93 citations · 120 across the 15 of their papers we have counts for
16 papers
ScaleHP: Scale-Mediated Optimization of Coupled Errors for Metric-Space Hand Pose Estimation
Ruitao Jing, Xingyu Chen, Hongyang Li +3
In this paper, we present ScaleHP, a unified framework that explicitly represents per-instance metric scale to resolve the coupled errors in calibrated camera-space hand pose estim…
SceneParser: Hierarchical Scene Parsing for Visual Semantics Understanding
Pengxin Xu, Xincheng Lin, Luping Xiao +5
General scene perception has progressed from object recognition toward open-vocabulary grounding, part localization, and affordance prediction. Yet these capabilities are often rea…
Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models
Yiran Ling, Qing Lian, Jinghang Li +6
In this paper, we propose GTA-VLA(Guide, Think, Act), an interactive Vision-Language-Action (VLA) framework that enables spatially steerable embodied reasoning by allowing users to…
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…
ChatRex: Taming Multimodal LLM for Joint Perception and Understanding
Qing Jiang, Gen Luo, Yuqin Yang +5
Perception and understanding are two pillars of computer vision. While multimodal large language models (MLLM) have demonstrated remarkable visual understanding capabilities, they…
DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
Tianhe Ren, Yihao Chen, Qing Jiang +17
In this paper, we introduce DINO-X, which is a unified object-centric vision model developed by IDEA Research with the best open-world object detection performance to date. DINO-X…
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
Tianhe Ren, Qing Jiang, Shilong Liu +13
This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the "Edge" of open-set object detec…
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
Qing Jiang, Feng Li, Zhaoyang Zeng +3
We present T-Rex2, a highly practical model for open-set object detection. Previous open-set object detection methods relying on text prompts effectively encapsulate the abstract c…
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Tianhe Ren, Shilong Liu, Ailing Zeng +14
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and seg…
Visual In-Context Prompting
Feng Li, Qing Jiang, Hao Zhang +9
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain. Existin…
T-Rex: Counting by Visual Prompting
Qing Jiang, Feng Li, Tianhe Ren +4
We introduce T-Rex, an interactive object counting model designed to first detect and then count any objects. We formulate object counting as an open-set object detection task with…