Density Map Distillation for Incremental Object Counting
- URL: http://arxiv.org/abs/2304.05255v1
- Date: Tue, 11 Apr 2023 14:46:21 GMT
- Title: Density Map Distillation for Incremental Object Counting
- Authors: Chenshen Wu and Joost van de Weijer
- Abstract summary: A na"ive approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks.
We propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD)
During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks.
- Score: 37.982124268097
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate the problem of incremental learning for object counting, where
a method must learn to count a variety of object classes from a sequence of
datasets. A na\"ive approach to incremental object counting would suffer from
catastrophic forgetting, where it would suffer from a dramatic performance drop
on previous tasks. In this paper, we propose a new exemplar-free functional
regularization method, called Density Map Distillation (DMD). During training,
we introduce a new counter head for each task and introduce a distillation loss
to prevent forgetting of previous tasks. Additionally, we introduce a
cross-task adaptor that projects the features of the current backbone to the
previous backbone. This projector allows for the learning of new features while
the backbone retains the relevant features for previous tasks. Finally, we set
up experiments of incremental learning for counting new objects. Results
confirm that our method greatly reduces catastrophic forgetting and outperforms
existing methods.
Related papers
- MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning
for Multimodal Video Captioning [10.95493493610559]
We propose a method to Mitigate Catastrophic Forgetting in class-incremental learning for multimodal Video Captioning (MCF-VC)
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)
Our experiments on the public dataset MSR-VTT show that the proposed method significantly resists the forgetting of previous tasks without replaying old samples, and performs well on the new task.
arXiv Detail & Related papers (2024-02-27T16:54:08Z) - Enhancing Consistency and Mitigating Bias: A Data Replay Approach for
Incremental Learning [100.7407460674153]
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks.
To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks.
However, it is not expected in practice considering the memory constraint or data privacy issue.
As a replacement, data-free data replay methods are proposed by inverting samples from the classification model.
arXiv Detail & Related papers (2024-01-12T12:51:12Z) - Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class
Incremental Learning [64.14254712331116]
Non-exemplar class incremental learning aims to learn both the new and old tasks without accessing any training data from the past.
We propose a novel framework of fine-grained knowledge selection and restoration.
arXiv Detail & Related papers (2023-12-20T02:34:11Z) - FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly
Supervised Object Localization [10.08410402383604]
This work addresses the task of class-incremental weakly supervised object localization (CI-WSOL)
The goal is to incrementally learn object localization for novel classes using only image-level annotations while retaining the ability to localize previously learned classes.
We first present a strong baseline method for CI-WSOL by adapting the strategies of class-incremental classifiers to catastrophic forgetting.
We then propose the feature drift compensation network to compensate for the effects of feature drifts on class scores and localization maps.
arXiv Detail & Related papers (2023-09-17T01:10:45Z) - CLR: Channel-wise Lightweight Reprogramming for Continual Learning [63.94773340278971]
Continual learning aims to emulate the human ability to continually accumulate knowledge over sequential tasks.
The main challenge is to maintain performance on previously learned tasks after learning new tasks.
We propose a Channel-wise Lightweight Reprogramming approach that helps convolutional neural networks overcome catastrophic forgetting.
arXiv Detail & Related papers (2023-07-21T06:56:21Z) - Prototype-Sample Relation Distillation: Towards Replay-Free Continual
Learning [14.462797749666992]
We propose a holistic approach to jointly learn the representation and class prototypes.
We propose a novel distillation loss that constrains class prototypes to maintain relative similarities as compared to new task data.
This method yields state-of-the-art performance in the task-incremental setting.
arXiv Detail & Related papers (2023-03-26T16:35:45Z) - ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning [59.08197876733052]
Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks.
Sometimes, learning multiple tasks simultaneously results in lower accuracy than learning only the target task, known as negative transfer.
ForkMerge is a novel approach that periodically forks the model into multiple branches, automatically searches the varying task weights.
arXiv Detail & Related papers (2023-01-30T02:27:02Z) - DIODE: Dilatable Incremental Object Detection [15.59425584971872]
Conventional deep learning models lack the capability of preserving previously learned knowledge.
We propose a dilatable incremental object detector (DIODE) for multi-step incremental detection tasks.
Our method achieves up to 6.4% performance improvement by increasing the number of parameters by just 1.2% for each newly learned task.
arXiv Detail & Related papers (2021-08-12T09:45:57Z) - Class-incremental learning: survey and performance evaluation on image
classification [38.27344435075399]
Incremental learning allows for efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data.
The main challenge for incremental learning is catastrophic forgetting, which refers to the precipitous drop in performance on previously learned tasks after learning a new one.
Recently, we have seen a shift towards class-incremental learning where the learner must discriminate at inference time between all classes seen in previous tasks without recourse to a task-ID.
arXiv Detail & Related papers (2020-10-28T23:28:15Z) - Incremental Object Detection via Meta-Learning [77.55310507917012]
We propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared.
In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection.
arXiv Detail & Related papers (2020-03-17T13:40:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.