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Found 67 papers, 25 papers with code
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training
Recent advancements in vision-language pre-training via contrastive learning have significantly improved performance across computer vision tasks.
Neural Implicit Representation for Highly Dynamic LiDAR Mapping and Odometry
By identifying and excluding dynamic elements from the mapping process, this segmentation enables the creation of a dense 3D map that accurately represents the static background only.
3D Scene Reconstruction
Simultaneous Localization and Mapping
3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities
3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations.
PORCA: Root Cause Analysis with Partially Observed Data
In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data.
STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis
The rapid evolution of large language models (LLMs) holds promise for reforming the methodology of spatio-temporal data mining.
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns.
Zero-Shot Video Editing through Adaptive Sliding Score Distillation
To address these challenges, this study proposes a novel paradigm of video-based score distillation, facilitating direct manipulation of original video content.
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
With the evolving of time, the types of anomaly edges are emerging and the labeled anomaly samples are few for each type.
IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel Relation
The scarcity of labeled data in real-world scenarios is a critical bottleneck of deep learning's effectiveness.
Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
Finally, by interleaving pseudo samples with real ones of the new task, we continually update the state and behavior generators to model progressively diverse behaviors, and regularize the multi-head critic via behavior cloning to mitigate forgetting.
ONNXPruner: ONNX-Based General Model Pruning Adapter
However, the practical application of these algorithms across various models and platforms remains a significant challenge.
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.
DIO: Dataset of 3D Mesh Models of Indoor Objects for Robotics and Computer Vision Applications
These images were processed using a photogrammetry software known as Meshroom to generate a dense surface reconstruction of the scene.
AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis
This paper introduces the idea of AccidentGPT, a foundation model of traffic accident analysis, which incorporates multi-modal input data to automatically reconstruct the accident process video with dynamics details, and furthermore provide multi-task analysis with multi-modal outputs.
A multi-layer refined network model for the identification of essential proteins
In order to improve the accuracy of the identification of essential proteins, researchers attempted to obtain a refined PIN by combining multiple biological information to filter out some unreliable interactions in the PIN.
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.
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.
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.
Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks
Air Quality Monitoring and Forecasting has been a popular research topic in recent years.
Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides.
Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models.
Causal Discovery from Temporal Data: An Overview and New Perspectives
Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery.
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning
OOD exacerbates inconsistencies in magnitudes and directions of task gradients, which brings challenges for GBML to optimize the meta-knowledge by minimizing the sum of task gradients in each minibatch.
Visual Perception System for Autonomous Driving
The recent surge in interest in autonomous driving stems from its rapidly developing capacity to enhance safety, efficiency, and convenience.
Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning
Very recently, reinforcement learning has been proposed as a control scheme to maintain the balloon in the region of a fixed location, facilitated through diverse opposing wind-fields at different altitudes.
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
Nevertheless, edge-powered ML solutions are more complex to realize due to the joint constraints from both edge computing and AI domains, and the corresponding solutions are expected to be efficient and adapted in technologies such as data processing, model compression, distributed inference, and advanced learning paradigms for Edge ML requirements.
MAP: Towards Balanced Generalization of IID and OOD through Model-Agnostic Adapters
To achieve this goal, we apply a bilevel optimization to explicitly model and optimize the coupling relationship between the OOD model and auxiliary adapter layers.
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.
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
Deep object detection for waterbird monitoring using aerial imagery
In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone.
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.
Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation
Gathering data for RL is known to be a laborious task, and real-world experiments can be risky.
Dense RGB-D-Inertial SLAM with Map Deformations
While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised.
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
To solve this problem, we present a plug-in Hierarchical Tree Structure-aware (HTS) method, which not only learns the relationship of FSL and pretext tasks, but more importantly, can adaptively select and aggregate feature representations generated by pretext tasks to maximize the performance of FSL tasks.
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.
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.
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.
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.
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.
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.
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.
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning
That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via the optimization.
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.
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
Inspired by the recent success of Few-Shot Learning (FSL) in natural image classification, we propose to apply FSL to skin disease identification to address the extreme scarcity of training sample problem.
RGBD-Dog: Predicting Canine Pose from RGBD Sensors
We evaluate our model on both synthetic and real RGBD images and compare our results to previously published work fitting canine models to images.
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.
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.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.
Defensive Few-shot Learning
(2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting?
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
Its key difference from the literature is the replacement of the image-level feature based measure in the final layer by a local descriptor based image-to-class measure.
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene.
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning
Furthermore, an attention mechanism is introduced to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated.
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM).
A Multilayer Framework for Online Metric Learning
Furthermore, in a progressively and nonlinearly learning way, MLOML has a stronger learning ability than traditional online metric learning in the case of limited available training data.
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
We created a synthetic block stacking environment with physics simulation in which the agent can learn a policy end-to-end through trial and error.
Learn to Model Motion from Blurry Footages
It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects.
WebCaricature: a benchmark for caricature recognition
In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition.
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper.
Visual Stability Prediction and Its Application to Manipulation
We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers.
To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction
In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance.
Video Interpolation using Optical Flow and Laplacian Smoothness
Non-rigid video interpolation is a common computer vision task.
Nonrigid Optical Flow Ground Truth for Real-World Scenes with Time-Varying Shading Effects
In this paper we present a dense ground truth dataset of nonrigidly deforming real-world scenes.
Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences
We demonstrate the success of our approach by showing significant error reduction on 6 popular optical flow algorithms applied to a range of real-world nonrigid benchmarks.
Blur Robust Optical Flow using Motion Channel
It is hard to estimate optical flow given a realworld video sequence with camera shake and other motion blur.
Learning Multi-Scale Representations for Material Classification
The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors.
Optical Flow Estimation Using Laplacian Mesh Energy
In this paper we present a novel non-rigid optical flow algorithm for dense image correspondence and non-rigid registration.
