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Found 241 papers, 103 papers with code
Impact of Preference Noise on the Alignment Performance of Generative Language Models
A key requirement in developing Generative Language Models (GLMs) is to have their values aligned with human values.
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
To fully utilize the information within the intermediate domain, we propose a symmetric Guidance training strategy (SymGD), which additionally offers direct guidance to unlabeled data by merging pseudo labels from intermediate samples.
ONNXPruner: ONNX-Based General Model Pruning Adapter
However, the practical application of these algorithms across various models and platforms remains a significant challenge.
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due to limited resolution.
Ranked #11 on
Visual Question Answering
on MM-Vet
Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data
Reinforcement Learning from Human Feedback (RLHF) is a popular method for aligning Language Models (LM) with human values and preferences.
Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation
However, these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets, reducing class discrimination, and an exclusive focus on mean feature consistency, lacking precision and comprehensiveness.
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)
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks.
SETA: Semantic-Aware Token Augmentation for Domain Generalization
In this paper, we study the impact of prior CNN-based augmentation methods on token-based models, revealing their performance is suboptimal due to the lack of incentivizing the model to learn holistic shape information.
Concatenate, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation
Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations.
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs.
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning
This paper further investigates the necessity of disentangled representation in downstream applications.
Can Transformers Capture Spatial Relations between Objects?
Spatial relationships between objects represent key scene information for humans to understand and interact with the world.
Data-freeWeight Compress and Denoise for Large Language Models
Significantly, our method is characterized by without necessitating additional involvement of any corpus, while simultaneously preserving orthogonality in conjunction with pruning and quantization methods.
Distributionally Robust Graph-based Recommendation System
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios
Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task.
InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-form text-image composition and comprehension.
Ranked #16 on
Visual Question Answering
on MM-Vet
General Flow as Foundation Affordance for Scalable Robot Learning
Our predicted flow offers actionable geometric and physics guidance, thus facilitating stable zero-shot skill transfer in real-world scenarios. We deploy our method with a policy based on closed-loop flow prediction.
Identifying and Analyzing Task-Encoding Tokens in Large Language Models
In-context learning (ICL) has become an effective solution for few-shot learning in natural language processing.
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations
2) From the feature perspective, the simple Tail Interaction module implicitly enhances potential correlations among all samples from all source domains, facilitating the acquisition of domain-invariant representations across multiple domains for the model.
Any-point Trajectory Modeling for Policy Learning
Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning.
PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance
Based on this technique, we assign appropriate training weights to unlabeled data to enhance the construction of a discriminative latent space.
Social-Transmotion: Promptable Human Trajectory Prediction
We translate the idea of a prompt from Natural Language Processing (NLP) to the task of human trajectory prediction, where a prompt can be a sequence of x-y coordinates on the ground, bounding boxes in the image plane, or body pose keypoints in either 2D or 3D.
Heterogeneous Graph Neural Architecture Search with GPT-4
The basic idea of GHGNAS is to design a set of prompts that can guide GPT-4 toward the task of generating new heterogeneous graph neural architectures.
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue
Knowledge-grounded dialogue is a task of generating an informative response based on both the dialogue history and external knowledge source.
Digital Life Project: Autonomous 3D Characters with Social Intelligence
In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulating life in a digital environment.
Ranked #2 on
Motion Synthesis
on InterHuman
Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
In this study, we are interested in imbuing robots with the capability of physically-grounded task planning.
TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer
In summary, we present the TSST task, a new benchmark for style transfer and emphasizing human-oriented evaluation, exploring and advancing the performance of current LLMs.
Plug-and-Play Latent Feature Editing for Orientation-Adaptive Quantitative Susceptibility Mapping Neural Networks
The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset.
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
Owning to the alignment of objectives in two phases, the RL agent can transfer between offline and online learning seamlessly.
JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds
To address this, we introduce a novel dataset for end-to-end trajectory forecasting, facilitating the evaluation of models in scenarios involving less-than-ideal preceding modules such as tracking.
The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics
Specifically, we propose a novel competition setting in which we select a list of allowed LLMs and disallow fine-tuning to ensure a focus on prompting.
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence.
DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands
Achieving human-like dexterous manipulation remains a crucial area of research in robotics.
Imitation Learning from Observation with Automatic Discount Scheduling
Nonetheless, we identify that tasks characterized by a progress dependency property pose significant challenges for such approaches; in these tasks, the agent needs to initially learn the expert's preceding behaviors before mastering the subsequent ones.
Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value
Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value.
Foundation Reinforcement Learning: towards Embodied Generalist Agents with Foundation Prior Assistance
Our method tolerates the unavoidable noise in embodied foundation models.
