5 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
START: Spatial and Textual Learning for Chart Understanding
Zhuoming Liu, Xiaofeng Gao, Feiyang Niu +3
Chart understanding is crucial for deploying multimodal large language models (MLLMs) in real-world scenarios such as analyzing scientific papers and technical reports. Unlike natu…
TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models
Junyi Zhang, Jia-Chen Gu, Wenbo Hu +3
Existing medical reasoning benchmarks for vision-language models primarily focus on analyzing a patient's condition based on an image from a single visit. However, this setting dev…
T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design
Jiachen Li, Qian Long, Jian Zheng +4
In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T…
FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation
Xuehai He, Jian Zheng, Jacob Zhiyuan Fang +6
Controllable text-to-image (T2I) diffusion models generate images conditioned on both text prompts and semantic inputs of other modalities like edge maps. Nevertheless, current con…
E-ViLM: Efficient Video-Language Model via Masked Video Modeling with Semantic Vector-Quantized Tokenizer
Jacob Zhiyuan Fang, Skyler Zheng, Vasu Sharma +1
To build scalable models for challenging real-world tasks, it is important to learn from diverse, multi-modal data in various forms (e.g., videos, text, and images). Among the exis…
Characterizing Video Question Answering with Sparsified Inputs
Shiyuan Huang, Robinson Piramuthu, Vicente Ordonez +2
In Video Question Answering, videos are often processed as a full-length sequence of frames to ensure minimal loss of information. Recent works have demonstrated evidence that spar…