127 citations · 168 across the 38 of their papers we have counts for
20 papers · 1 filter
Plans You Can Check: Verifier-Grounded Learning of an Open-Weight Planner for Executable Video-Editing
Haoyu Wang, Cheng Feng, Liuyang Bian +5
Practical video editing is not only pixel generation: an editor must turn a brief, a clip pool, music metadata, and hard constraints into an executable timeline. We study this deci…
SpatialAfford: Teaching Compact VLMs Where to Look and Where to Ground for Affordance
Yufei Zhang, Chenlu Zhan, Donghui Sun +2
Affordance grounding aims to localize the functional region for interaction, such as the handle to grasp or the button to press, rather than the whole object. This makes it more ch…
Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance
Kangsheng Duan, Ziyang Xu, Wenyu Liu +3
While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical deployment. Constructin…
UI-Mem: Self-Evolving Experience Memory for Online Reinforcement Learning in Mobile GUI Agents
Han Xiao, Guozhi Wang, Hao Wang +7
Online Reinforcement Learning (RL) offers a promising paradigm for enhancing GUI agents through direct environment interaction. However, its effectiveness is severely hindered by i…
VideoChat-M1: Collaborative Policy Planning for Video Understanding via Multi-Agent Reinforcement Learning
Boyu Chen, Zikang Wang, Zhengrong Yue +9
By leveraging tool-augmented Multimodal Large Language Models (MLLMs), multi-agent frameworks are driving progress in video understanding. However, most of them adopt static and no…
LENS: Learning to Segment Anything with Unified Reinforced Reasoning
Lianghui Zhu, Bin Ouyang, Yuxuan Zhang +8
Text-prompted image segmentation enables fine-grained visual understanding and is critical for applications such as human-computer interaction and robotics. However, existing super…
Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding
Han Xiao, Yina Xie, Guanxin Tan +12
Visual Document Understanding has become essential with the increase of text-rich visual content. This field poses significant challenges due to the need for effective integration…
PixelHacker: Image Inpainting with Structural and Semantic Consistency
Ziyang Xu, Kangsheng Duan, Xiaolei Shen +5
Image inpainting is a fundamental research area between image editing and image generation. Recent state-of-the-art (SOTA) methods have explored novel attention mechanisms, lightwe…
GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
Rui Hu, Lianghui Zhu, Yuxuan Zhang +7
Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its immense potential for bridging the gap between v…
BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices
Xudong Lu, Yinghao Chen, Cheng Chen +19
The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication t…
ControlAR: Controllable Image Generation with Autoregressive Models
Zongming Li, Tianheng Cheng, Shoufa Chen +6
Autoregressive (AR) models have reformulated image generation as next-token prediction, demonstrating remarkable potential and emerging as strong competitors to diffusion models. H…
Efficient Test-Time Prompt Tuning for Vision-Language Models
Yuhan Zhu, Guozhen Zhang, Chen Xu +4
Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts. Previous studies have shown the effectiveness of te…
FAGhead: Fully Animate Gaussian Head from Monocular Videos
Yixin Xuan, Xinyang Li, Gongxin Yao +4
High-fidelity reconstruction of 3D human avatars has a wild application in visual reality. In this paper, we introduce FAGhead, a method that enables fully controllable human portr…
EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
Yuxuan Zhang, Tianheng Cheng, Lianghui Zhu +7
Segment Anything Model (SAM) has attracted widespread attention for its superior interactive segmentation capabilities with visual prompts while lacking further exploration of text…
Meta-Auxiliary Learning for Micro-Expression Recognition
Jingyao Wang, Yunhan Tian, Yuxuan Yang +3
Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection. However, desp…
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning
Jiaxi Lv, Yi Huang, Mingfu Yan +6
Recent advances in text-to-video generation have harnessed the power of diffusion models to create visually compelling content conditioned on text prompts. However, they usually en…
DPL: Decoupled Prompt Learning for Vision-Language Models
Chen Xu, Yuhan Zhu, Guozhen Zhang +5
Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e.g., CLIP) to downstream tasks. However, current methods t…
Progressive Visual Prompt Learning with Contrastive Feature Re-formation
Chen Xu, Yuhan Zhu, Haocheng Shen +4
Prompt learning has been designed as an alternative to fine-tuning for adapting Vision-language (V-L) models to the downstream tasks. Previous works mainly focus on text prompt whi…
Real-Time Image Demoireing on Mobile Devices
Yuxin Zhang, Mingbao Lin, Xunchao Li +7
Moire patterns appear frequently when taking photos of digital screens, drastically degrading the image quality. Despite the advance of CNNs in image demoireing, existing networks…
Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images
Xinggang Wang, Jiapei Feng, Bin Hu +4
Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnost…