259 citations · 391 across the 25 of their papers we have counts for
28 papers
FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry
Muxin Liu, Xiaoyang Lyu, Tianhe Ren +7
We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fid…
SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing
Yicheng Xiao, Wenhu Zhang, Lin Song +10
Image spatial editing performs geometry-driven transformations, allowing precise control over object layout and camera viewpoints. Current models are insufficient for fine-grained…
Chain-of-Ground: Improving GUI Grounding via Iterative Reasoning and Reference Feedback
Aiden Yiliu Li, Bizhi Yu, Daoan Lei +2
GUI grounding aims to align natural language instructions with precise regions in complex user interfaces. Advanced multimodal large language models show strong ability in visual G…
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…
SegDINO3D: 3D Instance Segmentation Empowered by Both Image-Level and Object-Level 2D Features
Jinyuan Qu, Hongyang Li, Xingyu Chen +5
In this paper, we present SegDINO3D, a novel Transformer encoder-decoder framework for 3D instance segmentation. As 3D training data is generally not as sufficient as 2D training i…
DBellQuant: Breaking the Bell with Double-Bell Transformation for LLMs Post Training Binarization
Zijian Ye, Wei Huang, Yifei Yu +3
Large language models (LLMs) demonstrate remarkable performance but face substantial computational and memory challenges that limit their practical deployment. Quantization has eme…
Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos
Weifeng Lin, Xinyu Wei, Ruichuan An +7
We present Perceive Anything Model (PAM), a conceptually straightforward and efficient framework for comprehensive region-level visual understanding in images and videos. Our appro…
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…
TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video
Jinyuan Qu, Hongyang Li, Shilong Liu +3
In this paper, built upon TAPTRv2, we present TAPTRv3. TAPTRv2 is a simple yet effective DETR-like point tracking framework that works fine in regular videos but tends to fail in l…
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…
TAPTRv2: Attention-based Position Update Improves Tracking Any Point
Hongyang Li, Hao Zhang, Shilong Liu +5
In this paper, we present TAPTRv2, a Transformer-based approach built upon TAPTR for solving the Tracking Any Point (TAP) task. TAPTR borrows designs from DEtection TRansformer (DE…
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…
TAPTR: Tracking Any Point with Transformers as Detection
Hongyang Li, Hao Zhang, Shilong Liu +4
In this paper, we propose a simple and strong framework for Tracking Any Point with TRansformers (TAPTR). Based on the observation that point tracking bears a great resemblance to…
LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models
Hao Zhang, Hongyang Li, Feng Li +8
With the recent significant advancements in large multi-modal models (LMMs), the importance of their grounding capability in visual chat is increasingly recognized. Despite recent…
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…
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Shilong Liu, Hao Cheng, Haotian Liu +10
LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models. It maintains a skill repository of pre-trained vision and vision-lang…
Systematic Investigation of Sparse Perturbed Sharpness-Aware Minimization Optimizer
Peng Mi, Li Shen, Tianhe Ren +6
Deep neural networks often suffer from poor generalization due to complex and non-convex loss landscapes. Sharpness-Aware Minimization (SAM) is a popular solution that smooths the…
detrex: Benchmarking Detection Transformers
Tianhe Ren, Shilong Liu, Feng Li +13
The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and…
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models
Gen Luo, Yiyi Zhou, Tianhe Ren +3
Recently, growing interest has been aroused in extending the multimodal capability of large language models (LLMs), e.g., vision-language (VL) learning, which is regarded as the ne…
A Strong and Reproducible Object Detector with Only Public Datasets
Tianhe Ren, Jianwei Yang, Shilong Liu +6
This work presents Focal-Stable-DINO, a strong and reproducible object detection model which achieves 64.6 AP on COCO val2017 and 64.8 AP on COCO test-dev using only 700M parameter…
Detection Transformer with Stable Matching
Shilong Liu, Tianhe Ren, Jiayu Chen +8
This paper is concerned with the matching stability problem across different decoder layers in DEtection TRansformers (DETR). We point out that the unstable matching in DETR is cau…