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Found 44 papers, 18 papers with code
Topology-Preserving Class-Incremental Learning
A well-known issue for class-incremental learning is the catastrophic forgetting phenomenon, where the network's recognition performance on old classes degrades severely when incrementally learning new classes.
Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes
To alleviate the heavy annotation burden for training a reliable crowd counting model and thus make the model more practicable and accurate by being able to benefit from more data, this paper presents a new semi-supervised method based on the mean teacher framework.
Can SAM Count Anything? An Empirical Study on SAM Counting
Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting.
Remind of the Past: Incremental Learning with Analogical Prompts
It mimics the feature distribution of the target old class on the old model using only samples of new classes.
Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning
Task allocation plays a vital role in multi-robot autonomous cleaning systems, where multiple robots work together to clean a large area.
Benchmarking Deepart Detection
To address potentially appeared ethics questions, this paper establishes a deepart detection database (DDDB) that consists of a set of high-quality conventional art images (conarts) and five sets of deepart images generated by five state-of-the-art deepfake models.
Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference
To avoid obvious stage learning bottlenecks, we propose a brand-new stage-isolation based incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task of each stage without the interference of others.
Semi-supervised Crowd Counting via Density Agency
In this paper, we propose a new agency-guided semi-supervised counting approach.
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i. e., domain increment learning (DIL).
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Within the proposed benchmark, we explore some commonly known essentials of standard continual learning.
Deep Class Incremental Learning from Decentralized Data
In this paper, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data are stored in multiple repositories.
Boosting Crowd Counting via Multifaceted Attention
Secondly, we design the Local Attention Regularization to supervise the training of LRA by minimizing the deviation among the attention for different feature locations.
Object Counting: You Only Need to Look at One
This paper aims to tackle the challenging task of one-shot object counting.
Ranked #6 on
Object Counting
on FSC147
Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition
Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them.
3D Visual Tracking Framework with Deep Learning for Asteroid Exploration
3D visual tracking is significant to deep space exploration programs, which can guarantee spacecraft to flexibly approach the target.
Class incremental learning for video action classification
Class Incremental Learning (CIL) is a hot topic in machine learning for CNN models to learn new classes incrementally.
Anomaly Detection via Self-organizing Map
Anomaly detection plays a key role in industrial manufacturing for product quality control.
Ranked #43 on
Anomaly Detection
on MVTec AD
Direct Measure Matching for Crowd Counting
Second, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching.
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation.
Image-to-image Translation via Hierarchical Style Disentanglement
Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation with diverse styles) tasks.
Disentanglement
Multimodal Unsupervised Image-To-Image Translation
+1
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models
A common practice is to start from a large perturbation and then iteratively reduce it with a deterministic direction and a random one while keeping it adversarial.
Towards a Universal Model for Cross-Dataset Crowd Counting
This paper proposes to handle the practical problem of learning a universal model for crowd counting across scenes and datasets.
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation
This drawback limits the flexibility of UDA in complicated open-set tasks where no labels are shared between domains.
Micro-expression spotting: A new benchmark
Micro-expressions (MEs) are brief and involuntary facial expressions that occur when people are trying to hide their true feelings or conceal their emotions.
Few-Shot Class-Incremental Learning
FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones.
Ranked #8 on
Few-Shot Class-Incremental Learning
on CIFAR-100
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
Human action recognition from skeleton data, fueled by the Graph Convolutional Network (GCN), has attracted lots of attention, due to its powerful capability of modeling non-Euclidean structure data.
Beyond Universal Person Re-ID Attack
We propose a \emph{more universal} adversarial perturbation (MUAP) method for both image-agnostic and model-insensitive person Re-ID attack.
Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection
We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions.
Bayesian Loss for Crowd Count Estimation with Point Supervision
In crowd counting datasets, each person is annotated by a point, which is usually the center of the head.
Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement
The method includes two parts: 1) a Spatio-Temporal Video Enhancement Network (STVEN) for video enhancement, and 2) an rPPG network (rPPGNet) for rPPG signal recovery.
Heart rate estimation
Photoplethysmography (PPG) heart rate estimation
+2
Video Action Recognition Via Neural Architecture Searching
Deep neural networks have achieved great success for video analysis and understanding.
Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning
Especially, AGUIT benefits from two-fold: (1) It adopts a novel semi-supervised learning process by translating attributes of labeled data to unlabeled data, and then reconstructing the unlabeled data by a cycle consistency operation.
DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation
In this paper, we propose a novel segmentation approach, named Cartesian-polar dual-domain network (DDNet), which for the first time considers the complementary of the Cartesian domain and the polar domain.
A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework
Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facialexpressions.
Micro Expression Recognition
Micro-Expression Recognition
+1
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions
To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatial-temporal deformations of micro-expression sequence.
Cross-Database Micro-Expression Recognition: A Benchmark
Cross-database micro-expression recognition (CDMER) is one of recently emerging and interesting problem in micro-expression analysis.
Universal Perturbation Attack Against Image Retrieval
In this paper, we make the first attempt in attacking image retrieval systems.
Micro-Expression Spotting: A Benchmark
Micro-expressions are rapid and involuntary facial expressions, which indicate the suppressed or concealed emotions.
PCANet-II: When PCANet Meets the Second Order Pooling
Moreover, it is easy to combine other discriminative and robust cues by using the second order pooling.
Saliency Integration: An Arbitrator Model
Previous offline integration methods usually face two challenges: 1. if most of the candidate saliency models misjudge the saliency on an image, the integration result will lean heavily on those inferior candidate models; 2. an unawareness of the ground truth saliency labels brings difficulty in estimating the expertise of each candidate model.
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation.
Probing the Intra-Component Correlations within Fisher Vector for Material Classification
Fisher vector (FV) has become a popular image representation.
Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods
For ME recognition, the performance of previous studies is low.
Dynamic texture and scene classification by transferring deep image features
Moreover we explore two different implementations of the TCoF scheme, i. e., the \textit{spatial} TCoF and the \textit{temporal} TCoF, in which the mean-removed frames and the difference between two adjacent frames are used as the inputs of the ConvNet, respectively.
