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Found 38 papers, 15 papers with code
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge
DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question.
Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework
Various temporal knowledge graph (KG) completion models have been proposed in the recent literature.
TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion.
StyleBooth: Image Style Editing with Multimodal Instruction
We integrate encoded textual instruction and image exemplar as a unified condition for diffusion model, enabling the editing of original image following multimodal instructions.
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs.
Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance
The process involves (i) Locate: concatenating the noise with masked scene image to achieve precise regional editing, (ii) Assign: employing decoupled cross-attention mechanism to accommodate multi-modal guidance, and (iii) Refine: using a novel RefineNet to supplement subject details.
Stop Reasoning! When Multimodal LLMs with Chain-of-Thought Reasoning Meets Adversarial Images
Our research evaluates the adversarial robustness of MLLMs when employing CoT reasoning, finding that CoT marginally improves adversarial robustness against existing attack methods.
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis.
Understanding and Improving In-Context Learning on Vision-language Models
Our findings indicate that ICL in VLMs is predominantly driven by the textual information in the demonstrations whereas the visual information in the demonstrations barely affects the ICL performance.
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
The proposed dataset is designed to evaluate graph-language models' ability to understand graphs and make use of it for answer generation.
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models (e. g. Flamingo), image-text matching models (e. g.
Logic Diffusion for Knowledge Graph Reasoning
Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions.
A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering
Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices.
AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation
We evaluate our approach on the photographic ancient character datasets, e. g., OBC306 and CSDD.
Modeling the evolution of temporal knowledge graphs with uncertainty
Forecasting future events is a fundamental challenge for temporal knowledge graphs (tKG).
Mutimodal Ranking Optimization for Heterogeneous Face Re-identification
Secondly, we propose linear and non-linear fusion strategies to aggregate initial ranking lists of multimodal face pairs and acquire the optimized re-ranked list based on modal complementarity.
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities.
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
Deepfake Face Traceability with Disentangling Reversing Network
Deepfake face not only violates the privacy of personal identity, but also confuses the public and causes huge social harm.
Continuous Temporal Graph Networks for Event-Based Graph Data
Hence, we propose Continuous Temporal Graph Networks (CTGNs) to capture the continuous dynamics of temporal graph data.
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
In this paper, we follow the previous work that focuses on few-shot relational learning on static KGs and extend two fundamental TKG reasoning tasks, i. e., interpolated and extrapolated link prediction, to the one-shot setting.
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts.
Consistent Style Transfer
Recently, attentional arbitrary style transfer methods have been proposed to achieve fine-grained results, which manipulates the point-wise similarity between content and style features for stylization.
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting
Compared with the completion task, the forecasting task is more difficult that faces two main challenges: (1) how to effectively model the time information to handle future timestamps?
Video Similarity and Alignment Learning on Partial Video Copy Detection
To address the above issues, we propose the Video Similarity and Alignment Learning (VSAL) approach, which jointly models spatial similarity, temporal similarity and partial alignment.
When Face Recognition Meets Occlusion: A New Benchmark
In particular, we first collect a variety of glasses and masks as occlusion, and randomly combine the occlusion attributes (occlusion objects, textures, and colors) to achieve a large number of more realistic occlusion types.
Temporal Knowledge Graph Forecasting with Neural ODE
In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i. e., edge formation and dissolution.
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs
We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task.
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task.
DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion
Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs.
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs
The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.
MMD GAN with Random-Forest Kernels
In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.
Unsupervised Image Super-Resolution with an Indirect Supervised Path
The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image.
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer.
Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding
In addition, we propose Gradient Band-based Adversarial Training, which trained with a single randomly choose dominant adversarial example without taking any modification, to realize the "1:N" attack immunity for generalized dominant adversarial examples.
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
Face Analysis Project on MXNet
Ranked #5 on
Lightweight Face Recognition
on AgeDB-30
Dynamic Stacked Generalization for Node Classification on Networks
We propose a novel stacked generalization (stacking) method as a dynamic ensemble technique using a pool of heterogeneous classifiers for node label classification on networks.
