1 citations · 1 across the 6 of their papers we have counts for
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SOUPLE: Enhancing Audio-Visual Localization and Segmentation with Learnable Prompt Contexts
Khanh Binh Nguyen, Chae Jung Park
Large-scale pre-trained image-text models exhibit robust multimodal representations, yet applying the Contrastive Language-Image Pre-training (CLIP) model to audio-visual localizat…
Symmetric masking strategy enhances the performance of Masked Image Modeling
Khanh-Binh Nguyen, Chae Jung Park
Masked Image Modeling (MIM) is a technique in self-supervised learning that focuses on acquiring detailed visual representations from unlabeled images by estimating the missing pix…
SAVE: Segment Audio-Visual Easy way using Segment Anything Model
Khanh-Binh Nguyen, Chae Jung Park
The primary aim of Audio-Visual Segmentation (AVS) is to precisely identify and locate auditory elements within visual scenes by accurately predicting segmentation masks at the pix…
Retro: Reusing teacher projection head for efficient embedding distillation on Lightweight Models via Self-supervised Learning
Khanh-Binh Nguyen, Chae Jung Park
Self-supervised learning (SSL) is gaining attention for its ability to learn effective representations with large amounts of unlabeled data. Lightweight models can be distilled fro…
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
Khanh-Binh Nguyen
Semi-supervised learning (SSL) has become popular in recent years because it allows the training of a model using a large amount of unlabeled data. However, one issue that many SSL…
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
Khanh-Binh Nguyen
Recent studies on semi-supervised learning (SSL) have achieved great success. Despite their promising performance, current state-of-the-art methods tend toward increasingly complex…