Graph Neural Architecture Search with GPT-4
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture
Heuristically, we extend the usual notion of action to a dual Cognition (high-level)-Action (low-level) architecture by introducing intuitive human cognitive priors, and propose a novel skill IL framework through human-robot interaction, called Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent to effectively cognize and imitate the critical skills from raw visual demonstrations.
OpenMSD: Towards Multilingual Scientific Documents Similarity Measurement
We propose the first multilingual scientific documents dataset, Open-access Multilingual Scientific Documents (OpenMSD), which has 74M papers in 103 languages and 778M citation pairs.
Exploring Flat Minima for Domain Generalization with Large Learning Rates
Instead, we observe that leveraging a large learning rate can simultaneously promote weight diversity and facilitate the identification of flat regions in the loss landscape.
A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness
Although having attributed it to training dynamics, existing theoretical explanations of activation sparsity are restricted to shallow networks, small training steps and special training, despite its emergence in deep models standardly trained for a large number of steps.
InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules
By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations.
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain.
Efficient Last-iterate Convergence Algorithms in Solving Games
Recent works propose a Reward Transformation (RT) framework for MWU, which removes the uniqueness condition and achieves competitive performance with OMWU.
DomainAdaptor: A Novel Approach to Test-time Adaptation
To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test.
Quantitative Susceptibility Mapping through Model-based Deep Image Prior (MoDIP)
The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects.
Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs
Neural Radiance Fields from Sparse input} (NeRF-S) have shown great potential in synthesizing novel views with a limited number of observed viewpoints.
3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks
Our experimental results on the MMWHS dataset demonstrate that our method outperforms the state-of-the-art (SOTA) semi-supervised segmentation methods.
DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering
Realistic human-centric rendering plays a key role in both computer vision and computer graphics.
Policy Contrastive Imitation Learning
However, the performance of AIL is still unsatisfactory on the more challenging tasks.
Towards Explainable Evaluation Metrics for Machine Translation
In this context, we also discuss the latest state-of-the-art approaches to explainable metrics based on generative models such as ChatGPT and GPT4.
A Universal Semantic-Geometric Representation for Robotic Manipulation
To this end, we present $\textbf{Semantic-Geometric Representation} (\textbf{SGR})$, a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning.
Shades of meaning: Uncovering the geometry of ambiguous word representations through contextualised language models
Lexical ambiguity presents a profound and enduring challenge to the language sciences.
Programmatically Grounded, Compositionally Generalizable Robotic Manipulation
Robots operating in the real world require both rich manipulation skills as well as the ability to semantically reason about when to apply those skills.
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
Our study yields a series of intriguing results, including the discovery that the effectiveness of pre-training is highly dependent on the choice of the downstream policy learning algorithm.
Seer: Language Instructed Video Prediction with Latent Diffusion Models
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals.
Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation
Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks.
Real-time scheduling of renewable power systems through planning-based reinforcement learning
The well-trained scheduling agent significantly reduces renewable curtailment and load shedding, which are issues arising from traditional scheduling's reliance on inaccurate day-ahead forecasts.
Decision Transformer under Random Frame Dropping
Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world.
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization
In the early stage, our method loosens the practical constraints of unsafe transitions (adding extra safety budget) with the aid of a new metric we propose.
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.
Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction.
Ranked #40 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
A Policy Optimization Method Towards Optimal-time Stability
In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization.
PanoViT: Vision Transformer for Room Layout Estimation from a Single Panoramic Image
In this paper, we propose PanoViT, a panorama vision transformer to estimate the room layout from a single panoramic image.
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner.
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix
In this study, we present a new unified contrastive learning representation framework (named UniCLR) suitable for all the above four kinds of methods from a novel perspective of basic affinity matrix.
Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping
We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0. 6 mm isotropic at the finest.
Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions
This paper proposes the Virtual MCTS (V-MCTS), a variant of MCTS that spends more search time on harder states and less search time on simpler states adaptively.
Planning for Sample Efficient Imitation Learning
Inspired by the recent success of EfficientZero in RL, we propose EfficientImitate (EI), a planning-based imitation learning method that can achieve high in-environment sample efficiency and performance simultaneously.
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient
Empirically, we evaluate MAPPG on the well-known matrix game and differential game, and verify that MAPPG can converge to the global optimum for both discrete and continuous action spaces.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery
Specifically, we propose an inter-class sKLD constraint to effectively exploit the disjoint relationship between labelled and unlabelled classes, enforcing the separability for different classes in the embedding space.
