Searcharxiv⌕ Search

arXiv subjects

Jaegul Choo

Publications and source records attributed to Jaegul Choo.

At least 127 records · Page 7Linked to original sources
arXiv2024

Morphology-Aware Interactive Keypoint Estimation

Diagnosis based on medical images, such as X-ray images, often involves manual annotation of anatomical keypoints. However, this process involves significant human efforts and can thus be a bottleneck in the diagnostic process. To fully automate this procedure, deep-learning-based methods have been widely proposed and have achieved high performance in detecting keypoints in medical images. However, these methods still have clinical limitations: accuracy cannot be guaranteed for all cases, and it is necessary for doctors to double-check all predictions of models. In response, we propose a novel deep neural network that, given an X-ray image, automatically detects and refines the anatomical keypoints through a user-interactive system in which doctors can fix mispredicted keypoints with fewer clicks than needed during manual revision. Using our own collected data and the publicly available AASCE dataset, we demonstrate the effectiveness of the proposed method in reducing the annotation costs via extensive quantitative and qualitative results. A demo video of our approach is available on our project webpage.

cs.CV↗
arXiv2024

Self-Supervised Contrastive Learning for Long-term Forecasting

Long-term forecasting presents unique challenges due to the time and memory complexity of handling long sequences. Existing methods, which rely on sliding windows to process long sequences, struggle to effectively capture long-term variations that are partially caught within the short window (i.e., outer-window variations). In this paper, we introduce a novel approach that overcomes this limitation by employing contrastive learning and enhanced decomposition architecture, specifically designed to focus on long-term variations. To this end, our contrastive loss incorporates global autocorrelation held in the whole time series, which facilitates the construction of positive and negative pairs in a self-supervised manner. When combined with our decomposition networks, our contrastive learning significantly improves long-term forecasting performance. Extensive experiments demonstrate that our approach outperforms 14 baseline models in multiple experiments over nine long-term benchmarks, especially in challenging scenarios that require a significantly long output for forecasting. Source code is available at https://github.com/junwoopark92/Self-Supervised-Contrastive-Forecsating.

cs.LG↗
arXiv2024

MagiCapture: High-Resolution Multi-Concept Portrait Customization

Large-scale text-to-image models including Stable Diffusion are capable of generating high-fidelity photorealistic portrait images. There is an active research area dedicated to personalizing these models, aiming to synthesize specific subjects or styles using provided sets of reference images. However, despite the plausible results from these personalization methods, they tend to produce images that often fall short of realism and are not yet on a commercially viable level. This is particularly noticeable in portrait image generation, where any unnatural artifact in human faces is easily discernible due to our inherent human bias. To address this, we introduce MagiCapture, a personalization method for integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references. For instance, given a handful of random selfies, our fine-tuned model can generate high-quality portrait images in specific styles, such as passport or profile photos. The main challenge with this task is the absence of ground truth for the composed concepts, leading to a reduction in the quality of the final output and an identity shift of the source subject. To address these issues, we present a novel Attention Refocusing loss coupled with auxiliary priors, both of which facilitate robust learning within this weakly supervised learning setting. Our pipeline also includes additional post-processing steps to ensure the creation of highly realistic outputs. MagiCapture outperforms other baselines in both quantitative and qualitative evaluations and can also be generalized to other non-human objects.

cs.CV↗
arXiv2024

When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection

Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evolves over time, leading to a "new normal problem", where the distribution of normality can be changed due to the distribution shifts between training and test data. This paper highlights the prevalence of the new normal problem in unsupervised time-series anomaly detection studies. To tackle this issue, we propose a simple yet effective test-time adaptation strategy based on trend estimation and a self-supervised approach to learning new normalities during inference. Extensive experiments on real-world benchmarks demonstrate that incorporating the proposed strategy into the anomaly detector consistently improves the model's performance compared to the baselines, leading to robustness to the distribution shifts.

