sent into a point cloud network to get representations, which are employed to segment points that belong to distorted parts and further reconstruct them to restore the shape to normal.
Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation
Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively.
Monocular 3D Human Pose Estimation
Unsupervised 3D Human Pose Estimation
Geometric Granularity Aware Pixel-To-Mesh
Pixel-to-mesh has wide applications, especially in virtual or augmented reality, animation and game industry.
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement
We propose to disentangle graph structure and node attributes into two distinct sets of representations, and such disentanglement can be done in either the input or the embedding space.
Skeleton2Mesh: Kinematics Prior Injected Unsupervised Human Mesh Recovery
The challenges of the latter task are two folds: (1) pose failure (i. e., pose mismatching -- different skeleton definitions in dataset and SMPL , and pose ambiguity -- endpoints have arbitrary joint angle configurations for the same 3D joint coordinates).
Image Translation via Fine-grained Knowledge Transfer
Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks.
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling
To alleviate these obstacles, we present the first large-scale spatial precipitation downscaling dataset named RainNet, which contains more than $62, 400$ pairs of high-quality low/high-resolution precipitation maps for over $17$ years, ready to help the evolution of deep learning models in precipitation downscaling.
Sketch Generation with Drawing Process Guided by Vector Flow and Grayscale
Existing pencil sketch algorithms are based on texture rendering rather than the direct imitation of strokes, making them unable to show the drawing process but only a final result.
Omni-GAN: On the Secrets of cGANs and Beyond
The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse.
Ranked #9 on
Conditional Image Generation
on ImageNet 128x128
CooGAN: A Memory-Efficient Framework for High-Resolution Facial Attribute Editing
In contrast to great success of memory-consuming face editing methods at a low resolution, to manipulate high-resolution (HR) facial images, i. e., typically larger than 7682 pixels, with very limited memory is still challenging.
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
We present MedMNIST, a collection of 10 pre-processed medical open datasets.
Learning Black-Box Attackers with Transferable Priors and Query Feedback
This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available.
Anisotropic Stroke Control for Multiple Artists Style Transfer
Furthermore, the results are with distinctive artistic style and retain the anisotropic semantic information.
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy.
Ranked #1 on
Text-To-Speech Synthesis
on 20000 utterances
(using extra training data)
Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study
A three-level hierarchical classification system for pulmonary lesions is developed, which covers most diseases in cancer-related diagnosis.
Hierarchical Style-based Networks for Motion Synthesis
Generating diverse and natural human motion is one of the long-standing goals for creating intelligent characters in the animated world.
Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds
We develop a novel learning scheme named Self-Prediction for 3D instance and semantic segmentation of point clouds.
Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation.
Domain Adaptation
Multi-Source Unsupervised Domain Adaptation
Video Prediction via Example Guidance
In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics.
Collaborative Learning for Faster StyleGAN Embedding
The latent code of the recent popular model StyleGAN has learned disentangled representations thanks to the multi-layer style-based generator.
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks
To learn the sampling policy, a Markov decision process is embedded into the search algorithm and a moving average is applied for better stability.
AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness.
Relational Learning between Multiple Pulmonary Nodules via Deep Set Attention Transformers
In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules.
Cross-domain Detection via Graph-induced Prototype Alignment
To mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment via elaborate prototype representations.
Wasserstein-Bounded Generative Adversarial Networks
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
Adversarial Domain Adaptation with Domain Mixup
In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.
Reinventing 2D Convolutions for 3D Images
Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size.
CartoonRenderer: An Instance-based Multi-Style Cartoon Image Translator
In a nutshell, our method maps photo into a feature model and renders the feature model back into image space.
Facial Image Deformation Based on Landmark Detection
In this work, we use facial landmarks to make the deformation for facial images more authentic.
Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis
The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable.
Composable Semi-parametric Modelling for Long-range Motion Generation
Learning diverse and natural behaviors is one of the longstanding goal for creating intelligent characters in the animated world.
Evaluating and Boosting Uncertainty Quantification in Classification
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making.
Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning.
Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling
Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points.
Learning Context Graph for Person Search
Person re-identification has achieved great progress with deep convolutional neural networks.
Adversarial Attack and Defense on Point Sets
A set of novel 3D point cloud attack operations are proposed via pointwise gradient perturbation and adversarial point attachment / detachment.
Disentangled Deep Autoencoding Regularization for Robust Image Classification
In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult to generalize to novel test images with reasonably large geometric transformations.
Video Prediction via Selective Sampling
Most adversarial learning based video prediction methods suffer from image blur, since the commonly used adversarial and regression loss pair work rather in a competitive way than collaboration, yielding compromised blur effect.
Deep Regression Tracking with Shrinkage Loss
Regression trackers directly learn a mapping from regularly dense samples of target objects to soft labels, which are usually generated by a Gaussian function, to estimate target positions.
Geometric Constrained Joint Lane Segmentation and Lane Boundary Detection
The main contributions of the proposed frame- work are highlighted in two facets: (1) We put forward a multiple-task learning framework with mutually interlinked sub-structures between lane segmentation and lane boundary detection to improve overall performance.
