We propose a Vision-Language Simulation Model (VLSM) that unifies visual and textual understanding to synthesize executable FlexScript from layout sketches and ...
Deep representations across modalities are inherently intertwined. In this paper, we systematically analyze the spectral characteristics of various semantic and...
Generating realistic human-human interactions is a challenging task that requires not only high-quality individual body and hand motions, but also coherent coor...
We introduce Perception Encoder Audiovisual, PE-AV, a new family of encoders for audio and video understanding trained with scaled contrastive learning. Built o...
Recently, the introduction of Chain-of-Thought (CoT) has largely improved the generation ability of unified models. However, it is observed that the current thi...
We build the first system to address the problem of reconstructing in-scene object manipulation from a monocular RGB video. It is challenging due to ill-posed s...
While Multimodal Large Language Models (MLLMs) have achieved impressive performance on semantic tasks, their spatial intelligence--crucial for robust and ground...
Autoregressive (AR) visual generation relies on tokenizers to map images to and from discrete sequences. However, tokenizers are trained to reconstruct clean im...
Generating long-range, geometrically consistent video presents a fundamental dilemma: while consistency demands strict adherence to 3D geometry in pixel space, ...
Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan...
We leverage multimodal large language models (LLMs) to construct a dataset of 306,070 German patents (1877-1918) from 9,562 archival image scans using our LLM-b...
Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, demanding accurate automated diagnostic systems. While general-domain vision-la...
While Multimodal Large Language Models have achieved human-like performance on many visual and textual reasoning tasks, their proficiency in fine-grained spatia...
Recent breakthroughs in self-supervised Joint-Embedding Predictive Architectures (JEPAs) have established that regularizing Euclidean representations toward iso...
Overview YOLOv6 is a new step in object detection designed for factories, stores, and cameras everywhere. Built by a team focused on speed and reliability, it...
Vision-Language-Action (VLA) models align vision and language with embodied control, but their object referring ability remains limited when relying solely on t...
Artistic style transfer in generative models remains a significant challenge, as existing methods often introduce style only via model fine-tuning, additional a...
Multimodal Large Language Models (MLLMs) combine visual and textual representations to enable rich reasoning capabilities. However, the high computational cost ...
Vocabulary-free fine-grained image recognition aims to distinguish visually similar categories within a meta-class without a fixed, human-defined label set. Exi...
Deep neural networks often exploit shortcuts. These are spurious cues which are associated with output labels in the training data but are unrelated to task sem...
Myth: Computer Vision is only effective for images and not for videos. Reality: Computer Vision can handle both images and videos, thanks to advancements in tem...
Handwritten text recognition and optical character recognition solutions show excellent results with processing data of modern era, but efficiency drops with La...
Introduction Person re-identification re-ID is the task of finding the same individual across different camera views. It has important applications in security...
Overview Teaching computers to recognize patterns without labeled data—known as unsupervised learning—has become more accessible thanks to simple tweaks to the...