cs.LG↗
arXiv2023

iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds

Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging. While a few previous studies have attempted to leverage semi-automatic methods for cost-effective bounding box annotation, such methods have limitations in efficiently handling numerous multi-class objects. To effectively accelerate 3D annotation pipelines, we propose iDet3D, an efficient interactive 3D object detector. Supporting a user-friendly 2D interface, which can ease the cognitive burden of exploring 3D space to provide click interactions, iDet3D enables users to annotate the entire objects in each scene with minimal interactions. Taking the sparse nature of 3D point clouds into account, we design a negative click simulation (NCS) to improve accuracy by reducing false-positive predictions. In addition, iDet3D incorporates two click propagation techniques to take full advantage of user interactions: (1) dense click guidance (DCG) for keeping user-provided information throughout the network and (2) spatial click propagation (SCP) for detecting other instances of the same class based on the user-specified objects. Through our extensive experiments, we present that our method can construct precise annotations in a few clicks, which shows the practicality as an efficient annotation tool for 3D object detection.

cs.CV↗
arXiv2023

HPCClusterScape: Increasing Transparency and Efficiency of Shared High-Performance Computing Clusters for Large-scale AI Models

The emergence of large-scale AI models, like GPT-4, has significantly impacted academia and industry, driving the demand for high-performance computing (HPC) to accelerate workloads. To address this, we present HPCClusterScape, a visualization system that enhances the efficiency and transparency of shared HPC clusters for large-scale AI models. HPCClusterScape provides a comprehensive overview of system-level (e.g., partitions, hosts, and workload status) and application-level (e.g., identification of experiments and researchers) information, allowing HPC operators and machine learning researchers to monitor resource utilization and identify issues through customizable violation rules. The system includes diagnostic tools to investigate workload imbalances and synchronization bottlenecks in large-scale distributed deep learning experiments. Deployed in industrial-scale HPC clusters, HPCClusterScape incorporates user feedback and meets specific requirements. This paper outlines the challenges and prerequisites for efficient HPC operation, introduces the interactive visualization system, and highlights its contributions in addressing pain points and optimizing resource utilization in shared HPC clusters.

cs.HC↗
arXiv2023

PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning

In Reinforcement Learning (RL), enhancing sample efficiency is crucial, particularly in scenarios when data acquisition is costly and risky. In principle, off-policy RL algorithms can improve sample efficiency by allowing multiple updates per environment interaction. However, these multiple updates often lead the model to overfit to earlier interactions, which is referred to as the loss of plasticity. Our study investigates the underlying causes of this phenomenon by dividing plasticity into two aspects. Input plasticity, which denotes the model's adaptability to changing input data, and label plasticity, which denotes the model's adaptability to evolving input-output relationships. Synthetic experiments on the CIFAR-10 dataset reveal that finding smoother minima of loss landscape enhances input plasticity, whereas refined gradient propagation improves label plasticity. Leveraging these findings, we introduce the PLASTIC algorithm, which harmoniously combines techniques to address both concerns. With minimal architectural modifications, PLASTIC achieves competitive performance on benchmarks including Atari-100k and Deepmind Control Suite. This result emphasizes the importance of preserving the model's plasticity to elevate the sample efficiency in RL. The code is available at https://github.com/dojeon-ai/plastic.

cs.LG↗
arXiv2023

FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning

A User Next Location Prediction (UNLP) task, which predicts the next location that a user will move to given his/her trajectory, is an indispensable task for a wide range of applications. Previous studies using large-scale trajectory datasets in a single server have achieved remarkable performance in UNLP task. However, in real-world applications, legal and ethical issues have been raised regarding privacy concerns leading to restrictions against sharing human trajectory datasets to any other server. In response, Federated Learning (FL) has emerged to address the personal privacy issue by collaboratively training multiple clients (i.e., users) and then aggregating them. While previous studies employed FL for UNLP, they are still unable to achieve reliable performance because of the heterogeneity of clients' mobility. To tackle this problem, we propose the Federated Learning for Geographic Information (FedGeo), a FL framework specialized for UNLP, which alleviates the heterogeneity of clients' mobility and guarantees personal privacy protection. Firstly, we incorporate prior global geographic adjacency information to the local client model, since the spatial correlation between locations is trained partially in each client who has only a heterogeneous subset of the overall trajectories in FL. We also introduce a novel aggregation method that minimizes the gap between client models to solve the problem of client drift caused by differences between client models when learning with their heterogeneous data. Lastly, we probabilistically exclude clients with extremely heterogeneous data from the FL process by focusing on clients who visit relatively diverse locations. We show that FedGeo is superior to other FL methods for model performance in UNLP task. We also validated our model in a real-world application using our own customers' mobile phones and the FL agent system.

