246 citations · 404 across the 15 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…