Search Results for author:
Found 127 papers, 46 papers with code
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification
On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks.
EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers
Removing unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge.
MambaVO: Deep Visual Odometry Based on Sequential Matching Refinement and Training Smoothing
To address this challenge, this paper proposes MambaVO, which conducts robust initialization, Mamba-based sequential matching refinement, and smoothed training to enhance the matching quality and improve the pose estimation in deep visual odometry.
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC
SMAC-HARD supports customizable opponent strategies, randomization of adversarial policies, and interfaces for MARL self-play, enabling agents to generalize to varying opponent behaviors and improve model stability.
RL-LLM-DT: An Automatic Decision Tree Generation Method Based on RL Evaluation and LLM Enhancement
After iterative refinements, our curling AI based on the decision tree ranks first on the Jidi platform among 34 curling AIs in total, which demonstrates that LLMs can significantly enhance the robustness and adaptability of decision trees, representing a substantial advancement in the field of Game AI.
Explaining Model Overfitting in CNNs via GMM Clustering
Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision.
Integrating Dual Prototypes for Task-Wise Adaption in Pre-Trained Model-Based Class-Incremental Learning
This paper proposes the Dual Prototype network for Task-wise Adaption (DPTA) of PTM-based CIL.
UnitedVLN: Generalizable Gaussian Splatting for Continuous Vision-Language Navigation
Vision-and-Language Navigation (VLN), where an agent follows instructions to reach a target destination, has recently seen significant advancements.
Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning
FSCIL combines the characteristics and challenges of class-incremental learning and few-shot learning: (i) Current classes occupy the entire feature space, which is detrimental to learning new classes.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Region-Guided Attack on the Segment Anything Model (SAM)
The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial.
Approximate attention with MLP: a pruning strategy for attention-based model in multivariate time series forecasting
Attention-based architectures have become ubiquitous in time series forecasting tasks, including spatio-temporal (STF) and long-term time series forecasting (LTSF).
Survey of User Interface Design and Interaction Techniques in Generative AI Applications
The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so.
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models
StarCraft Multi-Agent Challenge (SMAC) is one of the most commonly used experimental environments in multi-agent reinforcement learning (MARL), where the specific task is to control a set number of allied units to defeat enemy forces.
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
This sampling strategy for flow step can be easily applied to existing flow matching based models without retraining.
SurANet: Surrounding-Aware Network for Concealed Object Detection via Highly-Efficient Interactive Contrastive Learning Strategy
In this paper, considering that the surrounding environment information can be well utilized to identify the concealed objects, and thus, we propose a novel deep Surrounding-Aware Network, namely SurANet, for COD tasks, which introduces surrounding information into feature extraction and loss function to improve the discrimination.
Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model
To this end, we establish a causal framework for ST predictions, termed CaPaint, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process.
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification
In addition, to further enhance the efficacy of EntAugment, we introduce a novel entropy regularization term, EntLoss, which complements the EntAugment approach.
InstructSing: High-Fidelity Singing Voice Generation via Instructing Yourself
Subsequently, the HN module is connected with an extended WaveNet by an UNet-based module, which transforms the output of the HN module to a latent variable sequence containing essential periodic and aperiodic information.
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems
Previous works on adversarial attacks have primarily focused on white-box attacks that directly perturb the states or actions of victim agents, often in scenarios with a limited number of attacks.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning
To address these issues, we introduce the first publicly available map editor for the popular mobile game Honor of Kings and design a lightweight environment, Mini Honor of Kings (Mini HoK), for researchers to conduct experiments.
Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
Sh\=ukai quantifies the state to enhance generalizability, introducing Heterogeneous League Training (HELT) to achieve balanced competence, generalizability, and training efficiency.
The SkatingVerse Workshop & Challenge: Methods and Results
The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding.
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation
The policy network optimizes the variability within the augmented data, while the target network utilizes the adaptively augmented samples for training.
Multi-Scale Dilated Convolution Network for Long-Term Time Series Forecasting
Accurate forecasting of long-term time series has important applications for decision making and planning.
