Author name cluster

Simyung Chang

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

InfiniPot-V: Memory-Constrained KV Cache Compression for Streaming Video Understanding

Minsoo Kim
Kyuhong Shim
Jungwook Choi
Simyung Chang

Modern multimodal large language models (MLLMs) can reason over hour-long video, yet their key–value (KV) cache grows linearly with time—quickly exceeding the fixed memory of phones, AR glasses, and edge robots. Prior compression schemes either assume the whole video and user query are available offline or must first build the full cache, so memory still scales with stream length. InfiniPot-V is the first training-free, query-agnostic framework that enforces a hard, length-independent memory cap for \textit{streaming} video understanding. During video encoding it monitors the cache and, once a user-set threshold is reached, runs a lightweight compression pass that (i) removes temporally redundant tokens via Temporal-axis Redundancy (TaR) metric and (ii) keeps semantically significant tokens via Value-Norm (VaN) ranking. Across four open-source MLLMs and four long-video and streaming-video benchmarks, InfiniPot-V cuts peak GPU memory by up to 94\%, sustains real-time generation, and matches or surpasses full-cache accuracy—even in multi-turn dialogues. By dissolving the KV cache bottleneck without retraining or query knowledge, InfiniPot-V closes the gap for on-device streaming video assistants.

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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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ICML Conference 2022 Conference Paper

Variational On-the-Fly Personalization

Jangho Kim
Juntae Lee
Simyung Chang
Nojun Kwak

With the development of deep learning (DL) technologies, the demand for DL-based services on personal devices, such as mobile phones, also increases rapidly. In this paper, we propose a novel personalization method, Variational On-the-Fly Personalization. Compared to the conventional personalization methods that require additional fine-tuning with personal data, the proposed method only requires forwarding a handful of personal data on-the-fly. Assuming even a single personal data can convey the characteristics of a target person, we develop the variational hyper-personalizer to capture the weight distribution of layers that fits the target person. In the testing phase, the hyper-personalizer estimates the model’s weights on-the-fly based on personality by forwarding only a small amount of (even a single) personal enrollment data. Hence, the proposed method can perform the personalization without any training software platform and additional cost in the edge device. In experiments, we show our approach can effectively generate reliable personalized models via forwarding (not back-propagating) a handful of samples.

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AAAI Conference 2020 Conference Paper

URNet: User-Resizable Residual Networks with Conditional Gating Module

Sangho Lee
Simyung Chang
Nojun Kwak

Convolutional Neural Networks are widely used to process spatial scenes, but their computational cost is ﬁxed and depends on the structure of the network used. There are methods to reduce the cost by compressing networks or varying its computational path dynamically according to the input image. However, since a user can not control the size of the learned model, it is difﬁcult to respond dynamically if the amount of service requests suddenly increases. We propose User-Resizable Residual Networks (URNet), which allows users to adjust the computational cost of the network as needed during evaluation. URNet includes Conditional Gating Module (CGM) that determines the use of each residual block according to the input image and the desired cost. CGM is trained in a supervised manner using the newly proposed scale(cost) loss and its corresponding training methods. UR- Net can control the amount of computation and its inference path according to user’s demand without degrading the accuracy signiﬁcantly. In the experiments on ImageNet, URNet based on ResNet-101 maintains the accuracy of the baseline even when resizing it to approximately 80% of the original network, and demonstrates only about 1% accuracy degradation when using about 65% of the computation.

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NeurIPS Conference 2018 Conference Paper

Genetic-Gated Networks for Deep Reinforcement Learning

Simyung Chang
John Yang
Jaeseok Choi
Nojun Kwak

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method can take both advantages of gradient-free optimization and gradient-based optimization methods, of which the former is effective for problems with multiple local minima, while the latter can quickly find local minima. In addition, multiple chromosomes can define different models, making it easy to construct multiple models and can be effectively applied to problems that require multiple models. We show that this G2N can be applied to typical reinforcement learning algorithms to achieve a large improvement in sample efficiency and performance.

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