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20242026
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cs.CV2026

Customized Visual Storytelling with Unified Multimodal LLMs

Wei-Hua Li, Cheng Sun, Chu-Song Chen

Multimodal story customization aims to generate coherent story flows conditioned on textual descriptions, reference identity images, and shot types. While recent progress in story…

cs.CV2025

MotiF: Making Text Count in Image Animation with Motion Focal Loss

Shijie Wang, Samaneh Azadi, Rohit Girdhar +3

Text-Image-to-Video (TI2V) generation aims to generate a video from an image following a text description, which is also referred to as text-guided image animation. Most existing m…

cs.CV2025

Pre-trained Vision-Language Models Learn Discoverable Visual Concepts

Yuan Zang, Tian Yun, Hao Tan +2

Do vision-language models (VLMs) pre-trained to caption an image of a "durian" learn visual concepts such as "brown" (color) and "spiky" (texture) at the same time? We aim to answe…

cs.CV2024

Do Pre-trained Vision-Language Models Encode Object States?

Kaleb Newman, Shijie Wang, Yuan Zang +2

For a vision-language model (VLM) to understand the physical world, such as cause and effect, a first step is to capture the temporal dynamics of the visual world, for example how…

cs.CV2024

Learning Visual Grounding from Generative Vision and Language Model

Shijie Wang, Dahun Kim, Ali Taalimi +2

Visual grounding tasks aim to localize image regions based on natural language references. In this work, we explore whether generative VLMs predominantly trained on image-text data…

cs.CV2024

Vamos: Versatile Action Models for Video Understanding

Shijie Wang, Qi Zhao, Minh Quan Do +3

What makes good representations for video understanding, such as anticipating future activities, or answering video-conditioned questions? While earlier approaches focus on end-to-…