Search Results for author:
Found 13 papers, 4 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.
GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving
With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow.
3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo Auto-labelling
We present a novel approach from the perspective of auto-labelling, aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds.
Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection
3D object Detection with LiDAR-camera encounters overfitting in algorithm development which is derived from the violation of some fundamental rules.
HPS-Det: Dynamic Sample Assignment with Hyper-Parameter Search for Object Detection
Sample assignment plays a prominent part in modern object detection approaches.
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 #8 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.
DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators
On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by na\"ive implementations without optimizations, and the throughput is further improved up to 1. 26x by leveraging heterogeneous optimizations in DNNVM.
Deep Image: Scaling up Image Recognition
We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning.
