Search Results for author:
Found 24 papers, 3 papers with code
ProgressFace: Scale-Aware Progressive Learning for Face Detection
In this work, we propose a novel scale-aware progressive training mechanism to address large scale variations across faces.
Sparse Laneformer
We analyze that dense anchors are not necessary for lane detection, and propose a transformer-based lane detection framework based on a sparse anchor mechanism.
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Our method also achieves state-of-the-art pruning performance on the vision transformer model.
Separated RoadTopoFormer
Previous works such as map learning and BEV lane detection neglect the connection relationship between lane instances, and traffic elements detection tasks usually neglect the relationship with lane lines.
Ranked #2 on
3D Lane Detection
on OpenLane-V2 val
Bootstrapping the Cross-Validation Estimate
Cross-validation is a widely used technique for evaluating the performance of prediction models.
Hierarchical Pretraining for Biomedical Term Embeddings
Electronic health records (EHR) contain narrative notes that provide extensive details on the medical condition and management of patients.
FDViT: Improve the Hierarchical Architecture of Vision Transformer
In this paper, we propose FDViT to improve the hierarchical architecture of the vision transformer by using a flexible downsampling layer that is not limited to integer stride to smoothly reduce the sizes of the middle feature maps.
HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection
Sample assignment plays a prominent part in modern object detection approaches.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification
First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition.
Ranked #6 on
Fine-Grained Image Classification
on CUB-200-2011
(using extra training data)
Fine-Grained Image Classification
Fine-Grained Visual Categorization
Dynamic Sparse R-CNN
Sparse R-CNN is a recent strong object detection baseline by set prediction on sparse, learnable proposal boxes and proposal features.
Towards Discriminative Representation Learning for Unsupervised Person Re-identification
We observe that these proposed schemes are capable of facilitating the learning of discriminative feature representations.
RankDetNet: Delving Into Ranking Constraints for Object Detection
To this end, we comprehensively investigate three types of ranking constraints, i. e., global ranking, class-specific ranking and IoU-guided ranking losses.
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving
Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets.
Improving Low-Precision Network Quantization via Bin Regularization
Such bin regularization (BR) mechanism encourages the weight distribution of each quantization bin to be sharp and approximate to a Dirac delta distribution ideally.
Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling
In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model.
Estimation and Validation of Ratio-based Conditional Average Treatment Effects Using Observational Data
Motivated by the need of modeling the number of relapses in multiple sclerosis patients, where the ratio of relapse rates is a natural choice of the treatment effect, we propose to estimate the conditional average treatment effect (CATE) as the ratio of expected potential outcomes, and derive a doubly robust estimator of this CATE in a semiparametric model of treatment-covariate interactions.
IFAA: Robust association identification and Inference For Absolute Abundance in microbiome analyses
The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e. g., stool) can be considered as an approximation of RA in an entire ecosystem (e. g., gut).
Applications
Metric Learning in Codebook Generation of Bag-of-Words for Person Re-identification
Person re-identification is generally divided into two part: first how to represent a pedestrian by discriminative visual descriptors and second how to compare them by suitable distance metrics.
Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models
In detail, the proposed method distributes the $d$-dimensional data of size $N$ generated from a transelliptical graphical model into $m$ worker machines, and estimates the latent precision matrix on each worker machine based on the data of size $n=N/m$.
Communication-efficient Distributed Sparse Linear Discriminant Analysis
We propose a communication-efficient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime.
Scalable Person Re-Identification: A Benchmark
As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor.
Ranked #90 on
Person Re-Identification
on DukeMTMC-reID
Query-Adaptive Late Fusion for Image Search and Person Re-Identification
However, in a more realistic situation, one does not know in advance whether a feature is effective or not for a given query.
Person Re-identification Meets Image Search
In the light of recent advances in image search, this paper proposes to treat person re-identification as an image search problem.
