Author name cluster

Sungrack Yun

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7 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans

Shubhankar Borse
Seokeon Choi
Sunghyun Park
Jeongho Kim
Shreya Kadambi
Risheek Garrepalli
Sungrack Yun
Durga Malladi

Generation of images containing multiple humans, performing complex actions, while preserving their facial identities, is a significant challenge. A major factor contributing to this is the lack of a a dedicated benchmark. To address this, we introduce MultiHuman-Testbench, a novel benchmark for rigorously evaluating generative models for multi-human generation. The benchmark comprises 1800 samples, including carefully curated text prompts, describing a range of simple to complex human actions. These prompts are matched with a total of 5, 550 unique human face images, sampled uniformly to ensure diversity across age, ethnic background, and gender. Alongside captions, we provide human-selected pose conditioning images which accurately match the prompt. We propose a multi-faceted evaluation suite employing four key metrics to quantify face count, ID similarity, prompt alignment, and action detection. We conduct a thorough evaluation of a diverse set of models, including zero-shot approaches and training-based methods, with and without regional priors. We also propose novel techniques to incorporate image and region isolation using human segmentation and Hungarian matching, significantly improving ID similarity. Our proposed benchmark and key findings provide valuable insights and a standardized tool for advancing research in multi-human image generation.

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NeurIPS Conference 2024 Conference Paper

Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models

Wonguk Cho
Seokeon Choi
Debasmit Das
Matthias Reisser
Taesup Kim
Sungrack Yun
Fatih Porikli

Recent advancements in text-to-image diffusion models have enabled the personalization of these models to generate custom images from textual prompts. This paper presents an efficient LoRA-based personalization approach for on-device subject-driven generation, where pre-trained diffusion models are fine-tuned with user-specific data on resource-constrained devices. Our method, termed Hollowed Net, enhances memory efficiency during fine-tuning by modifying the architecture of a diffusion U-Net to temporarily remove a fraction of its deep layers, creating a hollowed structure. This approach directly addresses on-device memory constraints and substantially reduces GPU memory requirements for training, in contrast to previous methods that primarily focus on minimizing training steps and reducing the number of parameters to update. Additionally, the personalized Hollowed Net can be transferred back into the original U-Net, enabling inference without additional memory overhead. Quantitative and qualitative analyses demonstrate that our approach not only reduces training memory to levels as low as those required for inference but also maintains or improves personalization performance compared to existing methods.

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ICLR Conference 2022 Conference Paper

ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning

Debasmit Das
Sungrack Yun
Fatih Porikli

Most current few-shot learning methods train a model from abundantly labeled base category data and then transfer and adapt the model to sparsely labeled novel category data. These methods mostly generalize well on novel categories from the same domain as the base categories but perform poorly for distant domain categories. In this paper, we propose a framework for few-shot learning coined as ConFeSS (Contrastive Learning and Feature Selection System) that tackles large domain shift between base and novel categories. The first step of our framework trains a feature extracting backbone with the contrastive loss on the base category data. Since the contrastive loss does not use supervision, the features can generalize better to distant target domains. For the second step, we train a masking module to select relevant features that are more suited to target domain classification. Finally, a classifier is fine-tuned along with the backbone such that the backbone produces features similar to the relevant ones. To evaluate our framework, we tested it on a recently introduced cross-domain few-shot learning benchmark. Experimental results demonstrate that our framework outperforms all meta-learning approaches and produces competitive results against recent cross-domain methods. Additional analyses are also performed to better understand our framework.

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AAAI Conference 2022 Conference Paper

Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks

Jun-Tae Lee
Hyunsin Park
Sungrack Yun
Simyung Chang

It has been crucial to leverage the rich information of multiple modalities in many tasks. Existing works have tried to design multi-modal networks with descent multi-modal fusion modules. Instead, we focus on improving generalization capability of multi-modal networks, especially the fusion module. Viewing the multi-modal data as different projections of information, we first observe that bad projection can cause poor generalization behaviors of multi-modal networks. Then, motivated by well-generalized network’s low sensitivity to perturbation, we propose a novel multi-modal training method, multi-head modularization (MHM). We modularize a multimodal network as a series of uni-modal embedding, multimodal embedding, and task-specific head modules. Also, for training, we exploit multiple head modules learned with different datasets, swapping each other. From this, we can make the multi-modal embedding module robust to all the heads with different generalization behaviors. In testing phase, we select one of the head modules not to increase the computational cost. Owing to the perturbation of head modules, though including one selected head, the deployed network is more well-generalized compared to the simply end-to-end learned. We verify the effectiveness of MHM on various multi-modal tasks. We use the state-of-the-art methods as baselines, and show notable performance gain for all the baselines.

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ICLR Conference 2021 Conference Paper

Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization

Juntae Lee
Mihir Jain
Hyoungwoo Park
Sungrack Yun

Temporally localizing actions in videos is one of the key components for video understanding. Learning from weakly-labeled data is seen as a potential solution towards avoiding expensive frame-level annotations. Different from other works which only depend on visual-modality, we propose to learn richer audiovisual representation for weakly-supervised action localization. First, we propose a multi-stage cross-attention mechanism to collaboratively fuse audio and visual features, which preserves the intra-modal characteristics. Second, to model both foreground and background frames, we construct an open-max classifier that treats the background class as an open-set. Third, for precise action localization, we design consistency losses to enforce temporal continuity for the action class prediction, and also help with foreground-prediction reliability. Extensive experiments on two publicly available video-datasets (AVE and ActivityNet1.2) show that the proposed method effectively fuses audio and visual modalities, and achieves the state-of-the-art results for weakly-supervised action localization.

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ICML Conference 2021 Conference Paper

Federated Learning of User Verification Models Without Sharing Embeddings

Hossein Hosseini
Hyunsin Park
Sungrack Yun
Christos Louizos
Joseph Soriaga
Max Welling

We consider the problem of training User Verification (UV) models in federated setup, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users. To address this problem, we propose Federated User Verification (FedUV), a framework in which users jointly learn a set of vectors and maximize the correlation of their instance embeddings with a secret linear combination of those vectors. We show that choosing the linear combinations from the codewords of an error-correcting code allows users to collaboratively train the model without revealing their embedding vectors. We present the experimental results for user verification with voice, face, and handwriting data and show that FedUV is on par with existing approaches, while not sharing the embeddings with other users or the server.

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NeurIPS Conference 2012 Conference Paper

Phoneme Classification using Constrained Variational Gaussian Process Dynamical System

Hyunsin Park
Sungrack Yun
Sanghyuk Park
Jongmin Kim
Chang Yoo

This paper describes a new acoustic model based on variational Gaussian process dynamical system (VGPDS) for phoneme classification. The proposed model overcomes the limitations of the classical HMM in modeling the real speech data, by adopting a nonlinear and nonparametric model. In our model, the GP prior on the dynamics function enables representing the complex dynamic structure of speech, while the GP prior on the emission function successfully models the global dependency over the observations. Additionally, we introduce variance constraint to the original VGPDS for mitigating sparse approximation error of the kernel matrix. The effectiveness of the proposed model is demonstrated with extensive experimental results including parameter estimation, classification performance on the synthetic and benchmark datasets.

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