11 accepted papers
Multi-modal agents are making rapid progress on general computer-use tasks. However, existing benchmarks remain largely confined to web browsers and rudimentary applications, failing to capture the professional software workflows that dominate real-world scientific and industrial practices. To bridg
The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have revitalized autonomous driving, particularly in reasoning tasks within perceivable regions. However, when faced with perception-limited areas (dynamic or static occlusion regions), MLLMs struggle to effectively integra…
"Compositional generalization has received much attention in vision-and-language and visual reasoning recently. Substitutivity, the capability to generalize to novel compositions with synonymous primitives such as words and visual entities, is an essential factor in evaluating the compositional gene…
Recent work has revealed that in-context learning for large language models exhibits compositional generalization capacity, which can be enhanced by selecting in-context demonstrations similar to test cases to provide contextual information. However, how to exhibit in-context compositional generaliz…
Recurrently refining the optical flow based on a single high-resolution feature demonstrates high performance. We exploit the strength of this strategy to build a novel architecture for the joint learning of optical flow and depth. Our pro-posed architecture is improved to work in the case of traini…
Cross-modal optical flow estimation aims to predict motion fields between two frames collected from different modalities, recently attracting intensive attention. However, a substantial yet challenging problem is how to match images across a large modal discrepancy. In this paper, we propose a modal…
Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the sequence length. General efficient transformers are challenging to be directly adapted to model document. They are unabl…
Scene flow estimation is critical for real-world vision problems such as autonomous driving and augmented reality. Due to the popularity of 3D LiDAR sensors, scene flow estimation from 3D point clouds arouses increasing attention. Existing methods usually use a flow embedding-based layer to find cor…
Spiking cameras have shown promising advantages for optical flow estimation in high-speed scenarios. The recent work SCFlow [1] attempts to train an optical flow model using spike frames based on a multi-scale flow reconstruction loss. However, only using the flow reconstruction loss is unable to ef…
Many deep learning applications benefit from multi-task learning with several related objectives. In autonomous driving scenarios, being able to accurately infer motion and spatial information is essential for scene understanding. In this paper, we combine an adaptive feature refinement module and a…
Variational models for optical flow estimation usually define an energy function that contains prior assumptions to explore rudimentary statistics of images. However, such methods cannot learn motion knowledge from the pre-prepared data and have many parameters that need to be set manually. Nowadays…