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03/25/2023
Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection
Prompt-OVD is an efficient and effective framework for open-vocabulary o...
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03/22/2023
Q-HyViT: Post-Training Quantization for Hybrid Vision Transformer with Bridge Block Reconstruction
Recently, vision transformers (ViT) have replaced convolutional neural n...
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03/22/2023
Re-thinking Federated Active Learning based on Inter-class Diversity
Although federated learning has made awe-inspiring advances, most studie...
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10/13/2022
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Unlabeled data examples awaiting annotations contain open-set noise inev...
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07/19/2022
Time Is MattEr: Temporal Self-supervision for Video Transformers
Understanding temporal dynamics of video is an essential aspect of learn...
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07/01/2022
e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce
Understanding vision and language representations of product content is ...
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05/30/2022
Dataset Condensation via Efficient Synthetic-Data Parameterization
The great success of machine learning with massive amounts of data comes...
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05/11/2022
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Cross-domain few-shot learning (CD-FSL), where there are few target samp...
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05/03/2022
FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning
Robustness is becoming another important challenge of federated learning...
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03/29/2022
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries
Learning under a continuously changing data distribution with incorrect ...
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03/19/2022
Meta-Learning for Online Update of Recommender Systems
Online recommender systems should be always aligned with users' current ...
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02/01/2022
Understanding Cross-Domain Few-Shot Learning: An Experimental Study
Cross-domain few-shot learning has drawn increasing attention for handli...
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12/13/2021
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
Software 2.0 is a fundamental shift in software engineering where machin...
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12/08/2020
Two-Phase Learning for Overcoming Noisy Labels
To counter the challenge associated with noise labels, the learning stra...
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07/16/2020
Learning from Noisy Labels with Deep Neural Networks: A Survey
Deep learning has achieved remarkable success in numerous domains with h...
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11/19/2019
Prestopping: How Does Early Stopping Help Generalization against Label Noise?
Noisy labels are very common in real-world training data, which lead to ...
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11/19/2019
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
The performance of deep neural networks is significantly affected by how...
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10/23/2019
