32 citations · 53 across the 13 of their papers we have counts for
17 papers
GLARE: Generating Listening Heads with Appropriate Reactions
Zikai Liao, Yumin Suh, Yi Ouyang +3
While talking head generation has advanced rapidly, generating natural listener behavior in dyadic conversations, which know when to react, how to react, and with what type of resp…
ST-VLM: Kinematic Instruction Tuning for Spatio-Temporal Reasoning in Vision-Language Models
Dohwan Ko, Sihyeon Kim, Yumin Suh +4
Spatio-temporal reasoning is essential in understanding real-world environments in various fields, eg, autonomous driving and sports analytics. Recent advances have improved the sp…
Image-Specific Adaptation of Transformer Encoders for Compute-Efficient Segmentation
Manyi Yao, Abhishek Aich, Yumin Suh +3
Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segme…
Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation
Abhishek Aich, Yumin Suh, Samuel Schulter +1
A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency be…
Generating Enhanced Negatives for Training Language-Based Object Detectors
Shiyu Zhao, Long Zhao, Vijay Kumar B. G +4
The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotatio…
Efficient Controllable Multi-Task Architectures
Abhishek Aich, Samuel Schulter, Amit K. Roy-Chowdhury +2
We aim to train a multi-task model such that users can adjust the desired compute budget and relative importance of task performances after deployment, without retraining. This ena…
Taming Self-Training for Open-Vocabulary Object Detection
Shiyu Zhao, Samuel Schulter, Long Zhao +5
Recent studies have shown promising performance in open-vocabulary object detection (OVD) by utilizing pseudo labels (PLs) from pretrained vision and language models (VLMs). Howeve…
OmniLabel: A Challenging Benchmark for Language-Based Object Detection
Samuel Schulter, Vijay Kumar B G, Yumin Suh +4
Language-based object detection is a promising direction towards building a natural interface to describe objects in images that goes far beyond plain category names. While recent…
Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation
Weijian Deng, Yumin Suh, Stephen Gould +1
This work aims to assess how well a model performs under distribution shifts without using labels. While recent methods study prediction confidence, this work reports prediction di…
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels
Jiho Choi, Junghoon Park, Woocheol Kim +3
The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than…
Controllable Dynamic Multi-Task Architectures
Dripta S. Raychaudhuri, Yumin Suh, Samuel Schulter +4
Multi-task learning commonly encounters competition for resources among tasks, specifically when model capacity is limited. This challenge motivates models which allow control over…
On Generalizing Beyond Domains in Cross-Domain Continual Learning
Christian Simon, Masoud Faraki, Yi-Hsuan Tsai +5
Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowled…
Learning Semantic Segmentation from Multiple Datasets with Label Shifts
Dongwan Kim, Yi-Hsuan Tsai, Yumin Suh +4
With increasing applications of semantic segmentation, numerous datasets have been proposed in the past few years. Yet labeling remains expensive, thus, it is desirable to jointly…
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains
Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai +2
Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as…
Learning to Optimize Domain Specific Normalization for Domain Generalization
Seonguk Seo, Yumin Suh, Dongwan Kim +3
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to in…
Part-Aligned Bilinear Representations for Person Re-identification
Yumin Suh, Jingdong Wang, Siyu Tang +2
We propose a novel network that learns a part-aligned representation for person re-identification. It handles the body part misalignment problem, that is, body parts are misaligned…
Holistic Planimetric prediction to Local Volumetric prediction for 3D Human Pose Estimation
Gyeongsik Moon, Ju Yong Chang, Yumin Suh +1
We propose a novel approach to 3D human pose estimation from a single depth map. Recently, convolutional neural network (CNN) has become a powerful paradigm in computer vision. Man…