Blog(3)

• Learning Representations from Explainable and Connectionist Approaches for Visual Question Answering

  VQA [1,2] is the field of research that aims to develop methods for answering natural language questions based on the information provided in corresponding images.

• SemEval-2025 Task 8: LLM Ensemble Methods for QA over Tabular Data

  Question answering using Large Language Models has gained significant popularity in both everyday communication and at the workplace. However|certain tasks|such as querying tables|still pose challenges for commercial and open-source chatbots powered by advanced deep learning models.

• Vision-Language Model Guided Semi-Supervised Learning for No-Reference Video Quality Assessment

  The widespread adoption of mobile devices has led to a rapid growth of video content that is captured, transmitted and shared on various social media platforms.

Research Areas(0)

Publications(27)

• LittleBit: Ultra-Low Bit Quantization via Latent Factorization

  AuthorBanseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim

  PublishedNeural Information Processing Systems (NeurIPS)

  Date2025-12-02

• Augmenting Perceptual Super-Resolution via Image Quality Predictors

  AuthorFengjia Zhang, Samrudhdhi B. Rangrej, Tristan Ty Aumentado-Armstrong, Afsaneh Fazly, Alex Levinshtein

  PublishedComputer Vision and Pattern Recognition (CVPR)

  Date2025-06-11

• Prodigy: Expeditiously Adaptive Parameter-Free Learner

  AuthorKonstantin Mishchenko

  PublishedInternational Conference on Machine Learning (ICML)

  Date2024-07-23

View More

News(6)

• Samsung R&D Institute Ukraine's Visual Intelligence Team at CVPR 2026

  The competition was a Visual Question Answering (VQA) challenge focused on autonomous driving safety, using dashcam videos from the 2COOOL dataset. Participants had to develop models that accurately answer structured, numeric questions about incident-related details (e.g., weather, road conditions, primary/secondary entities, prevention measures, and points of impact).

• Samsung R&D Institute Ukraine's Visual Intelligence Team at CVPR 2026

  Samsung demonstrated technical superiority in video VQA by securing 4th place in the 'VQA - AUTOPILOT CVPR' competition at the prestigious CVPR 2026

• [Blog] SmoGVLM: A Small, Graph-Enhanced Vision-Language Model

  Large vision-language models (VLMs) have achieved impressive performance across a wide range of multimodal tasks, from visual question answering (VQA) to reasoning over images and text [1, 2]. However, these models often suffer from hallucinations and poor grounding when faced with knowledge-intensive queries.

View More

Others(0)