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving
In this paper, we address this challenge by introducing a world model-based autonomous driving 4D representation learning framework, dubbed \emph{DriveWorld}, which is capable of pre-training from multi-camera driving videos in a spatio-temporal fashion.
Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond
General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual environments to decision-making systems.
Spectrally Pruned Gaussian Fields with Neural Compensation
However, this comes with high memory consumption, e. g., a well-trained Gaussian field may utilize three million Gaussian primitives and over 700 MB of memory.
Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer
Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues.
Reconstructing the Image Stitching Pipeline: Integrating Fusion and Rectangling into a Unified Inpainting Model
This paper proposes the Simple and Robust Stitcher (SRStitcher), which revolutionizes the image stitching pipeline by simplifying the fusion and rectangling stages into a unified inpainting model, requiring no model training or fine-tuning.
LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition
LEAF introduces a hierarchical expression-aware aggregation strategy that operates at three levels: semantic, instance, and category.
Facial Expression Recognition
Facial Expression Recognition (FER)
Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis
2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose.
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
To address this issue, we propose a robust contrastive-based S4 framework, termed the Probabilistic Representation Contrastive Learning (PRCL) framework to enhance the robustness of the unsupervised training process.
EmoWear: Exploring Emotional Teasers for Voice Message Interaction on Smartwatches
We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions.
SynSP: Synergy of Smoothness and Precision in Pose Sequences Refinement
To leverage this quality information we propose a motion refinement network termed SynSP to achieve a Synergy of Smoothness and Precision in the sequence refinement tasks.
Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency
AdaPruner can still significantly enhance model performance even after pruning up to 10-30\% of the training data.
DanZero+: Dominating the GuanDan Game through Reinforcement Learning
The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period.
RADAP: A Robust and Adaptive Defense Against Diverse Adversarial Patches on Face Recognition
RADAP employs innovative techniques, such as FCutout and F-patch, which use Fourier space sampling masks to improve the occlusion robustness of the FR model and the performance of the patch segmenter.
NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields
Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns.
A Simple Geometric-Aware Indoor Positioning Interpolation Algorithm Based on Manifold Learning
Interpolation methodologies have been widely used within the domain of indoor positioning systems.
Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization
Adversarial machine learning (AML) studies attacks that can fool machine learning algorithms into generating incorrect outcomes as well as the defenses against worst-case attacks to strengthen model robustness.
UniParser: Multi-Human Parsing with Unified Correlation Representation Learning
Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information.
Ranked #1 on
Multi-Human Parsing
on MHP v2.0
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery
Particularly, the gradients from the segmentation model are exploited to discover the easily confused region, in which it is difficult to identify the pixel-wise objects from the background in a frame.
3D Implicit Transporter for Temporally Consistent Keypoint Discovery
To address this issue, the Transporter method was introduced for 2D data, which reconstructs the target frame from the source frame to incorporate both spatial and temporal information.
Uncovering the Unseen: Discover Hidden Intentions by Micro-Behavior Graph Reasoning
HID presents a unique challenge in that hidden intentions lack the obvious visual representations to distinguish them from normal intentions.
Unified Single-Stage Transformer Network for Efficient RGB-T Tracking
With this structure, the network can extract fusion features of the template and search region under the mutual interaction of modalities.
Ranked #4 on
Rgb-T Tracking
on GTOT
Color Prompting for Data-Free Continual Unsupervised Domain Adaptive Person Re-Identification
In this work, we propose a Color Prompting (CoP) method for data-free continual unsupervised domain adaptive person Re-ID.
Domain Adaptive Person Re-Identification
Person Re-Identification
+1
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition
To tackle these issues, we introduce MA-FSAR, a framework that employs the Parameter-Efficient Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations.
RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition
Data augmentation has been widely used in low-resource NER tasks to tackle the problem of data sparsity.
Evidential Detection and Tracking Collaboration: New Problem, Benchmark and Algorithm for Robust Anti-UAV System
Still, previous works have simplified such an anti-UAV task as a tracking problem, where the prior information of UAVs is always provided; such a scheme fails in real-world anti-UAV tasks (i. e. complex scenes, indeterminate-appear and -reappear UAVs, and real-time UAV surveillance).
