Search Results for author:
Found 28 papers, 7 papers with code
End-to-End Personalized Next Location Recommendation via Contrastive User Preference Modeling
History encoder is designed to model mobility patterns from historical check-in sequences, while query generator explicitly learns user preferences to generate user-specific intention queries.
Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images
This deficiency is a confounder that limits the performance of existing MIL methods.
Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective
In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC).
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training
During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to ``reconstruct'' the motion.
Towards a Unified View on Visual Parameter-Efficient Transfer Learning
Towards this goal, we propose a framework with a unified view of PETL called visual-PETL (V-PETL) to investigate the effects of different PETL techniques, data scales of downstream domains, positions of trainable parameters, and other aspects affecting the trade-off.
Learned Video Compression via Heterogeneous Deformable Compensation Network
In this paper, we propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance caused by single-size deformable kernels in downsampled feature domain.
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era.
Ranked #1 on
Highlight Detection
on YouTube Highlights
Taking an Emotional Look at Video Paragraph Captioning
In this work, a comprehensive study is conducted on video paragraph captioning, with the goal to generate paragraph-level descriptions for a given video.
Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling
Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion.
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively.
Optimized Separable Convolution: Yet Another Efficient Convolution Operator
In this research, we propose a novel and principled operator called optimized separable convolution by optimal design for the internal number of groups and kernel sizes for general separable convolutions can achieve the complexity of O(C^{\frac{3}{2}}K).
Improving Contrastive Learning by Visualizing Feature Transformation
To our knowledge, this is the first attempt of its kind.
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
Rethinking Convolution: Towards an Optimal Efficiency
In this research, we propose a novel operator called \emph{optimal separable convolution} which can be calculated at $O(C^{\frac{3}{2}}KHW)$ by optimal design for the internal number of groups and kernel sizes for general separable convolutions.
ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection
We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of <human, action, object> in images.
Human-Object Interaction Detection
Zero-Shot Human-Object Interaction Detection
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos
Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos.
From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation
To remove the need for the parallel training corpora has practical significance for real-world applications, and it is one of the main goals of unsupervised learning.
AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations
This ensures the resultant TERs of individual images contain the {\em intrinsic} information about their visual structures that would equivary {\em extricably} under various transformations in a generalized {\em nonlinear} case.
Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos
Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion.
DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks
Specifically, we jointly learn a deep attention encoder, and the instance-level correspondences could be consequently discovered through attending on the learned instances.
DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)
Specifically, we jointly learn a deep attention encoder, and the instancelevel correspondences could be consequently discovered through attending on the learned instance pairs.
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties.
Network Iterative Learning for Dynamic Deep Neural Networks via Morphism
In this research, we present a novel learning scheme called network iterative learning for deep neural networks.
Fast Haze Removal for Nighttime Image Using Maximum Reflectance Prior
Then, we propose a simple but effective image prior, maximum reflectance prior, to estimate the varying ambient illumination.
A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input.
Ranked #2 on
Aesthetics Quality Assessment
on AVA
Modularized Morphing of Neural Networks
Different from existing work where basic morphing types on the layer level were addressed, we target at the central problem of network morphism at a higher level, i. e., how a convolutional layer can be morphed into an arbitrary module of a neural network.
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network
Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline.
Network Morphism
The second requirement for this network morphism is its ability to deal with non-linearity in a network.
