Preparing training data for deep vision models is a labor-intensive task. To address this,
generative models have emerged as an effective solution for generating synthetic data …

Despite the success of diffusion models (DMs), we still lack a thorough understanding of
their latent space. To understand the latent space $\mathbf {x} _t\in\mathcal {X} $, we …

This paper studies the problem of weakly open-vocabulary semantic segmentation
(WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs …

The variety of objects in the real world is nearly unlimited and is thus impossible to capture
using models trained on a fixed set of categories. As a result, in recent years, open …

Complex 3D scene understanding has gained increasing attention, with scene encoding
strategies built on top of visual foundation models playing a crucial role in this success …

Abstract We present TokenCompose a Latent Diffusion Model for text-to-image generation
that achieves enhanced consistency between user-specified text prompts and model …

Open-vocabulary semantic segmentation is a challenging task that requires segmenting
novel object categories at inference time. Recent works explore vision-language pre-training …

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action
instances with only category labels. Most methods widely adopt the off-the-shelf …

Abstract Vision-Language Large Models (VLMs) recently become primary backbone of AI,
due to the impressive performance. However, their expensive computation costs, ie …

This paper introduces a novel unified representation of diffusion models for image
generation and segmentation. Specifically we use a colormap to represent entity-level …