GIMM: InfoMin-Max for Automated Graph Contrastive Learning
Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessary for the downstream task.
A Fast and Robust Camera-IMU Online Calibration Method For Localization System
Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance.
The 3rd Anti-UAV Workshop & Challenge: Methods and Results
Second, we set up two tracks for the first time, i. e., Anti-UAV Tracking and Anti-UAV Detection & Tracking.
Single-stage Multi-human Parsing via Point Sets and Center-based Offsets
We instead present a high-performance Single-stage Multi-human Parsing (SMP) deep architecture that decouples the multi-human parsing problem into two fine-grained sub-problems, i. e., locating the human body and parts.
Ranked #2 on
Multi-Human Parsing
on MHP v2.0
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning.
MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance.
Ranked #8 on
Vehicle Re-Identification
on VehicleID Large
Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text)
Those who used the text assistant asked more questions, but the question lengths were similar.
Attribute-Specific Manipulation Based on Layer-Wise Channels
We analyse the gradients layer by layer on the style space.
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking
HD-AMOT defines the diversified informative representation by encoding the geometric and semantic information, and formulates the frame inference strategy as a Markov decision process to learn an optimal sampling policy based on the designed informative representation.
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification
The experimental results on various datasets and CNN models verify that the proposed method outperforms other previous data augmentation methods in image classification tasks.
Neural Dependencies Emerging from Learning Massive Categories
Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others.
DanZero: Mastering GuanDan Game with Reinforcement Learning
We compare it with 8 baseline AI programs which are based on heuristic rules and the results reveal the outstanding performance of DanZero.
AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression
With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose.
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance
In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models.
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large.
RSTAM: An Effective Black-Box Impersonation Attack on Face Recognition using a Mobile and Compact Printer
Furthermore, we propose a random meta-optimization strategy for ensembling several pre-trained face models to generate more general adversarial masks.
Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information
From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios.
RandoMix: A mixed sample data augmentation method with multiple mixed modes
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains.
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling
In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment.
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
In this way, agents dealing with the same subtask share their learning of specific abilities and different subtasks correspond to different specific abilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Infographics Wizard: Flexible Infographics Authoring and Design Exploration
With the help of designers, we propose a semi-automated infographic framework for general structured and flow-based infographic design generation.
Image Data Augmentation for Deep Learning: A Survey
By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data.
DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning
Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games.
Thin-Plate Spline Motion Model for Image Animation
Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image.
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack
A special branch of adversarial examples, namely sparse adversarial examples, can fool the target DNNs by perturbing only a few pixels.
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
Multi-agent Reinforcement Learning
reinforcement-learning
+4
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking
In our approach, we regard each camera as an agent and address AMOT with a multi-agent reinforcement learning solution.
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Specifically, we model both individual Q-values and global Q-value with categorical distribution.
Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy Regularization
In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards.
Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation
The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i. e., top-down or bottom-up, leading to redundant pipelines with high computation cost.
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning
Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.
EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation
This paper presents EDAssistant, a JupyterLab extension that supports EDA with in-situ search of example notebooks and recommendation of useful APIs, powered by novel interactive visualization of search results.
Multi-scale fusion self attention mechanism
Based on the traditional attention mechanism, multi-scale fusion self attention extracts phrase information at different scales by setting convolution kernels at different levels, and calculates the corresponding attention matrix at different scales, so that the model can better extract phrase level information.
User-Centric Semi-Automated Infographics Authoring and Recommendation
Based on the framework, we also propose an interactive tool, \name{}, for assisting novice designers with creating high-quality infographics from an input in a markdown format by offering recommendations of different design components of infographics.
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
Face.evoLVe: A High-Performance Face Recognition Library
In this paper, we develop face. evoLVe -- a comprehensive library that collects and implements a wide range of popular deep learning-based methods for face recognition.
Rethinking Sampling Strategies for Unsupervised Person Re-identification
While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role.
Interactive Dimensionality Reduction for Comparative Analysis
However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups.
SASICM A Multi-Task Benchmark For Subtext Recognition
The F1 score of SASICMBERT, whose pretrained model is BERT, is 65. 12%, which is 0. 75% higher than that of SASICMg.
