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Found 12 papers, 5 papers with code
Adaptive Shortcut Debiasing for Online Continual Learning
We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment.
Active Prompt Learning in Vision Language Models
In response to this inquiry, we observe that (1) simply applying a conventional active learning framework to pre-trained VLMs even may degrade performance compared to random selection because of the class imbalance in labeling candidates, and (2) the knowledge of VLMs can provide hints for achieving the balance before labeling.
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
We identify the inadequacy of universal and specific prompting in handling these dynamic shifts.
Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection
Prompt-OVD is an efficient and effective framework for open-vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to detect objects in both base and novel classes.
Generating Instance-level Prompts for Rehearsal-free Continual Learning
However, we observe that the use of a prompt pool creates a domain scalability problem between pre-training and continual learning.
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Unlabeled data examples awaiting annotations contain open-set noise inevitably.
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
A large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning scenarios under noisy data streams are yet underexplored.
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels
Specifically, we argue the importance of both diversity and purity of examples in the episodic memory of continual learning models.
Rainbow Memory: Continual Learning with a Memory of Diverse Samples
Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realistic rather artificial.
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples
The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognition models.
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules.
Ranked #1 on
Portrait Segmentation
on EG1800
ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules
In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight segmentation models.
