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.

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Publications(29)

• Listening Like a Judge: A Music-Aware Framework for Automatic Singing Performance Evaluation

  AuthorNeelam Saini, Sourav Ghosh

  PublishedInterspeech 2026 (Interspeech)

  Date2026-09-27

• Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration

  AuthorIsma Hadji, Enrique Sanchez, Adrian Bulat, Georgios Tzimiropoulos, Brais Martinez

  PublishedThe IEEE/CVF Conference on Computer Vision and Pattern (CVPR) Recognition 2026

  Date2026-06-05

• LittleBit: Ultra-Low Bit Quantization via Latent Factorization

  AuthorBanseok Lee, Dongkyu Kim, Youngcheon You, Youngmin Kim

  PublishedNeural Information Processing Systems (NeurIPS)

  Date2025-12-02

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News(5)

• Samsung R&D Institute Ukraine's Visual Intelligence Team at CVPR 2026: Advancing Visual Intelligence and Document AI

  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.

• [Blog] 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.

View More

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