12 citations · 23 across the 14 of their papers we have counts for
16 papers · 1 filter
ReasonUni: Reasoning-Augmented Reconstruction Advances Unified Multimodal Models
Feize Wu, Zhiyuan Yan, Yuhuan Hong +10
Image-to-text (I2T) understanding and text-to-image (T2I) generation are dual tasks in unified multimodal models (UMMs). Reconstructive post-training couples these tasks, but exist…
UniSpace: Unified Visual Representation and Scalable Multimodal Modeling
Jinbo Yan, Limeng Qiao, Jie Qin +3
Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation. However, their final tokens discard fine…
HoloCount: A Holistic Visual Counting Benchmark for MLLMs
Jinhong Deng, Limeng Qiao, Guanglu Wan
Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning. While Multimodal Large Languag…
X2SAM: Any Segmentation in Images and Videos
Hao Wang, Limeng Qiao, Chi Zhang +4
Multimodal Large Language Models (MLLMs) have demonstrated strong image-level visual understanding and reasoning, yet their pixel-level perception across both images and videos rem…
GeoAlign: Geometric Feature Realignment for MLLM Spatial Reasoning
Zhaochen Liu, Limeng Qiao, Guanglu Wan +1
Multimodal large language models (MLLMs) have exhibited remarkable performance in various visual tasks, yet still struggle with spatial reasoning. Recent efforts mitigate this by i…
STAR: STacked AutoRegressive Scheme for Unified Multimodal Learning
Jie Qin, Jiancheng Huang, Limeng Qiao +1
Multimodal large language models (MLLMs) play a pivotal role in advancing the quest for general artificial intelligence. However, achieving unified target for multimodal understand…
UniComp: Rethinking Video Compression Through Informational Uniqueness
Chao Yuan, Shimin Chen, Minliang Lin +3
Distinct from attention-based compression methods, this paper presents an information uniqueness driven video compression framework, termed UniComp, which aims to maximize the info…
RIV: Recursive Introspection Mask Diffusion Vision Language Model
YuQian Li, Limeng Qiao, Lin Ma
Mask Diffusion-based Vision Language Models (MDVLMs) have achieved remarkable progress in multimodal understanding tasks. However, these models are unable to correct errors in gene…
Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization
Yifan Chang, Jie Qin, Limeng Qiao +4
Vector quantization (VQ) is a key component in discrete tokenizers for image generation, but its training is often unstable due to straight-through estimation bias, one-step-behind…
Beyond the Visible: Benchmarking Occlusion Perception in Multimodal Large Language Models
Zhaochen Liu, Kaiwen Gao, Shuyi Liang +4
Occlusion perception, a critical foundation for human-level spatial understanding, embodies the challenge of integrating visual recognition and reasoning. Though multimodal large l…
X-SAM: From Segment Anything to Any Segmentation
Hao Wang, Limeng Qiao, Zequn Jie +6
Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding. Although…
VITRIX-CLIPIN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions
Ziteng Wang, Siqi Yang, Limeng Qiao +1
Despite the success of Vision-Language Models (VLMs) like CLIP in aligning vision and language, their proficiency in detailed, fine-grained visual comprehension remains a key chall…
UniViTAR: Unified Vision Transformer with Native Resolution
Limeng Qiao, Yiyang Gan, Bairui Wang +4
Conventional Vision Transformer simplifies visual modeling by standardizing input resolutions, often disregarding the variability of natural visual data and compromising spatial-co…
PLUG: Revisiting Amodal Segmentation with Foundation Model and Hierarchical Focus
Zhaochen Liu, Limeng Qiao, Xiangxiang Chu +1
Aiming to predict the complete shapes of partially occluded objects, amodal segmentation is an important step towards visual intelligence. With crucial significance, practical prio…
MobileVLM V2: Faster and Stronger Baseline for Vision Language Model
Xiangxiang Chu, Limeng Qiao, Xinyu Zhang +8
We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an im…
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
Xiangxiang Chu, Limeng Qiao, Xinyang Lin +8
We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniq…