49 citations · 102 across the 60 of their papers we have counts for
12 papers · 1 filter
Your Vision-Language Model Can't Even Count to 20: Exposing the Failures of VLMs in Compositional Counting
Xuyang Guo, Zekai Huang, Zhenmei Shi +2
Vision-Language Models (VLMs) have become a central focus of today's AI community, owing to their impressive abilities gained from training on large-scale vision-language data from…
T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation
Yubin Chen, Xuyang Guo, Zhenmei Shi +2
Text-to-video (T2V) models have shown remarkable performance in generating visually reasonable scenes, while their capability to leverage world knowledge for ensuring semantic cons…
T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models
Xuyang Guo, Jiayan Huo, Zhenmei Shi +3
Thanks to recent advancements in scalable deep architectures and large-scale pretraining, text-to-video generation has achieved unprecedented capabilities in producing high-fidelit…
Can You Count to Nine? A Human Evaluation Benchmark for Counting Limits in Modern Text-to-Video Models
Xuyang Guo, Zekai Huang, Jiayan Huo +4
Generative models have driven significant progress in a variety of AI tasks, including text-to-video generation, where models like Video LDM and Stable Video Diffusion can produce…
HOFAR: High-Order Augmentation of Flow Autoregressive Transformers
Yingyu Liang, Zhizhou Sha, Zhenmei Shi +2
Flow Matching and Transformer architectures have demonstrated remarkable performance in image generation tasks, with recent work FlowAR [Ren et al., 2024] synergistically integrati…
Text-to-Image Diffusion Models Cannot Count, and Prompt Refinement Cannot Help
Xuyang Guo, Jiayan Huo, Yingyu Liang +4
Generative modeling is widely regarded as one of the most essential problems in today's AI community, with text-to-image generation having gained unprecedented real-world impacts.…
High-Order Matching for One-Step Shortcut Diffusion Models
Bo Chen, Chengyue Gong, Xiaoyu Li +5
One-step shortcut diffusion models [Frans, Hafner, Levine and Abbeel, ICLR 2025] have shown potential in vision generation, but their reliance on first-order trajectory supervision…
RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation
Yuefan Cao, Chengyue Gong, Xiaoyu Li +4
Text-to-video generation models have made impressive progress, but they still struggle with generating videos with complex features. This limitation often arises from the inability…
Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models
Jiayu Wang, Yifei Ming, Zhenmei Shi +4
Large language models (LLMs) and vision-language models (VLMs) have demonstrated remarkable performance across a wide range of tasks and domains. Despite this promise, spatial unde…
Deep Online Fused Video Stabilization
Zhenmei Shi, Fuhao Shi, Wei-Sheng Lai +2
We present a deep neural network (DNN) that uses both sensor data (gyroscope) and image content (optical flow) to stabilize videos through unsupervised learning. The network fuses…
DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking
Zhenmei Shi, Haoyang Fang, Yu-Wing Tai +1
Psychological studies have found that human visual tracking system involves learning, memory, and planning. Despite recent successes, not many works have focused on memory and plan…
SF-Net: Structured Feature Network for Continuous Sign Language Recognition
Zhaoyang Yang, Zhenmei Shi, Xiaoyong Shen +1
Continuous sign language recognition (SLR) aims to translate a signing sequence into a sentence. It is very challenging as sign language is rich in vocabulary, while many among the…