Image-to-Video Generation via 3D Facial Dynamics
Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.
Joint Face Image Restoration and Frontalization for Recognition
First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart.
Faster and Simpler Siamese Network for Single Object Tracking
With the development of the deep network and the release for a series of large scale datasets for single object tracking, siamese networks have been proposed and perform better than most of the traditional methods.
IC Networks: Remodeling the Basic Unit for Convolutional Neural Networks
Inspired by the elastic collision model in physics, we present a general structure which can be integrated into the existing CNNs to improve their performance.
Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
The releasing of such a large-scale dataset could be a useful initial step in research of tracking UAVs.
IC Neuron: An Efficient Unit to Construct Neural Networks
We believe that the IC neuron can be a basic unit to build network structures.
Effective Fusion Factor in FPN for Tiny Object Detection
We propose a novel concept, fusion factor, to control information that deep layers deliver to shallow layers, for adapting FPN to tiny object detection.
OLALA: Object-Level Active Learning for Efficient Document Layout Annotation
Document images often have intricate layout structures, with numerous content regions (e. g. texts, figures, tables) densely arranged on each page.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
A Visual Analytics Framework for Contrastive Network Analysis
A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other.
Network Comparison with Interpretable Contrastive Network Representation Learning
This analysis task could be greatly assisted by contrastive learning, which is an emerging analysis approach to discover salient patterns in one dataset relative to another.
Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios.
Temporal Convolutional Attention-based Network For Sequence Modeling
So we propose an exploratory architecture referred to Temporal Convolutional Attention-based Network (TCAN) which combines temporal convolutional network and attention mechanism.
IC-Network: Efficient Structure for Convolutional Neural Networks
We term this structure the "Inter-layer Collision" (IC) structure.
Pairwise Interactive Graph Attention Network for Context-Aware Recommendation
PIGAT introduces the attention mechanism to consider the importance of each interacted user/item to both the user and the item, which captures user interests, item attractions and their influence on the recommendation context.
SANVis: Visual Analytics for Understanding Self-Attention Networks
Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications.
Super Interaction Neural Network
Recent studies have demonstrated that the convolutional networks heavily rely on the quality and quantity of generated features.
Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation
Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning
In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.
"Tom" pet robot applied to urban autism
The consequent urban autism problem has become more and more serious.
Operation-aware Neural Networks for User Response Prediction
Many deep models are proposed to automatically learn high-order feature interactions.
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
Ranked #7 on
Face Alignment
on AFLW2000-3D
Multi-Prototype Networks for Unconstrained Set-based Face Recognition
In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Face anti-spoofing (a. k. a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.
Ranked #2 on
Face Anti-Spoofing
on MSU-MFSD
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Ranked #1 on
Age-Invariant Face Recognition
on MORPH Album2
Dynamic Conditional Networks for Few-Shot Learning
This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i. e, only a few training samples are available for each condition.
Object Relation Detection Based on One-shot Learning
Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between images and natural language.
Weakly Supervised Phrase Localization With Multi-Scale Anchored Transformer Network
In this paper, we propose a novel weakly supervised model, Multi-scale Anchored Transformer Network (MATN), to accurately localize free-form textual phrases with only image-level supervision.
Towards Pose Invariant Face Recognition in the Wild
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.
Ranked #1 on
Multi-Human Parsing
on PASCAL-Part
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis
In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses.
Ranked #1 on
Face Verification
on IJB-A
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Specifically, iFAN achieves an overall F-score of 91. 15% on the Helen dataset for face parsing, a normalized mean error of 5. 81% on the MTFL dataset for facial landmark localization and an accuracy of 45. 73% on the BNU dataset for emotion recognition with a single model.
Multiple-Human Parsing in the Wild
To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.
Ranked #3 on
Multi-Human Parsing
on MHP v1.0
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge.
Robust LSTM-Autoencoders for Face De-Occlusion in the Wild
The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches.
Nonequilibrium and nonlinear kinetics as key determinants for bistability in fission yeast G2-M transition
A living cell is an open, nonequilibrium biochemical system where ATP hydrolysis serves as the energy source for a wide range of intracellular processes, possibly including the assurance for decision-making.
