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Found 20 papers, 5 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.
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection
By transferring the knowledge of IFC from the base training to fine-tuning, the IFC generates plentiful novel samples to calibrate the novel class distribution.
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
Audio-Visual Segmentation (AVS) aims to extract the sounding object from a video frame, which is represented by a pixel-wise segmentation mask.
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.
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.
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.
Class-Incremental Semantic Segmentation
Knowledge Distillation
+1
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.
CSLNSpeech: solving extended speech separation problem with the help of Chinese sign language
We design a general deep learning network for learning the combination of three modalities, audio, face, and sign language information, for better solving the speech separation problem.
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 #79 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.
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Image segmentation refers to the process to divide an image into nonoverlapping meaningful regions according to human perception, which has become a classic topic since the early ages of computer vision.
