Human activity recognition (HAR) has multifaceted applications due to its worldly usage of
acquisition devices such as smartphones, video cameras, and its ability to capture human …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed
by consolidating our insights into data, models, and visual representations in the LLaVA …

We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

Visual data such as images and videos are typically modeled as discretizations of inherently
continuous, multidimensional signals. Existing continuous-signal models attempt to exploit …

Abstract We present Multiscale Vision Transformers (MViT) for video and image recognition,
by connecting the seminal idea of multiscale feature hierarchies with transformer models …

Our objective in this work is video-text retrieval-in particular a joint embedding that enables
efficient text-to-video retrieval. The challenges in this area include the design of the visual …

The canonical approach to video action recognition dictates a neural model to do a classic
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …

Multimodal large language models (MLLMs) building upon the foundation of powerful large
language models (LLMs) have recently demonstrated exceptional capabilities in generating …