Predictive Inference with Feature Conformal Prediction
Conformal prediction is a distribution-free technique for establishing valid prediction intervals.
PLN: Parasitic-Like Network for Barely Supervised Medical Image Segmentation
In this paper, by introducing an extremely sparse annotation way of labeling only one slice per 3D image, we investigate a novel barely-supervised segmentation setting with only a few sparsely-labeled images along with a large amount of unlabeled images.
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation
Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance?
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network architecture makes decisions in an opaque manner that hinders humans from understanding the learned knowledge and how input observations influence decisions.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Semantic-Aware Fine-Grained Correspondence
Establishing visual correspondence across images is a challenging and essential task.
Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction
This is especially the case with image observations, where a single image only includes one view of the scene, and it suffers from a lack of motion information and object occlusions.
EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer
Most existing methods view makeup transfer as transferring color distributions of different facial regions and ignore details such as eye shadows and blushes.
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Across applications spanning supervised classification and sequential control, deep learning has been reported to find "shortcut" solutions that fail catastrophically under minor changes in the data distribution.
Auto-Encoding Adversarial Imitation Learning
In this work, we propose Auto-Encoding Adversarial Imitation Learning (AEAIL), a robust and scalable AIL framework.
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
In this paper, we examine negative-free contrastive learning methods to study the disentanglement property empirically.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
$G^2$: Enhance Knowledge Grounded Dialogue via Ground Graph
Besides, a Ground Graph Aware Transformer ($G^2AT$) is proposed to enhance knowledge grounded response generation.
On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice
Lately, there has been a global effort by multiple research groups to detect COVID-19 from voice.
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization
Few-shot abstractive summarization has become a challenging task in natural language generation.
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources
The BFRnet method is compared with three conventional BFR methods and one previous deep learning method using simulated and in vivo brains from 4 healthy and 2 hemorrhagic subjects.
DePA: Improving Non-autoregressive Machine Translation with Dependency-Aware Decoder
We propose a novel and general Dependency-Aware Decoder (DePA) to enhance target dependency modeling in the decoder of fully NAT models from two perspectives: decoder self-attention and decoder input.
MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization
The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples.
Playing Lottery Tickets in Style Transfer Models
(2) Using iterative magnitude pruning, we find the matching subnetworks at 89. 2% sparsity in AdaIN and 73. 7% sparsity in SANet, which demonstrates that style transfer models can play lottery tickets too.
Towards Explainable Evaluation Metrics for Natural Language Generation
We also provide a synthesizing overview over recent approaches for explainable machine translation metrics and discuss how they relate to those goals and properties.
TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing
Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD.
Ask to Understand: Question Generation for Multi-hop Question Answering
Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance.
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation
Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO.
Generalized Bandit Regret Minimizer Framework in Imperfect Information Extensive-Form Game
In this paper, we propose a generalized framework for this learning setting.
Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation
Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees for policies.
Transformers in Medical Image Analysis: A Review
Transformers have dominated the field of natural language processing, and recently impacted the computer vision area.
Online Attentive Kernel-Based Temporal Difference Learning
Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA.
MVDG: A Unified Multi-view Framework for Domain Generalization
Beyond the training stage, overfitting could also cause unstable prediction in the test stage.
Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data.
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Particularly, the proposed method can generate a variety of data variants to better deal with the overfitting issue.
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.
Mastering Atari Games with Limited Data
Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal.
Ranked #2 on
Atari Games 100k
on Atari 100k
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Permutations then serve as target generation orders for training an insertion-based Transformer language model.
NAS-FCOS: Efficient Search for Object Detection Architectures
Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures.
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.
Unifying Cross-lingual Summarization and Machine Translation with Compression Rate
Through introducing compression rate, the information ratio between the source and the target text, we regard the MT task as a special CLS task with a compression rate of 100%.
Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization
Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process.
The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Results
In this paper, we introduce the Eval4NLP-2021shared task on explainable quality estimation.
Disentangling Properties of Contrastive Methods
In this paper, we explored the possibility of using contrastive methods to learn a disentangled representation, a discriminative approach that is drastically different from previous approaches.
Auto-Encoding Inverse Reinforcement Learning
Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function.
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts
When operating in partially observed settings, it is important for a control policy to fuse information from a history of observations.
Attention-based Interpretation and Response to The Trade-Off of Adversarial Training
To boost the robustness of a model against adversarial examples, adversarial training has been regarded as a benchmark method.
To be Closer: Learning to Link up Aspects with Opinions
Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
LibFewShot: A Comprehensive Library for Few-shot Learning
Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks.
Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies
Health professional regulators aim to protect the health and well-being of patients and the public by setting standards for scrutinising and overseeing the training and conduct of health and care professionals.