cs.CR↗
arXiv2023

StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task.The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON outperforms the baselines in qualitative and quantitative evaluation, showing promising quality in arbitrary person images. Our code is available at https://github.com/rlawjdghek/StableVITON.

cs.CV↗
arXiv2023

Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation

This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.

cs.AI↗
arXiv2023

Expression Domain Translation Network for Cross-domain Head Reenactment

Despite the remarkable advancements in head reenactment, the existing methods face challenges in cross-domain head reenactment, which aims to transfer human motions to domains outside the human, including cartoon characters. It is still difficult to extract motion from out-of-domain images due to the distinct appearances, such as large eyes. Recently, previous work introduced a large-scale anime dataset called AnimeCeleb and a cross-domain head reenactment model, including an optimization-based mapping function to translate the human domain's expressions to the anime domain. However, we found that the mapping function, which relies on a subset of expressions, imposes limitations on the mapping of various expressions. To solve this challenge, we introduce a novel expression domain translation network that transforms human expressions into anime expressions. Specifically, to maintain the geometric consistency of expressions between the input and output of the expression domain translation network, we employ a 3D geometric-aware loss function that reduces the distances between the vertices in the 3D mesh of the human and anime. By doing so, it forces high-fidelity and one-to-one mapping with respect to two cross-expression domains. Our method outperforms existing methods in both qualitative and quantitative analysis, marking a significant advancement in the field of cross-domain head reenactment.

cs.CV↗
arXiv2023

Learning to Discover Skills through Guidance

In the field of unsupervised skill discovery (USD), a major challenge is limited exploration, primarily due to substantial penalties when skills deviate from their initial trajectories. To enhance exploration, recent methodologies employ auxiliary rewards to maximize the epistemic uncertainty or entropy of states. However, we have identified that the effectiveness of these rewards declines as the environmental complexity rises. Therefore, we present a novel USD algorithm, skill discovery with guidance (DISCO-DANCE), which (1) selects the guide skill that possesses the highest potential to reach unexplored states, (2) guides other skills to follow guide skill, then (3) the guided skills are dispersed to maximize their discriminability in unexplored states. Empirical evaluation demonstrates that DISCO-DANCE outperforms other USD baselines in challenging environments, including two navigation benchmarks and a continuous control benchmark. Qualitative visualizations and code of DISCO-DANCE are available at https://mynsng.github.io/discodance.

cs.LG↗
arXiv2023

SimCKP: Simple Contrastive Learning of Keyphrase Representations

Keyphrase generation (KG) aims to generate a set of summarizing words or phrases given a source document, while keyphrase extraction (KE) aims to identify them from the text. Because the search space is much smaller in KE, it is often combined with KG to predict keyphrases that may or may not exist in the corresponding document. However, current unified approaches adopt sequence labeling and maximization-based generation that primarily operate at a token level, falling short in observing and scoring keyphrases as a whole. In this work, we propose SimCKP, a simple contrastive learning framework that consists of two stages: 1) An extractor-generator that extracts keyphrases by learning context-aware phrase-level representations in a contrastive manner while also generating keyphrases that do not appear in the document; 2) A reranker that adapts scores for each generated phrase by likewise aligning their representations with the corresponding document. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach, which outperforms the state-of-the-art models by a significant margin.

cs.CL↗
arXiv2023

Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction

Video-to-video translation aims to generate video frames of a target domain from an input video. Despite its usefulness, the existing networks require enormous computations, necessitating their model compression for wide use. While there exist compression methods that improve computational efficiency in various image/video tasks, a generally-applicable compression method for video-to-video translation has not been studied much. In response, we present Shortcut-V2V, a general-purpose compression framework for video-to-video translation. Shourcut-V2V avoids full inference for every neighboring video frame by approximating the intermediate features of a current frame from those of the previous frame. Moreover, in our framework, a newly-proposed block called AdaBD adaptively blends and deforms features of neighboring frames, which makes more accurate predictions of the intermediate features possible. We conduct quantitative and qualitative evaluations using well-known video-to-video translation models on various tasks to demonstrate the general applicability of our framework. The results show that Shourcut-V2V achieves comparable performance compared to the original video-to-video translation model while saving 3.2-5.7x computational cost and 7.8-44x memory at test time.

