3 citations · 5 across the 6 of their papers we have counts for
8 papers
How Much Can We Prune? Exploring the Performance Limits of Visual Token Pruning
Janghoon Cho, Shubhankar Borse, Reza Pourreza +4
Visual token pruning is increasingly used to reduce the memory and inference costs of vision-language models. Existing work has largely focused on designing method-specific selecto…
FLoC: Facility Location-Based Efficient Visual Token Compression for Long Video Understanding
Janghoon Cho, Jungsoo Lee, Munawar Hayat +3
Recent studies in long video understanding have harnessed the advanced visual-language reasoning capabilities of Large Multimodal Models (LMMs), driving the evolution of video-LMMs…
Generalized Contrastive Learning for Universal Multimodal Retrieval
Jungsoo Lee, Janghoon Cho, Hyojin Park +4
Despite their consistent performance improvements, cross-modal retrieval models (e.g., CLIP) show degraded performances with retrieving keys composed of fused image-text modality (…
SubSpectral Normalization for Neural Audio Data Processing
Simyung Chang, Hyoungwoo Park, Janghoon Cho +3
Convolutional Neural Networks are widely used in various machine learning domains. In image processing, the features can be obtained by applying 2D convolution to all spatial dimen…
End-to-End Lane Marker Detection via Row-wise Classification
Seungwoo Yoo, Heeseok Lee, Heesoo Myeong +4
In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem pe…
Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning
Hyoungwoo Park, Sungrack Yun, Jungyun Eum +2
This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tr…
Acoustic Scene Classification Based on a Large-margin Factorized CNN
Janghoon Cho, Sungrack Yun, Hyoungwoo Park +2
In this paper, we present an acoustic scene classification framework based on a large-margin factorized convolutional neural network (CNN). We adopt the factorized CNN to learn the…
An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network
Sungrack Yun, Janghoon Cho, Jungyun Eum +2
This paper presents an end-to-end text-independent speaker verification framework by jointly considering the speaker embedding (SE) network and automatic speech recognition (ASR) n…