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Found 13 papers, 2 papers with code
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.
Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning
So we propose SNRO, which slightly shifts the features of new classes to remember old classes.
MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning
Further, in order to better constrain the knowledge characteristics of old and new tasks at the specific feature level, we have created the Two-stage Knowledge Distillation (TsKD), which is able to learn the new task well while weighing the old task.
Prompt-Driven Referring Image Segmentation with Instance Contrasting
Referring image segmentation (RIS) aims to segment the target referent described by natural language.
Class Incremental Learning with Multi-Teacher Distillation
In this paper we propose the MTD method to find multiple diverse teachers for CIL.
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.
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.
Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning
In order to address catastrophic forgetting without overfitting on the rehearsal samples, we propose Augmentation Stability Rehearsal (ASR) in this paper, which selects the most representative and discriminative samples by estimating the augmentation stability for rehearsal.
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.
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.
Offset Bin Classification Network for Accurate Object Detection
However, this loss function applies the same penalties on different samples with large errors, which results in suboptimal regression networks and inaccurate offsets.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