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net.
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
However, in small data regimes, we can not obtain a sufficient number of negative pairs or effectively avoid the over-fitting problem when negatives are not used at all.
Differentiable Architecture Pruning for Transfer Learning
We propose a new gradient-based approach for extracting sub-architectures from a given large model.
Keyframe-Focused Visual Imitation Learning
Imitation learning trains control policies by mimicking pre-recorded expert demonstrations.
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student.
Feature-based Style Randomization for Domain Generalization
As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target domain without any additional adaption.
Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio
An ASR model that operates on both primary and auxiliary data can achieve better accuracy compared to a primary-only solution; and a model that can serve both primary-only (PO) and primary-plus-auxiliary (PPA) modes is highly desirable.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Prediction or Comparison: Toward Interpretable Qualitative Reasoning
Qualitative relationships illustrate how changing one property (e. g., moving velocity) affects another (e. g., kinetic energy) and constitutes a considerable portion of textual knowledge.
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Method: A recently proposed deep learning-based QSM method, namely xQSM, is investigated to assess the accuracy of dipole inversion on reduced brain coverages.
Cross-Lingual Abstractive Summarization with Limited Parallel Resources
Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the cross-lingual summarization (CLS) task.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+1
Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments
Terrain traversability analysis plays a major role in ensuring safe robotic navigation in unstructured environments.
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network.
Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition
The end-to-end 2D Conv-Attention model is compared with a multi-head self-attention and superdirective-based neural beamformers.
Adapting by Pruning: A Case Study on BERT
Adapting pre-trained neural models to downstream tasks has become the standard practice for obtaining high-quality models.
Local descriptor-based multi-prototype network for few-shot Learning
Prototype-based few-shot learning methods are promising in that they are simple yet effective to handle any-shot problems, and many prototype associated works are raised since then.
CAT: Cross-Attention Transformer for One-Shot Object Detection
Given a query patch from a novel class, one-shot object detection aims to detect all instances of that class in a target image through the semantic similarity comparison.
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments
Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-board power.
Towards Self-Adaptive Metric Learning On the Fly
In this paper, we present a new online metric learning framework that attempts to tackle the challenge by learning an ANN-based metric with adaptive model complexity from a stream of constraints.
SetConv: A New Approach for Learning from Imbalanced Data
For many real-world classification problems, e. g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high.
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
In this paper, we propose an end-to-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA).
Ranked #6 on
Semantic Segmentation
on DensePASS
Mining Latent Classes for Few-shot Segmentation
Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.
Ranked #41 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information.
Accelerating Quantitative Susceptibility Mapping using Compressed Sensing and Deep Neural Network
In this study, a learning-based Deep Complex Residual Network (DCRNet) is proposed to recover both the magnitude and phase images from incoherently undersampled data, enabling high acceleration of QSM acquisition.
Mutual Information State Intrinsic Control
Reinforcement learning has been shown to be highly successful at many challenging tasks.
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning
To address this, we propose a novel self-supervised learning task, which we named Trajectory Contrastive Learning (TCL), to improve meta-training.
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world.
Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation
These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images.
Reinforcement Learning with Latent Flow
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
LoFGAN: Fusing Local Representations for Few-Shot Image Generation
Given only a few available images for a novel unseen category, few-shot image generation aims to generate more data for this category.
Discovering Autoregressive Orderings with Variational Inference
One strategy to recover this information is to decode both the content and location of tokens.
Discrete Predictive Representation for Long-horizon Planning
Discrete representations have been key in enabling robots to plan at more abstract levels and solve temporally-extended tasks more efficiently for decades.
Maximizing absorption in photon trapping ultra-fast silicon photodetectors
Photon trapping structures address this trade-off by enhancing the light-matter interactions, but maximizing their performance remains a challenge due to a multitude of factors influencing their design and fabrication.
Optics Applied Physics
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Imitation learning trains policies to map from input observations to the actions that an expert would choose.
CariMe: Unpaired Caricature Generation with Multiple Exaggerations
Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution for exaggerations in caricatures.
Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification
To find the optimal weight-agnostic network, we use a novel and computationally efficient method that translates the hard architecture-search problem into a feasible optimization problem. More specifically, we look at the optimal task-specific architectures as the optimal configuration of binary networks with {0, 1}-valued weights, which can be found through an approximate gradient descent strategy.
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation
They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains.
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
The implementation of the proposed method is available at https://github. com/KeleiHe/DAAN.
HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation
In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate.
Manifold Alignment for Semantically Aligned Style Transfer
In this paper, we make a new assumption that image features from the same semantic region form a manifold and an image with multiple semantic regions follows a multi-manifold distribution.
MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling
Therefore, the proposed network has a dual-branch architecture that tackles two tasks: 1) a segmentation sub-network aiming to generate the prostate segmentation, and 2) a voxel-metric learning sub-network aiming to improve the quality of the learned feature space supervised by a metric loss.
Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice).
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18-39%.
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation
We systematically investigate a range of metrics based on state-of-the-art cross-lingual semantic representations obtained with pretrained M-BERT and LASER.
xQSM-Quantitative Susceptibility Mapping with Octave Convolutional Neural Networks
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications.
Image and Video Processing
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Importantly, we highlight the value and importance of the distribution diversity in the augmentation-based pretext few-shot tasks, which can effectively alleviate the overfitting problem and make the few-shot model learn more robust feature representations.
Data Augmentation
Unsupervised Few-Shot Image Classification
+1
Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation
Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients.
Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization
Semantic segmentation in a supervised learning manner has achieved significant progress in recent years.
Deep Learning on Knowledge Graph for Recommender System: A Survey
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS).
Phylogenetic Study of 2019-nCoV by Using Alignment Free Method (Evolutionary Bifurcation of Novel Coronavirus Mutants)
It is found that there exist three types of virus mutations, namely, the mutation among sub-branches of the same branch, the off-root mutation and the root-oriented mutation between large branches of the tree.
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation
Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance for medical image segmentation.
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal.
Asymmetric Distribution Measure for Few-shot Learning
Given the natural asymmetric relation between a query image and a support class, we argue that an asymmetric measure is more suitable for metric-based few-shot learning.
MW-GAN: Multi-Warping GAN for Caricature Generation with Multi-Style Geometric Exaggeration
Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo.
Multi-Agent Game Abstraction via Graph Attention Neural Network
Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents.
Differentiable Meta-learning Model for Few-shot Semantic Segmentation
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm.
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs.
Defensive Few-shot Learning
(2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting?
Visual cryptography in single-pixel imaging
The secret image can be recovered when identical illumination patterns are projected onto multiple visual key images and a single detector is used to record the total light intensities.
Does deep learning always outperform simple linear regression in optical imaging?
Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated.
Data hiding in complex-amplitude modulation using a digital micromirror device
A digital micromirror device (DMD) is an amplitude-type spatial light modulator.
Automatic Data Augmentation by Learning the Deterministic Policy
By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model.
Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation
The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards.
GUIDEGAN: ATTENTION BASED SPATIAL GUIDANCE FOR IMAGE-TO-IMAGE TRANSLATION
To tackle this problem, we propose a GuideGAN based on attention mechanism.
Generative Adversarial Network
Image-to-Image Translation
+1
Scoring-Aggregating-Planning: Learning task-agnostic priors from interactions and sparse rewards for zero-shot generalization
In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition.
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents.
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
A robust evaluation metric has a profound impact on the development of text generation systems.
Better Rewards Yield Better Summaries: Learning to Summarise Without References
Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings.
Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification
In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels.
GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification
Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch.
Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification
To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace.
Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation
The predominant RL paradigm for summarisation learns a cross-input policy, which requires considerable time, data and parameter tuning due to the huge search spaces and the delayed rewards.
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
On-orbit proximity operations in space rendezvous, docking and debris removal require precise and robust 6D pose estimation under a wide range of lighting conditions and against highly textured background, i. e., the Earth.
NAS-FCOS: Fast Neural Architecture Search for Object Detection
The success of deep neural networks relies on significant architecture engineering.
Ranked #124 on
Object Detection
on COCO test-dev
Preference-based Interactive Multi-Document Summarisation
Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound.
Known-plaintext attack and ciphertext-only attack for encrypted single-pixel imaging
In many previous works, a single-pixel imaging (SPI) system is constructed as an optical image encryption system.
Optical machine learning with incoherent light and a single-pixel detector
Like an optical computer, the system can perform machine learning tasks such as number digit recognition in an all-optical manner.
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.
Ranked #13 on
Node Classification
on PPI
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Conducting a manual evaluation is considered an essential part of summary evaluation methodology.
Thinkey: A Scalable Blockchain Architecture
This paper presents Thinkey, an efficient, secure, infinitely scalable and decentralized blockchain architecture.
Cryptography and Security
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification
For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains.
Ranked #19 on
Unsupervised Domain Adaptation
on Market to Duke
Risk Averse Robust Adversarial Reinforcement Learning
In this paper we introduce risk-averse robust adversarial reinforcement learning (RARARL), using a risk-averse protagonist and a risk-seeking adversary.