Egocentric Activity Prediction via Event Modulated Attention
Predicting future activities from an egocentric viewpoint is of particular interest in assisted living.
Structure Preserving Video Prediction
Despite recent emergence of adversarial based methods for video prediction, existing algorithms often produce unsatisfied results in image regions with rich structural information (i. e., object boundary) and detailed motion (i. e., articulated body movement).
Fine-Grained Video Captioning for Sports Narrative
First, to facilitate this novel research of fine-grained video caption, we collected a novel dataset called Fine-grained Sports Narrative dataset (FSN) that contains 2K sports videos with ground-truth narratives from YouTube. com.
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
Crowd counting or density estimation is a challenging task in computer vision due to large scale variations, perspective distortions and serious occlusions, etc.
Ranked #4 on
Crowd Counting
on WorldExpo’10
Multiple Granularity Group Interaction Prediction
Most human activity analysis works (i. e., recognition orãprediction) only focus on a single granularity, i. e., eitherãmodelling global motion based on the coarse level movement such as human trajectories orãforecasting future detailed action based on body partsâ movement such as skeleton motion.
Pose Transferrable Person Re-Identification
On the other hand, in addition to the conventional discriminator of GAN (i. e., to distinguish between REAL/FAKE samples), we propose a novel guider sub-network which encourages the generated sample (i. e., with novel pose) towards better satisfying the ReID loss (i. e., cross-entropy ReID loss, triplet ReID loss).
Flexible Network Binarization with Layer-wise Priority
In this work, we reveal an important fact that binarizing different layers has a widely-varied effect on the compression ratio of network and the loss of performance.
Performance Guaranteed Network Acceleration via High-Order Residual Quantization
Input binarization has shown to be an effective way for network acceleration.
Skeleton-aided Articulated Motion Generation
This work make the first attempt to generate articulated human motion sequence from a single image.
Ranked #2 on
Gesture-to-Gesture Translation
on NTU Hand Digit
Binary Coding for Partial Action Analysis With Limited Observation Ratios
Extensive experiments on four realistic action datasets in terms of three tasks (i. e., partial action retrieval, recognition and prediction) clearly show the superiority of PRBC over the state-of-the-art methods, along with significantly reduced memory load and computational costs during the online test.
Video Segmentation via Multiple Granularity Analysis
We introduce a Multiple Granularity Analysis framework for video segmentation in a coarse-to-fine manner.
Recurrent Modeling of Interaction Context for Collective Activity Recognition
However, most of the previous activity recognition methods do not offer a flexible and scalable scheme to handle the high order context modeling problem.
Zero-Shot Action Recognition With Error-Correcting Output Codes
Our ZSECOC equips the conventional ECOC with the additional capability of ZSAR, by addressing the domain shift problem.
Ranked #4 on
Zero-Shot Action Recognition
on Olympics
Image Matching via Loopy RNN
Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration.
Depth Structure Preserving Scene Image Generation
Key to automatically generate natural scene images is to properly arrange among various spatial elements, especially in the depth direction.
Predicting Human Interaction via Relative Attention Model
Predicting human interaction is challenging as the on-going activity has to be inferred based on a partially observed video.
Person Re-Identification via Recurrent Feature Aggregation
We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches.
Temporal Action Localization With Pyramid of Score Distribution Features
We investigate the feature design and classification architectures in temporal action localization.
Progressively Parsing Interactional Objects for Fine Grained Action Detection
Fine grained video action analysis often requires reliable detection and tracking of various interacting objects and human body parts, denoted as interactional object parsing.
Cascaded Interactional Targeting Network for Egocentric Video Analysis
Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i. e., massive bounding box annotations) with a small set of strongly supervised data (i. e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance.
Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition
This scheme leverages motion cues such as motion boundary and motion magnitude (in contrast, camera motion is usually considered as "noise" for most previous methods) to generate a more compact and discriminative set of object proposals, which are more closely related to the objects which are being manipulated.
Interaction Part Mining: A Mid-Level Approach for Fine-Grained Action Recognition
Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them.
Fine-grained Action Recognition
Human-Object Interaction Detection
+2
Motion Part Regularization: Improving Action Recognition via Trajectory Selection
Inspired by the recent advance in sentence regularization for text classification, we introduce a Motion Part Regularization framework to mining discriminative semi-local groups of dense trajectories.
Crowded Scene Analysis: A Survey
Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis.
Half-CNN: A General Framework for Whole-Image Regression
This is a generic regression framework that fits many applications.
CNN: Single-label to Multi-label
Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks.
Multiple Granularity Analysis for Fine-grained Action Detection
We propose to decompose the fine-grained human activity analysis problem into two sequential tasks with increasing granularity.
Beta Process Multiple Kernel Learning
Specifically, for the kernel representation calculated for each input feature instance, we multiply it element-wise with a latent binary vector named as instance selection variables, which targets at selecting good instances and attenuate the effect of ambiguous ones in the resulting new kernel representation.
