7 citations · 21 across the 14 of their papers we have counts for
17 papers
DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real World
Xiangtai Li, Tao Zhang, Yanwei Li +13
Multimodal Large Language Models (MLLMs) demonstrate a complex understanding of scenes, benefiting from large-scale and high-quality datasets. Most existing caption datasets lack t…
MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Wei Chow, Yuan Gao, Linfeng Li +15
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular r…
Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language Models
Shilin Xu, Yanwei Li, Rui Yang +9
Recent works on large language models (LLMs) have successfully demonstrated the emergence of reasoning capabilities via reinforcement learning (RL). Although recent efforts leverag…
On Path to Multimodal Generalist: General-Level and General-Bench
Hao Fei, Yuan Zhou, Juncheng Li +29
The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…
An Empirical Study of GPT-4o Image Generation Capabilities
Sixiang Chen, Jinbin Bai, Zhuoran Zhao +16
The landscape of image generation has rapidly evolved, from early GAN-based approaches to diffusion models and, most recently, to unified generative architectures that seek to brid…
4th PVUW MeViS 3rd Place Report: Sa2VA
Haobo Yuan, Tao Zhang, Xiangtai Li +5
Referring video object segmentation (RVOS) is a challenging task that requires the model to segment the object in a video given the language description. MeViS is a recently propos…
Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs
Yikang Zhou, Tao Zhang, Shilin Xu +7
Recent advancements in multimodal large language models (MLLM) have shown a strong ability in visual perception, reasoning abilities, and vision-language understanding. However, th…
Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos
Haobo Yuan, Xiangtai Li, Tao Zhang +8
This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-modal large language models, whic…
Direct Preference Optimization for LLM-Enhanced Recommendation Systems
Chao Sun, Yaobo Liang, Yaming Yang +3
Large Language Models (LLMs) have exhibited remarkable performance across a wide range of domains, motivating research into their potential for recommendation systems. Early effort…
LLAVADI: What Matters For Multimodal Large Language Models Distillation
Shilin Xu, Xiangtai Li, Haobo Yuan +3
The recent surge in Multimodal Large Language Models (MLLMs) has showcased their remarkable potential for achieving generalized intelligence by integrating visual understanding int…
An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
Xiangyu Zhao, Yicheng Chen, Shilin Xu +4
Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Exp…
RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything
Shilin Xu, Haobo Yuan, Qingyu Shi +9
Recent segmentation methods, which adopt large-scale data training and transformer architecture, aim to create one foundation model that can perform multiple tasks. However, most o…
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection
Shilin Xu, Xiangtai Li, Size Wu +3
Open-vocabulary object detection (OVOD) aims to detect the objects beyond the set of classes observed during training. This work introduces a straightforward and efficient strategy…
Towards Open Vocabulary Learning: A Survey
Jianzong Wu, Xiangtai Li, Shilin Xu +9
In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most a…
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation
Xiangtai Li, Shilin Xu, Yibo Yang +6
Panoptic Part Segmentation (PPS) unifies panoptic and part segmentation into one task. Previous works utilize separate approaches to handle things, stuff, and part predictions with…
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation
Xiangtai Li, Shilin Xu, Yibo Yang +3
Panoptic Part Segmentation (PPS) aims to unify panoptic segmentation and part segmentation into one task. Previous work mainly utilizes separated approaches to handle thing, stuff,…
Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition
Shilin Xu, Xiangtai Li, Jingbo Wang +3
Human fashion understanding is one crucial computer vision task since it has comprehensive information for real-world applications. This focus on joint human fashion segmentation a…