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Found 67 papers, 25 papers with code
3D Molecular Geometry Analysis with 2D Graphs
Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs.
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph
In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.
FMGNN: Fused Manifold Graph Neural Network
Graph representation learning has been widely studied and demonstrated effectiveness in various graph tasks.
PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model
In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial.
Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery
Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes.
Discriminating Known From Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder
To simulate this ability, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect the objects that are never-seen-before during model training, which is beneficial for promoting the safe deployment of object detectors.
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities.
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning
Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets.
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
With the training going on, the model begins to overfit noisy pairs.
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
Based on that, we propose a novel and practical VFL framework with black-box models, which is inseparably interconnected to the promising properties of ZOO.
Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency
Unfortunately, these methods only focus on selecting samples from the memory bank for replay and ignore the adequate exploration of semantic information in the single-pass data stream, leading to poor classification accuracy.
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation
Particularly, for the night-sunny scene, our method outperforms baselines by 3%, which indicates that our method is instrumental in enhancing generalization ability.
Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery
In response to the explosively-increasing requirement of annotated data, Novel Class Discovery (NCD) has emerged as a promising alternative to automatically recognize unknown classes without any annotation.
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning
While in metric space, we utilize weakly-supervised contrastive learning to excavate these negative correlations hidden in noisy data.
Mutual Quantization for Cross-Modal Search With Noisy Labels
More specifically, we propose a proxy-based contrastive (PC) loss to mitigate the gap between different modalities and train networks for different modalities jointly with small-loss samples that are selected with the PC loss and a mutual quantization loss.
Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement
To alleviate the impact of few samples, enhancing the generalization and discrimination abilities of detectors on new objects plays an important role.
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels
Since these noisy labels often cause severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Ranked #1 on
3D Geometry Prediction
on Molecule3D val
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer
And on this basis, a novel attribute transfer method, named semantic directional decomposition network (SDD-Net), is proposed to achieve semantic-level facial attribute transfer by latent semantic direction decomposition, improving the interpretability and editability of our method.
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i. e. AsySQN-SGD, -SVRG and -SAGA, are proposed.
Group Contrastive Self-Supervised Learning on Graphs
Our framework embeds the given graph into multiple subspaces, of which each representation is prompted to encode specific characteristics of graphs.
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches.
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network
Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity.
Unsupervised Hyperbolic Metric Learning
Learning feature embedding directly from images without any human supervision is a very challenging and essential task in the field of computer vision and machine learning.
Nearest Neighbor Matching for Deep Clustering
Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features.
Doubly Contrastive Deep Clustering
In this paper, we present a novel Doubly Contrastive Deep Clustering (DCDC) framework, which constructs contrastive loss over both sample and class views to obtain more discriminative features and competitive results.
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage.
Adversarial Attack on Deep Cross-Modal Hamming Retrieval
Specifically, given a target model, we first construct its substitute model to exploit cross-modal correlations within hamming space, with which we create adversarial examples by limitedly querying from a target model.
Incremental Embedding Learning via Zero-Shot Translation
Different from traditional incremental classification networks, the semantic gap between the embedding spaces of two adjacent tasks is the main challenge for embedding networks under incremental learning setting.
Adversarial Learning for Robust Deep Clustering
Meanwhile, we devise an adversarial attack strategy to explore samples that easily fool the clustering layers but do not impact the performance of the deep embedding.
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Grouping has been commonly used in deep metric learning for computing diverse features.
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
In this paper, we propose a novel Proxy-based deep Graph Metric Learning (ProxyGML) approach from the perspective of graph classification, which uses fewer proxies yet achieves better comprehensive performance.
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning
To the best of our knowledge, AFSGD-VP and its SVRG and SAGA variants are the first asynchronous federated learning algorithms for vertically partitioned data.
Projection & Probability-Driven Black-Box Attack
For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial.
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries
Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video.
Ranked #9 on
Referring Expression Segmentation
on J-HMDB
Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation
In addition, we address a key challenge in this multi-task setup, i. e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS).
Cross-Modal Learning with Adversarial Samples
Extensive experiments on two cross-modal benchmark datasets show that the adversarial examples produced by our CMLA are efficient in fooling a target deep cross-modal hashing network.
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
To address this issue, we propose a novel deep unsupervised hashing model, dubbed DistillHash, which can learn a distilled data set consisted of data pairs, which have confidence similarity signals.
Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval
Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario.
Deep Spectral Clustering using Dual Autoencoder Network
In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering.
Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search
For each hash table, a query-adaptive bitwise weighting is introduced to alleviate the quantization loss by simultaneously exploiting the quality of hash functions and their complement for nearest neighbor search.
Shared Predictive Cross-Modal Deep Quantization
In this paper, we propose a deep quantization approach, which is among the early attempts of leveraging deep neural networks into quantization-based cross-modal similarity search.
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification
Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification.
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval
Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics
In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge.
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification
The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel active learning strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross-dataset and intra-image; 3) the learned deep joint spectral-spatial feature representation is more generic and robust than many joint spectral-spatial feature representation.
Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval
Previous works mostly focus on a generative approach that takes a highly abstract and sparse sketch as input and then synthesizes the corresponding natural image.
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification
Two-stream architecture have shown strong performance in video classification task.
Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval.
Fully-Featured Attribute Transfer
Image attribute transfer aims to change an input image to a target one with expected attributes, which has received significant attention in recent years.
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speedup the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries.
Towards Visual Feature Translation
In this paper, we make the first attempt towards visual feature translation to break through the barrier of using features across different visual search systems.
Bilevel Distance Metric Learning for Robust Image Recognition
Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems.
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other.
Ranked #2 on
Person Re-Identification
on CUHK03-C
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
Asynchronous parallel stochastic gradient optimization has been playing a pivotal role to solve large-scale machine learning problems in big data applications.
Unsupervised Deep Generative Adversarial Hashing Network
HashGAN consists of three networks, a generator, a discriminator and an encoder.
Direct Shape Regression Networks for End-to-End Face Alignment
In this paper, we propose the direct shape regression network (DSRN) for end-to-end face alignment by jointly handling the aforementioned challenges in a unified framework.
Ranked #13 on
Face Alignment
on AFLW-19
CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation
In principle, CerfGAN contains a novel component, i. e., a multi-class discriminator (MCD), which gives the model an extremely powerful ability to match multiple translation mappings.
Semantic Structure-based Unsupervised Deep Hashing
Hashing is becoming increasingly popular for approximate nearest neighbor searching in massive databases due to its storage and search efficiency.
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations.
Learning A Structured Optimal Bipartite Graph for Co-Clustering
In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples.
Group Sparse Additive Machine
Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments.
Ranked #1 on
Image Clustering
on FRGC
Multilinear Hyperplane Hashing
Despite its successful progress in classic point-to-point search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up in many applications like active learning with SVMs.
Multi-View Complementary Hash Tables for Nearest Neighbor Search
have enjoyed the benefits of complementary hash tables and information fusion over multiple views.
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.
