24 citations · 30 across the 30 of their papers we have counts for
32 papers
BeaconKV: Key-Value Cache Compression Guided by Beacon Queries for Efficient Large Reasoning Model Inference
Janghyeon Kim, Minsoo Kim, Kyuhong Shim +1
Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generation, but the resulting key-value (KV) cache grows linearly with sequen…
RealSWE: A Compositional Evaluation of Coding Agents under Realistic User Requests
Gyuhyeong Kim, Hyojung Gwon, Jeonghyeon Kim +2
Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues: long, structured, and information-rich. Real user…
Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference
Wooje Park, Insu Lee, Minyoung Noh +4
Diffusion large language models (dLLMs) offer a promising alternative to autoregressive generation by decoding multiple tokens in parallel through iterative denoising. However, inc…
Advancing Multi-Robot Networks via MLLM-Driven Sensing, Communication, and Computation: A Comprehensive Survey
Hyun Jong Yang, Howon Lee, Kyuhong Shim +10
Imagine advanced humanoid robots, powered by multimodal large language models (MLLMs), coordinating missions across industries like warehouse logistics, manufacturing, and safety r…
Adaptive Capacity Allocation for Vision Language Action Fine-tuning
Donghoon Kim, Minji Bae, Unghui Nam +4
Vision language action models (VLAs) are increasingly used for Physical AI, but deploying a pre-trained VLA model to unseen environments, embodiments, or tasks still requires adapt…
Revealing Multi-View Hallucination in Large Vision-Language Models
Wooje Park, Insu Lee, Soohyun Kim +4
Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captured from diverse viewpoints. Despite this growing use, current LVLMs often gener…
Learning Contextual Retrieval for Robust Conversational Search
Seunghan Yang, Juntae Lee, Jihwan Bang +3
Effective conversational search demands a deep understanding of user intent across multiple dialogue turns. Users frequently use abbreviations and shift topics in the middle of con…
InfiniPot-V: Memory-Constrained KV Cache Compression for Streaming Video Understanding
Minsoo Kim, Kyuhong Shim, Jungwook Choi +1
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 phone…
Towards Comprehensive Scene Understanding: Integrating First and Third-Person Views for LVLMs
Insu Lee, Wooje Park, Jaeyun Jang +3
Large vision-language models (LVLMs) are increasingly deployed in interactive applications such as virtual and augmented reality, where a first-person (egocentric) view captured by…
Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models
Donghoon Kim, Minji Bae, Kyuhong Shim +1
Text-to-image generative models like DALL-E and Stable Diffusion have revolutionized visual content creation across various applications, including advertising, personalized media,…
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device
Juntae Lee, Jihwan Bang, Seunghan Yang +2
Retrieval-augmented generation (RAG) with large language models (LLMs) is especially valuable in specialized domains, where precision is critical. To more specialize the LLMs into…
Learning Primitive Relations for Compositional Zero-Shot Learning
Insu Lee, Jiseob Kim, Kyuhong Shim +1
Compositional Zero-Shot Learning (CZSL) aims to identify unseen state-object compositions by leveraging knowledge learned from seen compositions. Existing approaches often independ…
Unlocking Transfer Learning for Open-World Few-Shot Recognition
Byeonggeun Kim, Juntae Lee, Kyuhong Shim +1
Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to categorize inputs into known categories, termed closed-set classes, while identifying open-…
Preserving Pre-trained Representation Space: On Effectiveness of Prefix-tuning for Large Multi-modal Models
Donghoon Kim, Gusang Lee, Kyuhong Shim +1
Recently, we have observed that Large Multi-modal Models (LMMs) are revolutionizing the way machines interact with the world, unlocking new possibilities across various multi-modal…
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP
Eunji Kim, Kyuhong Shim, Simyung Chang +1
A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in translating textual input into an embedding space shared with images, thereby facilitating the…
InfiniPot: Infinite Context Processing on Memory-Constrained LLMs
Minsoo Kim, Kyuhong Shim, Jungwook Choi +1
Handling long input contexts remains a significant challenge for Large Language Models (LLMs), particularly in resource-constrained environments such as mobile devices. Our work ai…
Crayon: Customized On-Device LLM via Instant Adapter Blending and Edge-Server Hybrid Inference
Jihwan Bang, Juntae Lee, Kyuhong Shim +2
The customization of large language models (LLMs) for user-specified tasks gets important. However, maintaining all the customized LLMs on cloud servers incurs substantial memory a…
Role of Sensing and Computer Vision in 6G Wireless Communications
Seungnyun Kim, Jihoon Moon, Jinhong Kim +5
Recently, we are witnessing the remarkable progress and widespread adoption of sensing technologies in autonomous driving, robotics, and metaverse. Considering the rapid advancemen…
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization
Jiyoung Kim, Kyuhong Shim, Insu Lee +1
Unsupervised semantic segmentation (USS) aims to discover and recognize meaningful categories without any labels. For a successful USS, two key abilities are required: 1) informati…
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data
Seunghan Yang, Byeonggeun Kim, Kyuhong Shim +1
Few-shot keyword spotting (FS-KWS) models usually require large-scale annotated datasets to generalize to unseen target keywords. However, existing KWS datasets are limited in scal…
Knowledge Distillation from Non-streaming to Streaming ASR Encoder using Auxiliary Non-streaming Layer
Kyuhong Shim, Jinkyu Lee, Simyung Chang +1
Streaming automatic speech recognition (ASR) models are restricted from accessing future context, which results in worse performance compared to the non-streaming models. To improv…
Depth-Relative Self Attention for Monocular Depth Estimation
Kyuhong Shim, Jiyoung Kim, Gusang Lee +1
Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on vario…
Semantic-Preserving Augmentation for Robust Image-Text Retrieval
Sunwoo Kim, Kyuhong Shim, Luong Trung Nguyen +1
Image text retrieval is a task to search for the proper textual descriptions of the visual world and vice versa. One challenge of this task is the vulnerability to input image and…
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers
Minsoo Kim, Kyuhong Shim, Seongmin Park +2
Pre-trained Transformer models such as BERT have shown great success in a wide range of applications, but at the cost of substantial increases in model complexity. Quantization-awa…