Search Results for author:
Found 57 papers, 15 papers with code
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting
User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e. g., green tea), identify the online users to whom the ad package should be targeted.
Gradient Concealment: Free Lunch for Defending Adversarial Attacks
Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks.
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
The past year has witnessed a rapid development of masked image modeling (MIM).
Deep Point Cloud Simplification for High-quality Surface Reconstruction
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
MTLDesc: Looking Wider to Describe Better
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context.
TAPE: Task-Agnostic Prior Embedding for Image Restoration
Learning an generalized prior for natural image restoration is an important yet challenging task.
The KFIoU Loss for Rotated Object Detection
Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics.
Ranked #1 on
Object Detection In Aerial Images
on DOTA
NeuSample: Neural Sample Field for Efficient View Synthesis
Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy.
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #56 on
Semantic Segmentation
on Cityscapes test
(using extra training data)
Self-supervised High-fidelity and Re-renderable 3D Facial Reconstruction from a Single Image
Although each method has its own advantage, none of them is capable of recovering a high-fidelity and re-renderable facial texture, where the term 're-renderable' demands the facial texture to be spatially complete and disentangled with environmental illumination.
Understanding Self-supervised Learning via Information Bottleneck Principle
To address this issue, we introduce the information bottleneck principle and propose the Self-supervised Variational Information Bottleneck (SVIB) learning framework.
Bag of Instances Aggregation Boosts Self-supervised Distillation
Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
This is achieved by first pretraining the network via the proposed pixel-to-prototype contrastive loss over multiple datasets regardless of their taxonomy labels, and followed by fine-tuning the pretrained model over specific dataset as usual.
Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
Besides, limited by the lack of information fusion between the two towers, the model learning is insufficient to represent users' preferences on various topics well.
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens
Transformers have offered a new methodology of designing neural networks for visual recognition.
Semi-supervised Contrastive Learning with Similarity Co-calibration
Semi-supervised learning acts as an effective way to leverage massive unlabeled data.
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
Deep Deformation Detail Synthesis for Thin Shell Models
Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes.
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
Ranked #5 on
Object Detection In Aerial Images
on DOTA
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
In this paper, we propose a hierarchical semantic alignment strategy via expanding the views generated by a single image to \textbf{Cross-samples and Multi-level} representation, and models the invariance to semantically similar images in a hierarchical way.
Ranked #37 on
Self-Supervised Image Classification
on ImageNet
Scene text removal via cascaded text stroke detection and erasing
Specifically, we decouple the text removal problem into text stroke detection and stroke removal.
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation.
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning.
Center-wise Local Image Mixture For Contrastive Representation Learning
Contrastive learning based on instance discrimination trains model to discriminate different transformations of the anchor sample from other samples, which does not consider the semantic similarity among samples.
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry.
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results.
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks
To learn the sampling policy, a Markov decision process is embedded into the search algorithm and a moving average is applied for better stability.
Distilling Object Detectors with Task Adaptive Regularization
Knowledge distillation, which aims at training a smaller student network by transferring knowledge from a larger teacher model, is one of the promising solutions for model miniaturization.
A survey on deep hashing for image retrieval
To this end, I propose a concept: shadow of the CNN output.
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i. e., shape factor, mean, and variance).
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples.
Ranked #12 on
Fine-Grained Image Classification
on CUB-200-2011
Circumventing Outliers of AutoAugment with Knowledge Distillation
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization.
Ranked #93 on
Image Classification
on ImageNet
MGCN: Descriptor Learning using Multiscale GCNs
Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor.
Latency-Aware Differentiable Neural Architecture Search
However, these methods suffer the difficulty in optimizing network, so that the searched network is often unfriendly to hardware.
Wasserstein-Bounded Generative Adversarial Networks
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
Capacity Preserving Mapping for High-dimensional Data Visualization
We provide a rigorous mathematical treatment to the crowding issue in data visualization when high dimensional data sets are projected down to low dimensions for visualization.
Central Similarity Quantization for Efficient Image and Video Retrieval
In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy.
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture.
Ranked #15 on
Neural Architecture Search
on CIFAR-10
Distilling Object Detectors with Fine-grained Feature Imitation
To address the challenge of distilling knowledge in detection model, we propose a fine-grained feature imitation method exploiting the cross-location discrepancy of feature response.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Few-shot Adaptive Faster R-CNN
To address these challenges, we first introduce a pairing mechanism over source and target features to alleviate the issue of insufficient target domain samples.
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization
First, we design and implement a base network, which can attain better performance in terms of classification accuracy and generalization (in most cases) compared with state-of-the-art methods.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
ML-LocNet: Improving Object Localization with Multi-view Learning Network
This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision.
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.
Zigzag Learning for Weakly Supervised Object Detection
Unlike them, we propose a zigzag learning strategy to simultaneously discover reliable object instances and prevent the model from overfitting initial seeds.
Ranked #13 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
A Quantization-Friendly Separable Convolution for MobileNets
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
Hardware-Efficient Guided Image Filtering For Multi-Label Problem
In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance.
Speeding Up the Bilateral Filter: A Joint Acceleration Way
To achieve the constant-time BF whose complexity is irrelevant to the kernel size, many techniques have been proposed, such as 2D box filtering, dimension promotion, and shiftability property.
Ensemble of Part Detectors for Simultaneous Classification and Localization
Part-based representation has been proven to be effective for a variety of visual applications.
Picking Deep Filter Responses for Fine-Grained Image Recognition
Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging due to the highly localized and subtle differences in some specific parts.
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing
In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel, which enables the filter to smooth out high-contrast details and textures while preserving major image structures very well.
Fully Connected Guided Image Filtering
This paper presents a linear time fully connected guided filter by introducing the minimum spanning tree (MST) to the guided filter (GF).
Image Retargeting by Content-Aware Synthesis
We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators.
Segment-Tree Based Cost Aggregation for Stereo Matching
Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, "Segment-Tree", is proposed for non-local matching cost aggregation.
