2 citations · 5 across the 17 of their papers we have counts for
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DisDop: Distillation with Domain Priors for Open-Vocabulary Aerial Object Detection
Ruihao Xu, Yong Liu, Yansong Tang +6
With the widespread application of drones in recent years, object detection of aerial images has attracted increasing attention, especially open-vocabulary aerial detection which i…
FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios
Ruihao Xu, Yong Liu, Yansong Tang
Food defect detection is critical for automated quality control, yet existing studies lack unified benchmarks and suffer from data scarcity. We introduce FDD-48, a comprehensive da…
FoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis
Ruihao Xu, Xingming Shui, Jingxuan Niu +4
As AI-powered compliance monitoring becomes increasingly important in public governance and industrial safety, the ability to provide verifiable evidence and traceable accountabili…
Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking
Deyi Zhu, Yuji Wang, Yong Liu +4
Traditional visual object tracking (VOT) methods typically rely on task-specific supervised training, limiting their generalization to unseen objects and challenging scenarios with…
StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
Yiyang Fu, Chubin Zhang, Shukai Gong +7
It is infeasible to encompass all possible disturbances within the training dataset. This raises a critical question regarding the robustness of Vision-Language-Action (VLA) models…
Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings
Haonan Jiang, Yuji Wang, Yongjie Zhu +5
Leveraging Multimodal Large Language Models (MLLMs) has become pivotal for advancing Universal Multimodal Embeddings (UME) in addressing diverse cross-modal tasks. Recent studies d…
Flash-VStream: Efficient Real-Time Understanding for Long Video Streams
Haoji Zhang, Yiqin Wang, Yansong Tang +3
Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short vi…
Stepping Out of Similar Semantic Space for Open-Vocabulary Segmentation
Yong Liu, SongLi Wu, Sule Bai +3
Open-vocabulary segmentation aims to achieve segmentation of arbitrary categories given unlimited text inputs as guidance. To achieve this, recent works have focused on developing…
DreamLight: Towards Harmonious and Consistent Image Relighting
Yong Liu, Wenpeng Xiao, Qianqian Wang +5
We introduce a model named DreamLight for universal image relighting in this work, which can seamlessly composite subjects into a new background while maintaining aesthetic uniform…
SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes
Yuji Wang, Haoran Xu, Yong Liu +2
Reference Audio-Visual Segmentation (Ref-AVS) aims to provide a pixel-wise scene understanding in Language-aided Audio-Visual Scenes (LAVS). This task requires the model to continu…
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning
Sule Bai, Mingxing Li, Yong Liu +5
Traditional visual grounding methods primarily focus on single-image scenarios with simple textual references. However, extending these methods to real-world scenarios that involve…
IteRPrimE: Zero-shot Referring Image Segmentation with Iterative Grad-CAM Refinement and Primary Word Emphasis
Yuji Wang, Jingchen Ni, Yong Liu +2
Zero-shot Referring Image Segmentation (RIS) identifies the instance mask that best aligns with a specified referring expression without training and fine-tuning, significantly red…
Self-Calibrated CLIP for Training-Free Open-Vocabulary Segmentation
Sule Bai, Yong Liu, Yifei Han +4
Recent advancements in pre-trained vision-language models like CLIP have enabled the task of open-vocabulary segmentation. CLIP demonstrates impressive zero-shot capabilities in va…
Fully Aligned Network for Referring Image Segmentation
Yong Liu, Ruihao Xu, Yansong Tang
This paper focuses on the Referring Image Segmentation (RIS) task, which aims to segment objects from an image based on a given language description. The critical problem of RIS is…
Hierarchical Memory for Long Video QA
Yiqin Wang, Haoji Zhang, Yansong Tang +4
This paper describes our champion solution to the LOVEU Challenge @ CVPR'24, Track 1 (Long Video VQA). Processing long sequences of visual tokens is computationally expensive and m…
Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams
Haoji Zhang, Yiqin Wang, Yansong Tang +4
Benefiting from the advancements in large language models and cross-modal alignment, existing multi-modal video understanding methods have achieved prominent performance in offline…
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild
Kun Yuan, Hongbo Liu, Mading Li +6
Video quality assessment (VQA) is a challenging problem due to the numerous factors that can affect the perceptual quality of a video, \eg, content attractiveness, distortion type,…
1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation
Zhuoyan Luo, Yicheng Xiao, Yong Liu +4
The recent transformer-based models have dominated the Referring Video Object Segmentation (RVOS) task due to the superior performance. Most prior works adopt unified DETR framewor…
Open-Vocabulary Segmentation with Semantic-Assisted Calibration
Yong Liu, Sule Bai, Guanbin Li +2
This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with generalized contextual prior of CLIP. As the core of…
Universal Segmentation at Arbitrary Granularity with Language Instruction
Yong Liu, Cairong Zhang, Yitong Wang +3
This paper aims to achieve universal segmentation of arbitrary semantic level. Despite significant progress in recent years, specialist segmentation approaches are limited to speci…