7 citations · 12 across the 15 of their papers we have counts for
16 papers
VidOmni-Bench: A Benchmark for Fine-Grained Video Understanding via Spatio-Temporal Event Verification across Complexity and Duration
Changbeen Kim, Junwon Chang, Kipyo Kim +3
While Video Large Language Models (Video-LLMs) have recently demonstrated strong performance, reliably evaluating their fine-grained video understanding remains challenging. Existi…
Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language
Seungbeen Lee, Jinhong Jeong, Donghyun Kim +2
Our ability to interpret others' mental states through nonverbal cues (NVCs) is fundamental to our survival and social cohesion. While existing Theory of Mind (ToM) benchmarks have…
Visual Representation Alignment for Multimodal Large Language Models
Heeji Yoon, Jaewoo Jung, Junwan Kim +10
Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tas…
Talk in Pieces, See in Whole: Disentangled and Hierarchical Representation Learning in Language-based Object Detection
Sojung An, Kwanyong Park, Yong Jae Lee +1
Vision-language models (VLMs) have advanced multimodal perception, demonstrated by open-vocabulary object detection with simple language queries. State-of-the-art VLMs still strugg…
ZIUM: Zero-Shot Intent-Aware Adversarial Attack on Unlearned Models
Hyun Jun Yook, Ga San Jhun, Jae Hyun Cho +4
Machine unlearning (MU) removes specific data points or concepts from deep learning models to enhance privacy and prevent sensitive content generation. Adversarial prompts can expl…
CaptionSmiths: Flexibly Controlling Language Pattern in Image Captioning
Kuniaki Saito, Donghyun Kim, Kwanyong Park +2
An image captioning model flexibly switching its language pattern, e.g., descriptiveness and length, should be useful since it can be applied to diverse applications. However, desp…
Knowledge Rectification for Camouflaged Object Detection: Unlocking Insights from Low-Quality Data
Juwei Guan, Xiaolin Fang, Donghyun Kim +4
Low-quality data often suffer from insufficient image details, introducing an extra implicit aspect of camouflage that complicates camouflaged object detection (COD). Existing COD…
A Collaborative Ensemble Framework for CTR Prediction
Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu +13
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into…
UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Models
Sejoon Oh, Yiqiao Jin, Megha Sharma +4
Multimodal large language models (MLLMs) have revolutionized vision-language understanding but remain vulnerable to multimodal jailbreak attacks, where adversarial inputs are metic…
Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection
Gyusam Chang, Jiwon Lee, Donghyun Kim +5
Recent advances in 3D object detection leveraging multi-view cameras have demonstrated their practical and economical value in various challenging vision tasks. However, typical su…
Weak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection
Kwanyong Park, Kuniaki Saito, Donghyun Kim
Vision-language (VL) models often exhibit a limited understanding of complex expressions of visual objects (e.g., attributes, shapes, and their relations), given complex and divers…
Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA
Jongwoo Park, Kanchana Ranasinghe, Kumara Kahatapitiya +3
Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely related. Therefor…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs
Donghyun Kim, Byeongho Heo, Dongyoon Han
This paper revives Densely Connected Convolutional Networks (DenseNets) and reveals the underrated effectiveness over predominant ResNet-style architectures. We believe DenseNets'…
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
Jungho Lee, Dogyoon Lee, Minhyeok Lee +2
Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity. However, the presence of motion blu…
What, How, and When Should Object Detectors Update in Continually Changing Test Domains?
Jayeon Yoo, Dongkwan Lee, Inseop Chung +2
It is a well-known fact that the performance of deep learning models deteriorates when they encounter a distribution shift at test time. Test-time adaptation (TTA) algorithms have…