246 citations · 404 across the 15 of their papers we have counts for
16 papers
Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding
Sunoh Kim, Jungchan Cho, Joonsang Yu +2
In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by…
Balanced Energy Regularization Loss for Out-of-distribution Detection
Hyunjun Choi, Hawook Jeong, Jin Young Choi
In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal lo…
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models
Seulki Park, Daeho Um, Hajung Yoon +3
With the extensive use of vision-language models in various downstream tasks, evaluating their robustness is crucial. In this paper, we propose a benchmark for assessing the robust…
Position-aware Location Regression Network for Temporal Video Grounding
Sunoh Kim, Kimin Yun, Jin Young Choi
The key to successful grounding for video surveillance is to understand a semantic phrase corresponding to important actors and objects. Conventional methods ignore comprehensive c…
The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
Seulki Park, Youngkyu Hong, Byeongho Heo +2
The problem of class imbalanced data is that the generalization performance of the classifier deteriorates due to the lack of data from minority classes. In this paper, we propose…
Influence-Balanced Loss for Imbalanced Visual Classification
Seulki Park, Jongin Lim, Younghan Jeon +1
In this paper, we propose a balancing training method to address problems in imbalanced data learning. To this end, we derive a new loss used in the balancing training phase that a…
Class-Attentive Diffusion Network for Semi-Supervised Classification
Jongin Lim, Daeho Um, Hyung Jin Chang +2
Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal…
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks
Youngmin Ro, Jin Young Choi
Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single…
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold
YoungJoon Yoo, Sangdoo Yun, Hyung Jin Chang +2
This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression method…
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators
Dae Ung Jo, ByeongJu Lee, Jongwon Choi +2
In this paper, we propose a novel structure for a cross-modal data association, which is inspired by the recent research on the associative learning structure of the brain. We form…
A Comprehensive Overhaul of Feature Distillation
Byeongho Heo, Jeesoo Kim, Sangdoo Yun +3
We investigate the design aspects of feature distillation methods achieving network compression and propose a novel feature distillation method in which the distillation loss is de…
Skeleton-based Action Recognition of People Handling Objects
Sunoh Kim, Kimin Yun, Jongyoul Park +1
In visual surveillance systems, it is necessary to recognize the behavior of people handling objects such as a phone, a cup, or a plastic bag. In this paper, to address this proble…
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
Youngmin Ro, Jongwon Choi, Dae Ung Jo +3
In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a la…
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
Byeongho Heo, Minsik Lee, Sangdoo Yun +1
An activation boundary for a neuron refers to a separating hyperplane that determines whether the neuron is activated or deactivated. It has been long considered in neural networks…
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
Byeongho Heo, Minsik Lee, Sangdoo Yun +1
Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good te…
Context-aware Deep Feature Compression for High-speed Visual Tracking
Jongwon Choi, Hyung Jin Chang, Tobias Fischer +5
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The m…