Search Results for author:
Found 24 papers, 10 papers with code
Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
To overcome this, we propose HyperVD, a novel framework that learns snippet embeddings in hyperbolic space to improve model discrimination.
Adaptive Sparse Pairwise Loss for Object Re-Identification
To address this problem, we propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks.
Probabilistic Bilevel Coreset Selection
The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the entire dataset.
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
The overfitting issue is addressed by considering a bilevel formulation to search for the sample reweighting, in which the generalization complexity depends on the search space of sample weights instead of the model size.
GWRBoost:A geographically weighted gradient boosting method for explainable quantification of spatially-varying relationships
The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts.
Realtime Fewshot Portrait Stylization Based On Geometric Alignment
Previous learning based stylization methods suffer from the geometric and semantic gaps between portrait domain and style domain, which obstacles the style information to be correctly transferred to the portrait images, leading to poor stylization quality.
WeLM: A Well-Read Pre-trained Language Model for Chinese
Large Language Models pre-trained with self-supervised learning have demonstrated impressive zero-shot generalization capabilities on a wide spectrum of tasks.
UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation
As an essential prerequisite for developing a medical intelligent assistant system, medical image segmentation has received extensive research and concentration from the neural network community.
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Neural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature
In this paper, we investigate using rolling shutter with a global reset feature (RSGR) to restore clean global shutter (GS) videos.
Evaluation of non-pharmaceutical interventions and optimal strategies for containing the COVID-19 pandemic
Given multiple new COVID-19 variants are continuously emerging, non-pharmaceutical interventions are still primary control strategies to curb the further spread of coronavirus.
Black-box Prompt Learning for Pre-trained Language Models
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
Efficient Neural Network Training via Forward and Backward Propagation Sparsification
For the latter step, instead of using the chain rule based gradient estimators as in existing methods, we propose a variance reduced policy gradient estimator, which only requires two forward passes without backward propagation, thus achieving completely sparse training.
Effective Sparsification of Neural Networks with Global Sparsity Constraint
Weight pruning is an effective technique to reduce the model size and inference time for deep neural networks in real-world deployments.
Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB
To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager.
Explicit Connection Distillation
In this paper, we present Explicit Connection Distillation (ECD), a new KD framework, which addresses the knowledge distillation problem in a novel perspective of bridging dense intermediate feature connections between a student network and its corresponding teacher generated automatically in the training, achieving knowledge transfer goal via direct cross-network layer-to-layer gradients propagation, without need to define complex distillation losses and assume a pre-trained teacher model to be available.
Effective Training of Sparse Neural Networks under Global Sparsity Constraint
An appealing feature of ProbMask is that the amounts of weight redundancy can be learned automatically via our constraint and thus we avoid the problem of tuning pruning rates individually for different layers in a network.
Diversifying Dialogue Generation with Non-Conversational Text
Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation.
A Study of Pyramid Structure for Code Correction
We demonstrate the implementations of pyramid encoders in both multi-layer GRU and Transformer for seq2seq tasks.
Software Engineering
Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems
The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes.
Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation
Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry.
Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning
Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to offer generous cultural entertainment to its citizens.
Heuristic Search for Structural Constraints in Data Association
In this paper, we propose a new heuristic method to search for structural constraints (HSSC) of multiple targets when solving the problem of online multi-object tracking.
