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Found 34 papers, 8 papers with code
Learning with Noisy Class Labels for Instance Segmentation
Specifically, in instance segmentation, noisy class labels play different roles in the foreground-background sub-task and the foreground-instance sub-task.
CMaP-SAM: Contraction Mapping Prior for SAM-driven Few-shot Segmentation
Few-shot segmentation (FSS) aims to segment new classes using few annotated images.
HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment
To address the issue and facilitate the advancement of IHAs, we introduce the first Image Quality Assessment Database for image Harmony evaluation (HarmonyIQAD), which consists of 1, 350 harmonized images generated by 9 different IHAs, and the corresponding human visual preference scores.
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images
The ARIC dataset has advantages of multiple perspectives, 32 activity categories, three modalities, and real-world classroom scenarios.
Cognition Transferring and Decoupling for Text-supervised Egocentric Semantic Segmentation
However, most recent third-view methods leverage the frozen Contrastive Language-Image Pre-training (CLIP) model, which is pre-trained on the semantic-oriented third-view data and lapses in the egocentric view due to the ``relation insensitive" problem.
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images
In real classroom settings, normal teaching activities such as reading, account for a large proportion of samples, while rare non-teaching activities such as eating, continue to appear.
Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding Confusion
To address this issue, we propose the Distribution-Level Memory Recall (DMR) method, which uses a Gaussian mixture model to precisely fit the feature distribution of old knowledge at the distribution level and generate pseudo features in the next stage.
No Re-Train, More Gain: Upgrading Backbones with Diffusion Model for Few-Shot Segmentation
For the second issue, due to the varying granularity of transformed priors from diverse annotation types, we conceptualize these multi-granular transformed priors as analogous to noisy intermediates at different steps of a diffusion model.
On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks
Using the performance variations as indicators, we evaluate the adversarial robustness of eight predominant LIC algorithms against diverse attacks.
A Deep Learning Method for Beat-Level Risk Analysis and Interpretation of Atrial Fibrillation Patients during Sinus Rhythm
By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0. 7591.
Dual-Consistency Model Inversion for Non-Exemplar Class Incremental Learning
In this paper we present a novel method termed Dual-Consistency Model Inversion (DCMI) to generate better synthetic samples of old classes through two pivotal consistency alignments: (1) the semantic consistency between the synthetic images and the corresponding prototypes and (2) domain consistency between synthetic and real images of new classes.
Class Incremental Learning with Multi-Teacher Distillation
In this paper we propose the MTD method to find multiple diverse teachers for CIL.
Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration
Learning based image quality assessment (IQA) models have obtained impressive performance with the help of reliable subjective quality labels, where mean opinion score (MOS) is the most popular choice.
Cross-modal Cognitive Consensus guided Audio-Visual Segmentation
Then, we feed the unified-modal label back to the visual backbone as the explicit semantic-level guidance via a Cognitive Consensus guided Attention Module (CCAM), which highlights the local features corresponding to the interested object.
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning
However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition.
Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class
Most existing methods are based on convolutional networks and prevent forgetting through knowledge distillation, which (1) need to add additional convolutional layers to predict new classes, and (2) ignore to distinguish different regions corresponding to old and new classes during knowledge distillation and roughly distill all the features, thus limiting the learning of new classes.
CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
We place continual learning into a causal framework, based on which we find the task-induced bias is reduced naturally by two underlying mechanisms in task and domain incremental learning.
Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment
More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other.
RefCrowd: Grounding the Target in Crowd with Referring Expressions
Unfortunately, there is no effort to explore crowd understanding in multi-modal domain that bridges natural language and computer vision.
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning.
BA^2M: A Batch Aware Attention Module for Image Classification
Then, we feed the SARs of the whole batch to a normalization function to get the weights for each sample.
Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery.
CrossDet: Crossline Representation for Object Detection
In this paper, we propose a novel anchor-free object detection network, called CrossDet, which uses a set of growing cross lines along horizontal and vertical axes as object representations.
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations
Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction.
A New Local Transformation Module for Few-shot Segmentation
The result by the matrix transformation can be regarded as an attention map with high-level semantic cues, based on which a transformation module can be built simply. The proposed transformation module is a general module that can be used to replace the transformation module in the existing few-shot segmentation frameworks.
Ranked #86 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image
Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images.
Class Activation Map generation by Multiple Level Class Grouping and Orthogonal Constraint
Moreover, a new orthogonal module and a two-branch based CAM generation method are proposed to generate class regions that are orthogonal and complementary.
A New Few-shot Segmentation Network Based on Class Representation
This paper studies few-shot segmentation, which is a task of predicting foreground mask of unseen classes by a few of annotations only, aided by a set of rich annotations already existed.
Class Activation Map Generation by Representative Class Selection and Multi-Layer Feature Fusion
Existing method generates class activation map (CAM) by a set of fixed classes (i. e., using all the classes), while the discriminative cues between class pairs are not considered.
Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval
Finally, we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture.
Key-Word-Aware Network for Referring Expression Image Segmentation
On the other hand, the relationships of different image regions are not considered as well, even though they are greatly important to eliminate the undesired foreground object in accordance with specific query.
A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment
By modifying the perception threshold, we can illustrate the sorting accuracy with a more sophisticated SA-ST curve, rather than a single rank correlation coefficient.
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers
The experiments show that our model classifies most of the drug pairs into correct DDI categories, which outperforms the existing NLP or deep learning methods.
Group MAD Competition - A New Methodology to Compare Objective Image Quality Models
We first build a database that contains 4, 744 source natural images, together with 94, 880 distorted images created from them.
