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Found 61 papers, 21 papers with code
Exposure: A White-Box Photo Post-Processing Framework
Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well.
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
To achieve faster networks, we revisit popular operators and demonstrate that such low FLOPS is mainly due to frequent memory access of the operators, especially the depthwise convolution.
CameraCtrl: Enabling Camera Control for Text-to-Video Generation
Controllability plays a crucial role in video generation since it allows users to create desired content.
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
Disentangled Makeup Transfer with Generative Adversarial Network
Facial makeup transfer is a widely-used technology that aims to transfer the makeup style from a reference face image to a non-makeup face.
Continuously Indexed Domain Adaptation
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
Ranked #1 on
Domain Adaptation
on Rotating MNIST
Continuously Index Domain Adaptation
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
Enhanced Boundary Learning for Glass-like Object Segmentation
We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours.
Ranked #20 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
BoundarySqueeze: Image Segmentation as Boundary Squeezing
This module is used to squeeze the object boundary from both inner and outer directions, which contributes to precise mask representation.
Graph-Relational Domain Adaptation
In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e. g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure.
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, and then propose an adversarial variational Bayesian framework that infers domain indices from multi-domain data, thereby providing additional insight on domain relations and improving domain adaptation performance.
Controlling Directions Orthogonal to a Classifier
We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer.
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN).
Ranked #22 on
Image Generation
on STL-10
Trust Region-Guided Proximal Policy Optimization
We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well.
Truly Proximal Policy Optimization
Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging tasks.
Improving Video Instance Segmentation via Temporal Pyramid Routing
To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames.
Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
We propose Contrastive Poisoning (CP), the first effective such attack on CL.
Randomized algorithms for precise measurement of differentially-private, personalized recommendations
Personalized recommendations form an important part of today's internet ecosystem, helping artists and creators to reach interested users, and helping users to discover new and engaging content.
Taxonomy-Structured Domain Adaptation
Domain adaptation aims to mitigate distribution shifts among different domains.
Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent
Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales.
Action detection using a neural network elucidates the genetics of mouse grooming behavior
Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions.
From Bayesian Sparsity to Gated Recurrent Nets
The iterations of many first-order algorithms, when applied to minimizing common regularized regression functions, often resemble neural network layers with pre-specified weights.
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
We consider the problem of inferring the values of an arbitrary set of variables (e. g., risk of diseases) given other observed variables (e. g., symptoms and diagnosed diseases) and high-dimensional signals (e. g., MRI images or EEG).
Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations
In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violates the assumption of many current methods developed for this.
Road-network-based Rapid Geolocalization
We test all the candidate matching tuples under a hypothesise-and-test framework to search for the best match.
RF-Based Fall Monitoring Using Convolutional Neural Networks
Falls are the top reason for fatal and non-fatal injuries among seniors.
Ranked #2 on
RF-based Pose Estimation
on RF-MMD
Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
Uncertainty estimation is an essential step in the evaluation of the robustness for deep learning models in computer vision, especially when applied in risk-sensitive areas.
Learning Compositional Koopman Operators for Model-Based Control
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.
UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving
In contrast to existing methods which heavily rely on recurrent neural network for temporal context and hand-crafted structure for spatial context, our method could automatically partition the spatio-temporal space to adapt the data.
Bid Shading by Win-Rate Estimation and Surplus Maximization
We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.
Learning Blood Oxygen from Respiration Signals
Further, since breathing can be monitored without body contact by analyzing the radio signal in the environment, we show that oxygen too can be monitored without any wearable devices.
Exploring Logically Dependent Multi-task Learning with Causal Inference
In the field of causal inference, GS in our model is essentially a counterfactual reasoning process, trying to estimate the causal effect between tasks and utilize it to improve MTL.
Modeling Content Importance for Summarization with Pre-trained Language Models
Previous work is mostly based on statistical methods that estimate word-level salience, which does not consider semantics and larger context when quantifying importance.
Addressing Feature Suppression in Unsupervised Visual Representations
However, contrastive learning is susceptible to feature suppression, i. e., it may discard important information relevant to the task of interest, and learn irrelevant features.
Dependent Multi-Task Learning with Causal Intervention for Image Captioning
The intermediate task would help the model better understand the visual features and thus alleviate the content inconsistency problem.
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.
Mid-flight Forecasting for CPA Lines in Online Advertising
For Verizon MediaDemand Side Platform(DSP), forecasting of ad campaign performance not only feeds key information to the optimization server to allow the system to operate on a high-performance mode, but also produces actionable insights to the advertisers.
End-to-End Conversational Search for Online Shopping with Utterance Transfer
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation
The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text.
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation
In this paper, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic latent variable be semantic-free.
Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI
Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area.
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party
In most VFL frameworks, to protect the security and privacy of the participants' local data, a third party is needed to generate homomorphic encryption key pairs and perform decryption operations.
Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach
The human brain's white matter (WM) structure is of immense interest to the scientific community.
Domain Adaptation with Factorizable Joint Shift
In this paper, we propose a new assumption, Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in covariates and labels.
FedDAR: Federated Domain-Aware Representation Learning
To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients.
1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)
Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe.
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.
In this paper, we propose a systematic method to diagnose the correlations between an NLU dataset and a specific skill, and then take a fundamental reasoning skill, logical reasoning, as an example for analysis.
Contactless Oxygen Monitoring with Gated Transformer
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead.
Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution
Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data.
MaxGNR: A Dynamic Weight Strategy via Maximizing Gradient-to-Noise Ratio for Multi-Task Learning
A series of studies point out that too much gradient noise would lead to performance degradation in STL, however, in the MTL scenario, Inter-Task Gradient Noise (ITGN) is an additional source of gradient noise for each task, which can also affect the optimization process.
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough
Counterfactually-Augmented Data (CAD) has the potential to improve language models' Out-Of-Distribution (OOD) generalization capability, as CAD induces language models to exploit causal features and exclude spurious correlations.
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets
In this paper, we propose a principled approach that supports learning from heterogeneous datasets with different label sets.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization
Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization capability of language models, as CAD induces language models to exploit domain-independent causal features and exclude spurious correlations.
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality
In this study, we attribute the bias to the model's misuse of label dependency, i. e., the model tends to utilize the correlation shortcut in label dependency rather than fusing text information and label dependency for prediction.
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding
From the perspective of CoT, CoTT's two-step framework enables MLMs to implement task decomposition; CoTT's prompt tuning allows intermediate steps to be used in natural language form.
Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis
With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection
In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning.