cs.CV↗
arXiv2023

Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations

Pre-training the embedding of a location generated from human mobility data has become a popular method for location based services. In practice, modeling the location embedding is too expensive, due to the large number of locations to be trained in situations with fine-grained resolution or extensive target regions. Previous studies have handled less than ten thousand distinct locations, which is insufficient in the real-world applications. To tackle this problem, we propose a Geo-Tokenizer, designed to efficiently reduce the number of locations to be trained by representing a location as a combination of several grids at different scales. In the Geo-Tokenizer, a grid at a larger scale shares the common set of grids at smaller scales, which is a key factor in reducing the size of the location vocabulary. The sequences of locations preprocessed with the Geo-Tokenizer are utilized by a causal location embedding model to capture the temporal dependencies of locations. This model dynamically calculates the embedding vector of a target location, which varies depending on its trajectory. In addition, to efficiently pre-train the location embedding model, we propose the Hierarchical Auto-regressive Location Model objective to effectively train decomposed locations in the Geo-Tokenizer. We conducted experiments on two real-world user trajectory datasets using our pre-trained location model. The experimental results show that our model significantly improves the performance of downstream tasks with fewer model parameters compared to existing location embedding methods.

cs.AI↗
arXiv2023

PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction

Understanding the movement patterns of objects (e.g., humans and vehicles) in a city is essential for many applications, including city planning and management. This paper proposes a method for predicting future city-wide crowd flows by modeling the spatio-temporal patterns of historical crowd flows in fine-grained city-wide maps. We introduce a novel neural network named PArallel Spatio-Temporal Attention with spatial auto-correlation gating (PASTA) that effectively captures the irregular spatio-temporal patterns of fine-grained maps. The novel components in our approach include spatial auto-correlation gating, multi-scale residual block, and temporal attention gating module. The spatial auto-correlation gating employs the concept of spatial statistics to identify irregular spatial regions. The multi-scale residual block is responsible for handling multiple range spatial dependencies in the fine-grained map, and the temporal attention gating filters out irrelevant temporal information for the prediction. The experimental results demonstrate that our model outperforms other competing baselines, especially under challenging conditions that contain irregular spatial regions. We also provide a qualitative analysis to derive the critical time information where our model assigns high attention scores in prediction.

cs.LG↗
arXiv2023

PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration

Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document. A key challenge in DocRE is the cost of annotating such data which requires intensive human effort. Thus, we investigate the case of DocRE in a low-resource setting, and we find that existing models trained on low data overestimate the NA ("no relation") label, causing limited performance. In this work, we approach the problem from a calibration perspective and propose PRiSM, which learns to adapt logits based on relation semantic information. We evaluate our method on three DocRE datasets and demonstrate that integrating existing models with PRiSM improves performance by as much as 26.38 F1 score, while the calibration error drops as much as 36 times when trained with about 3% of data. The code is publicly available at https://github.com/brightjade/PRiSM.

cs.CL↗
arXiv2023

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

Time-series forecasting models often encounter abrupt changes in a given period of time which generally occur due to unexpected or unknown events. Despite their scarce occurrences in the training set, abrupt changes incur loss that significantly contributes to the total loss. Therefore, they act as noisy training samples and prevent the model from learning generalizable patterns, namely the normal states. Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states. For the reweighting framework, we first define a measurement termed Local Discrepancy (LD) which measures the degree of abruptness of a change in a given period of time. Since a training set is mostly composed of normal states, we then consider how frequently the temporal changes appear in the training set based on LD. Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with various in-output sequence lengths, we demonstrate that applying our reweighting framework reduces MSE by 10.1% on average and by up to 18.6% in the state-of-the-art model.

cs.LG↗