9 papers
One Token per Highly Selective Frame: Towards Extreme Compression for Long Video Understanding
Zheyu Zhang, Ziqi Pang, Shixing Chen +3
Long video understanding is inherently challenging for vision-language models (VLMs) because of the extensive number of frames. With each video frame typically expanding into tens…
AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark
Aruna Gauba, Irene Pi, Yunze Man +3
We present AgMMU, a challenging real-world benchmark for evaluating and advancing vision-language models (VLMs) in the knowledge-intensive domain of agriculture. Unlike prior datas…
RandAR: Decoder-only Autoregressive Visual Generation in Random Orders
Ziqi Pang, Tianyuan Zhang, Fujun Luan +5
We introduce RandAR, a decoder-only visual autoregressive (AR) model capable of generating images in arbitrary token orders. Unlike previous decoder-only AR models that rely on a p…
MR. Video: "MapReduce" is the Principle for Long Video Understanding
Ziqi Pang, Yu-Xiong Wang
We propose MR. Video, an agentic long video understanding framework that demonstrates the simple yet effective MapReduce principle for processing long videos: (1) Map: independentl…
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception
Ziqi Pang, Xin Xu, Yu-Xiong Wang
With the success of image generation, generative diffusion models are increasingly adopted for discriminative tasks, as pixel generation provides a unified perception interface. Ho…
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
Lang Lin, Xueyang Yu, Ziqi Pang +1
This paper proposes a novel framework utilizing multi-modal large language models (MLLMs) for referring video object segmentation (RefVOS). Previous MLLM-based methods commonly str…