Search Results for author:
Found 25 papers, 17 papers with code
Align-DETR: Improving DETR with Simple IoU-aware BCE loss
We propose a metric, recall of best-regressed samples, to quantitively evaluate the misalignment problem.
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.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.
How Powerful is Implicit Denoising in Graph Neural Networks
However, the analysis of implicit denoising effect in graph neural networks remains open.
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.
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.
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)
Local Augmentation for Graph Neural Networks
To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.
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)
IQDet: Instance-wise Quality Distribution Sampling for Object Detection
We propose a dense object detector with an instance-wise sampling strategy, named IQDet.
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 #73 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.
LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.
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.
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.
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Ranked #16 on
Few-Shot Object Detection
on MS-COCO (30-shot)
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.
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred.
Learning Spatial Fusion for Single-Shot Object Detection
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.
Ranked #147 on
Object Detection
on COCO test-dev
Higher-order Weighted Graph Convolutional Networks
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Pedestrian detection in a crowd is a very challenging issue.
Ranked #17 on
Object Detection
on CrowdHuman (full body)
Receptive Field Block Net for Accurate and Fast Object Detection
Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs.
