Search Results for author: Shilong Liu

Found 36 papers, 26 papers with code

T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy

1 code implementation21 Mar 2024 Qing Jiang, Feng Li, Zhaoyang Zeng, Tianhe Ren, Shilong Liu, Lei Zhang

Recognizing the complementary strengths and weaknesses of both text and visual prompts, we introduce T-Rex2 that synergizes both prompts within a single model through contrastive learning.

Contrastive Learning Descriptive +3

TAPTR: Tracking Any Point with Transformers as Detection

no code implementations19 Mar 2024 Hongyang Li, Hao Zhang, Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Lei Zhang

Based on the observation that point tracking bears a great resemblance to object detection and tracking, we borrow designs from DETR-like algorithms to address the task of TAP.

object-detection Object Detection +2

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

1 code implementation25 Jan 2024 Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, Lei Zhang

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).


LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models

1 code implementation5 Dec 2023 Hao Zhang, Hongyang Li, Feng Li, Tianhe Ren, Xueyan Zou, Shilong Liu, Shijia Huang, Jianfeng Gao, Lei Zhang, Chunyuan Li, Jianwei Yang

To address this issue, we have created GVC data that allows for the combination of grounding and chat capabilities.

T-Rex: Counting by Visual Prompting

no code implementations22 Nov 2023 Qing Jiang, Feng Li, Tianhe Ren, Shilong Liu, Zhaoyang Zeng, Kent Yu, Lei Zhang

Guided by the visual feedback from T-Rex, users can also interactively refine the counting results by prompting on missing or falsely-detected objects.

Object Object Counting +4

Visual In-Context Prompting

3 code implementations22 Nov 2023 Feng Li, Qing Jiang, Hao Zhang, Tianhe Ren, Shilong Liu, Xueyan Zou, Huaizhe xu, Hongyang Li, Chunyuan Li, Jianwei Yang, Lei Zhang, Jianfeng Gao

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.

Segmentation Visual Prompting

InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image

1 code implementation6 Nov 2023 Jianhui Li, Shilong Liu, Zidong Liu, Yikai Wang, Kaiwen Zheng, Jinghui Xu, Jianmin Li, Jun Zhu

With the success of Neural Radiance Field (NeRF) in 3D-aware portrait editing, a variety of works have achieved promising results regarding both quality and 3D consistency.

Neural Interactive Keypoint Detection

1 code implementation ICCV 2023 Jie Yang, Ailing Zeng, Feng Li, Shilong Liu, Ruimao Zhang, Lei Zhang

Click-Pose explores how user feedback can cooperate with a neural keypoint detector to correct the predicted keypoints in an interactive way for a faster and more effective annotation process.

Keypoint Detection

DFA3D: 3D Deformable Attention For 2D-to-3D Feature Lifting

no code implementations ICCV 2023 Hongyang Li, Hao Zhang, Zhaoyang Zeng, Shilong Liu, Feng Li, Tianhe Ren, Lei Zhang

Existing feature lifting approaches, such as Lift-Splat-based and 2D attention-based, either use estimated depth to get pseudo LiDAR features and then splat them to a 3D space, which is a one-pass operation without feature refinement, or ignore depth and lift features by 2D attention mechanisms, which achieve finer semantics while suffering from a depth ambiguity problem.

3D Object Detection object-detection

Semantic-SAM: Segment and Recognize Anything at Any Granularity

1 code implementation10 Jul 2023 Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao

In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity.

Image Segmentation Segmentation +1

detrex: Benchmarking Detection Transformers

1 code implementation12 Jun 2023 Tianhe Ren, Shilong Liu, Feng Li, Hao Zhang, Ailing Zeng, Jie Yang, Xingyu Liao, Ding Jia, Hongyang Li, He Cao, Jianan Wang, Zhaoyang Zeng, Xianbiao Qi, Yuhui Yuan, Jianwei Yang, Lei Zhang

To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation.

