4 accepted papers
Vision-Language Models (VLMs) excel in diverse visual tasks but face challenges in document understanding, which requires fine-grained text processing. While typical visual tasks perform well with low-resolution inputs, reading-intensive applications demand high-resolution, resulting in significant…
The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA) leading methods focus on the single-page setting while documents can span hundreds of pages. We present GRAM a method that seamlessl…
Vision-Language (VL) models have gained significant research focus enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder a Large Language Model (LLM) and a projection module that aligns visual features with the LLM's representation space. Despi…
Recent action recognition models have achieved impressive results by integrating objects, their locations and interactions. However, obtaining dense structured annotations for each frame is tedious and time-consuming, making these methods expensive to train and less scalable. At the same time, if a…