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Found 44 papers, 20 papers with code
MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training
Most MI attacks in the literature take advantage of the fact that ML models are trained to fit the training data well, and thus have very low loss on training instances.
Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion
In this work, we propose a novel strategy named \textbf{Degeneration-Tuning (DT)} to shield contents of unwanted concepts from SD weights.
PrimeNet: Pre-Training for Irregular Multivariate Time Series
In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time series.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting
Motivated by these insights, we present a novel method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data.
Multi-stage Progressive Reasoning for Dunhuang Murals Inpainting
The execution of the model is similar to the process of a mural restorer (i. e., inpainting the structure of the damaged mural globally first and then adding the local texture details further).
Deep Attention Unet: A Network Model with Global Feature Perception Ability
Remote sensing image segmentation is a specific task of remote sensing image interpretation.
Region-Aware Portrait Retouching with Sparse Interactive Guidance
Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch.
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables
However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restricts its receptive field and thus the performance.
Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases
Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements.
SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference
In this paper, we propose a novel dynamic early exiting combined with layer skipping for BERT inference named SmartBERT, which adds a skipping gate and an exiting operator into each layer of BERT.
Lformer: Text-to-Image Generation with L-shape Block Parallel Decoding
To better accelerate the generative transformers while keeping good generation quality, we propose Lformer, a semi-autoregressive text-to-image generation model.
Style Projected Clustering for Domain Generalized Semantic Segmentation
In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation.
Learning Steerable Function for Efficient Image Resampling
Image resampling is a basic technique that is widely employed in daily applications.
An advanced YOLOv3 method for small object detection
Small object detection has important application value in the fields of autonomous driving and drone scene analysis.
CLIP also Understands Text: Prompting CLIP for Phrase Understanding
However, the application of its text encoder solely for text understanding has been less explored.
Deep Learning for Logo Detection: A Survey
When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks.
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
Then, to obtain personalized explanations under this framework of insertion-based generation, we design a method of incorporating aspect planning and personalized references into the insertion process.
SPOT: Knowledge-Enhanced Language Representations for Information Extraction
To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively.
Ranked #4 on
Relation Extraction
on SemEval-2010 Task-8
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations
In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and visual information to explain our recommendations.
Fine-grained Contrastive Learning for Relation Extraction
Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on gold labels.
Ranked #36 on
Relation Extraction
on DocRED
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.
Coarse-to-Fine Sparse Sequential Recommendation
We propose to model user dynamics from shopping intents and interacted items simultaneously.
Self-supervised Social Relation Representation for Human Group Detection
The core of human group detection is the human social relation representation and division. In this paper, we propose a new two-stage multi-head framework for human group detection.
Panoramic Human Activity Recognition
To obtain a more comprehensive activity understanding for a crowded scene, in this paper, we propose a new problem of panoramic human activity recognition (PAR), which aims to simultaneous achieve the individual action, social group activity, and global activity recognition.
Space Layout of Low-entropy Hydration Shells Guides Protein Binding
According to an analysis of determined protein complex structures, shape matching between the largest low-entropy hydration shell region of a protein and that of its partner at the binding sites is revealed as a regular pattern.
Contextual Outpainting With Object-Level Contrastive Learning
To model the contextual correlation between foreground and background contents, we incorporate an object-level contrastive loss to regularize the learning of cross-modal representations of foreground contents and the corresponding background semantic layout, facilitating accurate semantic reasoning.
Connecting the Complementary-View Videos: Joint Camera Identification and Subject Association
In this paper, we develop a new approach that can simultaneously handle three tasks: i) localizing the side-view camera in the top view; ii) estimating the view direction of the side-view camera; iii) detecting and associating the same subjects on the ground across the complementary views.
Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w MRI of Human Brain Hemispheres
Ex vivo MRI of the brain provides remarkable advantages over in vivo MRI for visualizing and characterizing detailed neuroanatomy.
Weakly Supervised Named Entity Tagging with Learnable Logical Rules
We study the problem of building entity tagging systems by using a few rules as weak supervision.
Hydrophobic interaction determines docking affinity of SARS CoV 2 variants with antibodies
Preliminary epidemiologic, phylogenetic and clinical findings suggest that several novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have increased transmissibility and decreased efficacy of several existing vaccines.
SeNsER: Learning Cross-Building Sensor Metadata Tagger
Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e. g., measurement type and location) about sensors for running smart building applications.
The role of hydrophobic interactions in folding of $β$-sheets
Exploring the protein-folding problem has been a long-standing challenge in molecular biology.
A hydrophobic-interaction-based mechanism trigger docking between the SARS CoV 2 spike and angiotensin-converting enzyme 2
The hydrophobic interaction between the SARS-CoV-2 S and ACE2 protein is found to be significantly greater than that between SARS-CoV S and ACE2.
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling
In this paper, we focus on enhancing the generalization ability of the VIST model by considering the few-shot setting.
DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users' Dynamic Preferences for Information Diffusion Prediction
For one thing, previous works cannot jointly utilize both the social network and diffusion graph for prediction, which is insufficient to model the complexity of the diffusion process and results in unsatisfactory prediction performance.
Membership Inference Attacks and Defenses in Classification Models
We study the membership inference (MI) attack against classifiers, where the attacker's goal is to determine whether a data instance was used for training the classifier.
TiSASRec: Time Interval Aware Self-Attention for Sequential Recommendation
Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently.
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
Several recent works have considered the problem of generating reviews (or {`}tips{'}) as a form of explanation as to why a recommendation might match a customer{'}s interests.
Informative Visual Storytelling with Cross-modal Rules
To solve this problem, we propose a method to mine the cross-modal rules to help the model infer these informative concepts given certain visual input.
Ranked #11 on
Visual Storytelling
on VIST
ZJUNlict Extended Team Description Paper for RoboCup 2019
For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion and therefore, this paper thoroughly described the devotion which ZJUNLict has devoted and the effort that ZJUNLict has contributed.
Robotics 68T40
Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation
We develop a connection sensitive attention U-Net(CSAU) for accurate retinal vessel segmentation.