Benchmarking object-detection +2

Recognize Anything: A Strong Image Tagging Model

2 code implementations6 Jun 2023 Youcai Zhang, Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, Yandong Guo, Lei Zhang

We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.

Semantic Parsing

A Strong and Reproducible Object Detector with Only Public Datasets

2 code implementations25 Apr 2023 Tianhe Ren, Jianwei Yang, Shilong Liu, Ailing Zeng, Feng Li, Hao Zhang, Hongyang Li, Zhaoyang Zeng, Lei Zhang

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 parameters without any test time augmentation.

Ranked #5 on Object Detection on COCO minival (using extra training data)

object-detection Object Detection

PREIM3D: 3D Consistent Precise Image Attribute Editing from a Single Image

1 code implementation CVPR 2023 Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu, Zhengyi Wang, Zihao Xiao, Kaiwen Zheng, Jun Zhu

As for imprecise image editing, we attribute the problem to the gap between the latent space of real images and that of generated images.


Detection Transformer with Stable Matching

1 code implementation ICCV 2023 Shilong Liu, Tianhe Ren, Jiayu Chen, Zhaoyang Zeng, Hao Zhang, Feng Li, Hongyang Li, Jun Huang, Hang Su, Jun Zhu, Lei Zhang

We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR.


A Simple Framework for Open-Vocabulary Segmentation and Detection

2 code implementations ICCV 2023 Hao Zhang, Feng Li, Xueyan Zou, Shilong Liu, Chunyuan Li, Jianfeng Gao, Jianwei Yang, Lei Zhang

We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets.

Ranked #2 on Instance Segmentation on ADE20K val (using extra training data)

Instance Segmentation Panoptic Segmentation +2

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

7 code implementations9 Mar 2023 Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang

To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion.

Referring Expression Referring Expression Comprehension +2

Introducing Depth into Transformer-based 3D Object Detection

no code implementations25 Feb 2023 Hao Zhang, Hongyang Li, Ailing Zeng, Feng Li, Shilong Liu, Xingyu Liao, Lei Zhang

To address the second issue, we introduce an auxiliary learning task called Depth-aware Negative Suppression loss.

3D Object Detection Auxiliary Learning +3

Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation

3 code implementations3 Feb 2023 Jie Yang, Ailing Zeng, Shilong Liu, Feng Li, Ruimao Zhang, Lei Zhang

This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.

2D Human Pose Estimation Human Detection +3

DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding

1 code implementation28 Nov 2022 Shilong Liu, Yaoyuan Liang, Feng Li, Shijia Huang, Hao Zhang, Hang Su, Jun Zhu, Lei Zhang

As phrase extraction can be regarded as a $1$D text segmentation problem, we formulate PEG as a dual detection problem and propose a novel DQ-DETR model, which introduces dual queries to probe different features from image and text for object prediction and phrase mask prediction.

object-detection Object Detection +4

DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

14 code implementations7 Mar 2022 Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M. Ni, Heung-Yeung Shum

Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.

Real-Time Object Detection

Vision-Language Intelligence: Tasks, Representation Learning, and Large Models

no code implementations3 Mar 2022 Feng Li, Hao Zhang, Yi-Fan Zhang, Shilong Liu, Jian Guo, Lionel M. Ni, Pengchuan Zhang, Lei Zhang

This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension.

Few-Shot Learning Representation Learning

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

16 code implementations CVPR 2022 Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M. Ni, Lei Zhang

Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement.

Object Detection

DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

7 code implementations ICLR 2022 Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang

We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR.

Object Detection

Query2Label: A Simple Transformer Way to Multi-Label Classification

2 code implementations22 Jul 2021 Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu

The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image.

Classification Multi-Label Classification

Unsupervised Part Segmentation through Disentangling Appearance and Shape

no code implementations CVPR 2021 Shilong Liu, Lei Zhang, Xiao Yang, Hang Su, Jun Zhu

We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results.

Disentanglement Object +3

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