Search Results for author:
Found 47 papers, 34 papers with code
Observation, Analysis, and Solution: Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training
In this paper, we question if the extremely simple ViTs' fine-tuning performance with a small-scale architecture can also benefit from this pre-training paradigm, which is considerably less studied yet in contrast to the well-established lightweight architecture design methodology with sophisticated components introduced.
HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations
It is especially challenging to achieve real-time human motion tracking on a standalone VR Head-Mounted Display (HMD) such as Meta Quest and PICO.
GMM: Delving into Gradient Aware and Model Perceive Depth Mining for Monocular 3D Detection
In light of the success of sample mining techniques in 2D object detection, we propose a simple yet effective mining strategy for improving depth perception in 3D object detection.
Dynamic Grained Encoder for Vision Transformers
Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes.
MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception
This paper proposes an efficient multi-camera to Bird's-Eye-View (BEV) view transformation method for 3D perception, dubbed MatrixVT.
Ranked #2 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU lane - 224x480 - 100x100 at 0.5 metric)
BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo
To this end, we introduce an effective temporal stereo method to dynamically select the scale of matching candidates, enable to significantly reduce computation overhead.
Ranked #11 on
3D Object Detection
on nuScenes Camera Only
Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking
Recent advanced works generally employ a series of object attributes, e. g., position, size, velocity, and appearance, to provide the clues for the association in 3D MOT.
STS: Surround-view Temporal Stereo for Multi-view 3D Detection
Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view Temporal Stereo (STS) technique that leverages the geometry correspondence between frames across time to facilitate accurate depth learning.
PersDet: Monocular 3D Detection in Perspective Bird's-Eye-View
To address this problem, we revisit the generation of BEV representation and propose detecting objects in perspective BEV -- a new BEV representation that does not require feature sampling.
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference.
StreamYOLO: Real-time Object Detection for Streaming Perception
In this paper, we explore the performance of real time models on this metric and endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
DSPNet: Towards Slimmable Pretrained Networks based on Discriminative Self-supervised Learning
However, when facing various resource budgets in real-world applications, it costs a huge computation burden to pretrain multiple networks of various sizes one by one.
Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters.
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View (BEV) 3D object detection.
Ranked #4 on
3D Object Detection
on Rope3D
Unifying Voxel-based Representation with Transformer for 3D Object Detection
To this end, the modality-specific space is first designed to represent different inputs in the voxel feature space.
Voxel Field Fusion for 3D Object Detection
In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion.
A Closer Look at Self-Supervised Lightweight Vision Transformers
We also point out some defects of such pre-training, e. g., failing to benefit from large-scale pre-training data and showing inferior performance on data-insufficient downstream tasks.
Real-time Object Detection for Streaming Perception
In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem.
Ranked #1 on
Real-Time Object Detection
on Argoverse-HD (Full-Stack, Val)
(sAP metric, using extra
training data)
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining (ResCom) to tackle imbalanced recognition.
Ranked #5 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision
In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly.
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario.
YOLOX: Exceeding YOLO Series in 2021
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.
Ranked #1 on
Real-Time Object Detection
on Argoverse-HD (Detection-Only, Val)
(using extra training data)
Generalized Few-Shot Object Detection without Forgetting
Recently few-shot object detection is widely adopted to deal with data-limited situations.
IQDet: Instance-wise Quality Distribution Sampling for Object Detection
We propose a dense object detector with an instance-wise sampling strategy, named IQDet.
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Motivated by our discovery, we propose a unified distribution alignment strategy for long-tail visual recognition.
Ranked #17 on
Long-tail Learning
on Places-LT
OTA: Optimal Transport Assignment for Object Detection
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.
Ranked #62 on
Object Detection
on COCO test-dev
Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning
The teacher's weight is a momentum update of the student, and the teacher's BN statistics is a momentum update of those in history.
Rethinking Learnable Tree Filter for Generic Feature Transform
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation.
Fine-Grained Dynamic Head for Object Detection
To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation.
End-to-End Object Detection with Fully Convolutional Network
Mainstream object detectors based on the fully convolutional network has achieved impressive performance.
Fully Convolutional Networks for Panoptic Segmentation
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN.
Ranked #1 on
Panoptic Segmentation
on COCO minival
(SQ metric)
Self-EMD: Self-Supervised Object Detection without ImageNet
Our Faster R-CNN (ResNet50-FPN) baseline achieves 39. 8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet.
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track
In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation.
EqCo: Equivalent Rules for Self-supervised Contrastive Learning
In this paper, we propose EqCo (Equivalent Rules for Contrastive Learning) to make self-supervised learning irrelevant to the number of negative samples in the contrastive learning framework.
BorderDet: Border Feature for Dense Object Detection
In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.
AutoAssign: Differentiable Label Assignment for Dense Object Detection
During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.
Dynamic Scale Training for Object Detection
We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection.
Learning Dynamic Routing for Semantic Segmentation
To demonstrate the superiority of the dynamic property, we compare with several static architectures, which can be modeled as special cases in the routing space.
Learnable Tree Filter for Structure-preserving Feature Transform
To this end, tree filtering modules are embedded to formulate a unified framework for semantic segmentation.
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019).
Ranked #5 on
3D Object Detection
on nuScenes LiDAR only
ThunderNet: Towards Real-time Generic Object Detection
In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet.
Ranked #15 on
Object Detection
on PASCAL VOC 2007
DetNet: Design Backbone for Object Detection
(1) Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales.
MetaAnchor: Learning to Detect Objects with Customized Anchors
We propose a novel and flexible anchor mechanism named MetaAnchor for object detection frameworks.
DetNet: A Backbone network for Object Detection
Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection.
MegDet: A Large Mini-Batch Object Detector
The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design.
Light-Head R-CNN: In Defense of Two-Stage Object Detector
More importantly, simply replacing the backbone with a tiny network (e. g, Xception), our Light-Head R-CNN gets 30. 7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy.
