5 citations · 6 across the 6 of their papers we have counts for
10 papers · 1 filter
V-Reflection: Transforming MLLMs from Passive Observers to Active Interrogators
Jiazhou Zhou, Yucheng Chen, Hongyang Li +4
Multimodal Large Language Models (MLLMs) have achieved remarkable success, yet they remain prone to perception-related hallucinations in fine-grained tasks. This vulnerability aris…
SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs
Guanting Ye, Qiyan Zhao, Wenhao Yu +7
3D Large Vision-Language Models (3D LVLMs) built upon Large Language Models (LLMs) have achieved remarkable progress across various multimodal tasks. However, their inherited posit…
T-Rex-Omni: Integrating Negative Visual Prompt in Generic Object Detection
Jiazhou Zhou, Qing Jiang, Kanghao Chen +4
Object detection methods have evolved from closed-set to open-set paradigms over the years. Current open-set object detectors, however, remain constrained by their exclusive relian…
Detect Anything via Next Point Prediction
Qing Jiang, Junan Huo, Xingyu Chen +6
Object detection has long been dominated by traditional coordinate regression-based models, such as YOLO, DETR, and Grounding DINO. Although recent efforts have attempted to levera…
Rex-Thinker: Grounded Object Referring via Chain-of-Thought Reasoning
Qing Jiang, Xingyu Chen, Zhaoyang Zeng +2
Object referring aims to detect all objects in an image that match a given natural language description. We argue that a robust object referring model should be grounded, meaning i…
Referring to Any Person
Qing Jiang, Lin Wu, Zhaoyang Zeng +5
Humans are undoubtedly the most important participants in computer vision, and the ability to detect any individual given a natural language description, a task we define as referr…