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Found 33 papers, 16 papers with code
Training-Time-Friendly Network for Real-Time Object Detection
Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy.
CFS: A Distributed File System for Large Scale Container Platforms
We propose CFS, a distributed file system for large scale container platforms.
Distributed, Parallel, and Cluster Computing
The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform
We present the design and implementation of a visual search system for real time image retrieval on JD. com, the world's third largest and China's largest e-commerce site.
PixelLink: Detecting Scene Text via Instance Segmentation
Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/non-text classification and location regression.
Ranked #6 on
Scene Text Detection
on ICDAR 2013
RESA: Recurrent Feature-Shift Aggregator for Lane Detection
Lane detection is one of the most important tasks in self-driving.
Ranked #4 on
Lane Detection
on TuSimple
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion
Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance.
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES.
Ranked #12 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
Therefore, we propose to reformulate the instance depth to the combination of the instance visual surface depth (visual depth) and the instance attribute depth (attribute depth).
Lidar Point Cloud Guided Monocular 3D Object Detection
We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels.
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise.
Adversarial-Learned Loss for Domain Adaptation
In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA).
TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.
Suppress-and-Refine Framework for End-to-End 3D Object Detection
In this work, we propose a suppress-and-refine framework to remove these handcrafted components.
Deep Rotation Equivariant Network
Recently, learning equivariant representations has attracted considerable research attention.
On the Diversity of Realistic Image Synthesis
Experimental results show that images synthesized by our approach are significantly more diverse than that of the current existing works and equipping our diversity loss does not degrade the reality of the base networks.
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Both content information (e. g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item.
BENCHIP: Benchmarking Intelligence Processors
The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware).
Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention.
Separable Kernel for Image Deblurring
In this paper, we deal with the image deblurring problem in a completely new perspective by proposing separable kernel to represent the inherent properties of the camera and scene system.
DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration
The dominant pruning methods, filter-level pruning methods, evaluate their performance through the reduction ratio of computations and deem that a higher reduction ratio of computations is equivalent to a higher acceleration ratio in terms of inference time.
Multi-Class Classification from Noisy-Similarity-Labeled Data
We further estimate the transition matrix from only noisy data and build a novel learning system to learn a classifier which can assign noise-free class labels for instances.
Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds
3D object detection based on point clouds has become more and more popular.
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
To give an affirmative answer, in this paper, we propose a framework called Class2Simi: it transforms data points with noisy class labels to data pairs with noisy similarity labels, where a similarity label denotes whether a pair shares the class label or not.
Socially-Aware Conference Participant Recommendation with Personality Traits
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework
Specifically, it first casts the relationships between a certain model's accuracy and depth/width/resolution into a polynomial regression and then maximizes the polynomial to acquire the optimal values for the three dimensions.
Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification
In this paper, to complement these low recall neighbor pseudo labels, we propose a joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels.
Class2Simi: A New Perspective on Learning with Label Noise
It is worthwhile to perform the transformation: We prove that the noise rate for the noisy similarity labels is lower than that of the noisy class labels, because similarity labels themselves are robust to noise.
Convolutional Embedding Makes Hierarchical Vision Transformer Stronger
Vision Transformers (ViTs) have recently dominated a range of computer vision tasks, yet it suffers from low training data efficiency and inferior local semantic representation capability without appropriate inductive bias.
Boosting Semi-Supervised 3D Object Detection with Semi-Sampling
When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.
Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend
Our comprehensive experiments reveal that in approximately 90\% of cases, word-level attacks lead to the generation of examples where the frequency of $n$-grams decreases, a tendency we term as the $n$-gram Frequency Descend ($n$-FD).
Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer
In recent years, various companies have started to shift their data services from traditional data centers to the cloud.
Boosting Few-Shot Learning via Attentive Feature Regularization
Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor.
