Search Results for author:
Found 42 papers, 18 papers with code
Memory-based Cross-modal Semantic Alignment Network for Radiology Report Generation
To ensure the semantic consistency of the retrieved cross modal prior knowledge, a cross-modal semantic alignment module (SAM) is proposed.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence.
Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning
Distributed machine learning enables parallel training of extensive datasets by delegating computing tasks across multiple workers.
Watermarking Vision-Language Pre-trained Models for Multi-modal Embedding as a Service
Our extensive experiments on various datasets indicate that the proposed watermarking approach is effective and safe for verifying the copyright of VLPs for multi-modal EaaS and robust against model extraction attacks.
VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models
Then the passive party, who owns only features of the sample, injects the blinding factor into the local embedding and sends it to the active party.
Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval
Different from Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute.
Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search
Conventional research mainly studies from the view of modeling the implicit correlations between images and texts for query-ads matching, ignoring the alignment of detailed product information and resulting in suboptimal search performance. In this work, we propose a simple alignment network for explicitly mapping fine-grained visual parts in ads images to the corresponding text, which leverages the co-occurrence structure consistency between vision and language spaces without requiring expensive labeled training data.
Ranked #1 on
Image-text matching
on CommercialAdsDataset
Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
Further, this result is applied to robust adaptive model predictive control with uncertainty sets updated by set membership.
Towards Fast and Accurate Image-Text Retrieval with Self-Supervised Fine-Grained Alignment
Image-text retrieval requires the system to bridge the heterogenous gap between vision and language for accurate retrieval while keeping the network lightweight-enough for efficient retrieval.
A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music
In recent years, AI-generated music has made significant progress, with several models performing well in multimodal and complex musical genres and scenes.
Online learning for robust voltage control under uncertain grid topology
In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online setting where there is uncertainty in both the network topology as well as load and generation variations.
Convolution-enhanced Evolving Attention Networks
To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps.
On Infinite-horizon System Level Synthesis Problems
System level synthesis is a promising approach that formulates structured optimal controller synthesis problems as convex problems.
Differentiable Bilevel Programming for Stackelberg Congestion Games
A Stackelberg congestion game (SCG) is a bilevel program in which a leader aims to maximize their own gain by anticipating and manipulating the equilibrium state at which followers settle by playing a congestion game.
Robust Online Voltage Control with an Unknown Grid Topology
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability.
MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering
Knowledge-based visual question answering requires the ability of associating external knowledge for open-ended cross-modal scene understanding.
Online Adversarial Stabilization of Unknown Networked Systems
We investigate the problem of stabilizing an unknown networked linear system under communication constraints and adversarial disturbances.
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification
In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data.
Coarse-to-Careful: Seeking Semantic-related Knowledge for Open-domain Commonsense Question Answering
It is prevalent to utilize external knowledge to help machine answer questions that need background commonsense, which faces a problem that unlimited knowledge will transmit noisy and misleading information.
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension
Most of the existing systems design a simple classifier to determine answerability implicitly without explicitly modeling mutual interaction and relation between the question and passage, leading to the poor performance for determining the unanswerable questions.
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.
One-photon Solutions to Multiqubit Multimode quantum Rabi model
General solutions to the quantum Rabi model involve subspaces with unbounded number of photons.
Quantum Physics Optics
Evolving Attention with Residual Convolutions
In this paper, we propose a novel and generic mechanism based on evolving attention to improve the performance of transformers.
Predictive Attention Transformer: Improving Transformer with Attention Map Prediction
Instead, we model their dependencies via a chain of prediction models that take previous attention maps as input to predict the attention maps of a new layer through convolutional neural networks.
Bi-directional CognitiveThinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
End-to-End Learning and Intervention in Games
In this paper, we provide a unified framework for learning and intervention in games.
Bi-directional Cognitive Thinking Network for Machine Reading Comprehension
We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory.
Localized and Distributed H2 State Feedback Control
Distributed linear control design is crucial for large-scale cyber-physical systems.
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model.
Ranked #2 on
Scene Graph Generation
on Visual Genome
(mean Recall @20 metric)
DARWIN: A Highly Flexible Platform for Imaging Research in Radiology
Our platform is consists of a radiomics module and a deep learning module.
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering
Finally, we perform graph neural networks to infer the global-optimal answer by jointly considering all the concepts.
KBGN: Knowledge-Bridge Graph Network for Adaptive Vision-Text Reasoning in Visual Dialogue
Visual dialogue is a challenging task that needs to extract implicit information from both visual (image) and textual (dialogue history) contexts.
DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation.
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering
In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features.
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue
More importantly, we can tell which modality (visual or semantic) has more contribution in answering the current question by visualizing the gate values.
Ranked #6 on
Visual Dialog
on VisDial v0.9 val
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering
Upon the constructed graph, we propose a Scene Graph Convolutional Network (SceneGCN) to jointly reason the object properties and relational semantics for the correct answer.
Dual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal
The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision.
Edge-Based Blur Kernel Estimation Using Sparse Representation and Self-Similarity
Blind image deconvolution is the problem of recovering the latent image from the only observed blurry image when the blur kernel is unknown.
Textual Relationship Modeling for Cross-Modal Information Retrieval
A joint neural model is proposed to learn feature representation individually in each modality.
Multimedia
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
A dual-path neural network model is proposed for couple feature learning in cross-modal information retrieval.
Correlation of obesity and osteoporosis: Effect of free fatty acids on bone marrow-derived mesenchymal stem cell differentiation
In mouse BMSCs, the Sox9 and Runx2 genes were significantly up-regulated after exposure to CM from FFA-treated adipocytes, while PPARpparγ and CEBP-α were significantly down-regulated.
