Paper introduces a semi-supervised method for image segmentation using a small set of labeled images.
problem Costly and time-consuming to create large labeled datasets for semantic segmentation.
method Uses a small set of fully labeled images and a weak set of only bounding box labeled images. Trains a primary model with an ancillary model generating initial labels and a self-correction module improving these labels.
result Models trained with a small fully supervised set perform similarly or better than those trained with a large fully supervised set, requiring significantly less annotation effort.
Paper presents attacks on real-time object detection systems.
problem Adversarial attacks on real-time object detection systems.
method Three targeted adversarial Objectness Gradient attacks (TOG).
result Adversarial attacks can cause object-vanishing, object-fabrication, and object-mislabeling.
A new method for faster data annotation using click-supervision and hierarchical object detection.
problem Data annotation bottleneck in modern data collection.
method Semi-automatic approach combining human and neural network for hierarchical object detection.
result Improved annotation speed and accuracy compared to current methods.
Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than …
In this paper, we will show an unprecedented method to accelerate training and improve performance, which called random gradient (RG). This method can be easier to the training of any model without extra calculation cost, we use Image classification, Semantic segmentation, and GANs to confirm this method can improve sp…
A new method integrates multi-label and multi-view features for image classification.
problem Combining multi-label and multi-view information for effective image classification.
method Introduces MV3MR, a method that exploits the complementary property of different features and discovers intrinsic local geometry. result MV3MR outperforms existing methods on PASCAL VOC' 07 and MIR Flickr datasets. Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision. This paper introduces a generic framework to train deep networks, end-to-end, with no supervision. We propose to fix a se…
Deep structured output learning shows great promise in tasks like semantic image segmentation. We proffer a new, efficient deep structured model learning scheme, in which we show how deep Convolutional Neural Networks (CNNs) can be used to estimate the messages in message passing inference for structured prediction wit…
In this paper, we present a technique for unsupervised learning of visual representations. Specifically, we train a model for foreground and background classification task, in the process of which it learns visual representations. Foreground and background patches for training come af- ter mining for such patches from …
We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence that in the case of natural images and sufficiently small translation invariance, this transform preserves most of the signal information nee…
We propose a new analytical approximation to the χ2 kernel that converges geometrically. The analytical approximation is derived with elementary methods and adapts to the input distribution for optimal convergence rate. Experiments show the new approximation leads to improved performance in image classification and …
We introduce a novel loss max-pooling concept for handling imbalanced training data distributions, applicable as alternative loss layer in the context of deep neural networks for semantic image segmentation. Most real-world semantic segmentation datasets exhibit long tail distributions with few object categories compri…
The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning …
Enhanced rotation prediction improves SSL models by capturing both shape and texture information.
problem Rotation prediction misses texture information, limiting model performance.
method Introduces image enhanced rotation prediction (IE-Rot) that combines rotation and image enhancement tasks.
result IE-Rot models outperform Rotation on various benchmarks.
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. However, the behavior of deep networks is yet to be fully understood and is still an active area of re…
Simple method improves deep classifier accuracy under noisy labels.
problem Training deep classifiers with noisy labels.
method Probabilistic approach using temperature parameterized softmax.
result Improves accuracy, log-likelihood and calibration on noisy datasets.
Learning with non-modular losses is an important problem when sets of predictions are made simultaneously. The main tools for constructing convex surrogate loss functions for set prediction are margin rescaling and slack rescaling. In this work, we show that these strategies lead to tight convex surrogates iff the unde…
Study of Pascal algebra matrices and their jet bundle map for vector bundles.
problem Defining and studying Pascal algebra matrices and their map on jet bundles.
method Identifying Pascal algebra matrices, showing generator well defines Pascal map, using it for intrinsic contact definition.
result Intrinsic definition of point-wise contact between Hermitian vector bundles using unitary equivalence of Pascal maps.
VTAB benchmarks diverse visual tasks to assess representation learning effectiveness.
problem Lack of a unified evaluation for general visual representations.
method Developed VTAB, a benchmark for diverse visual tasks, and evaluated many representation learning algorithms.
result VTAB revealed insights into the effectiveness of various representation learning methods.
Neural networks offer high-accuracy solutions to a range of problems, but are costly to run in production systems because of computational and memory requirements during a forward pass. Given a trained network, we propose a techique called Deep Learning Approximation to build a faster network in a tiny fraction of the …
In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To ta…
New method grows deep networks efficiently by dynamically pruning and growing layers.
problem Training deep networks is computationally expensive and inefficient.
method Structured continuous sparsification starting from a small seed architecture.
result 49.7% inference FLOPs and 47.4% training FLOPs savings with 75.2% top-1 accuracy.
Automated VOC classification using embedded machine learning.
problem Classifying volatile organic compounds (VOCs) from electronic nose data.
method Embedded machine learning algorithm on Arduino Uno for VOC detection.
result 99% classification accuracy achieved in embedded solution.
Meta-learning approach improves object detection on new classes.
problem Deterioration of object detection performance on old classes in incremental settings.
method Meta-learning to reshape model gradients for optimal task adaptation.
result Meta-learning approach outperforms state-of-the-art methods in incremental object detection.
Unified framework DDNs for multi-label classification, improving inference efficiency.
problem Efficient inference for multi-label classification with dependency networks.
method Combining dependency networks and deep learning, proposing novel inference schemes.
result Novel inference schemes outperform basic neural architectures and Markov networks.
Post-training quantization method using multiple low-precision points achieves higher precision for critical weights.
problem Discretizing pre-trained deep neural networks without re-training.
method Multipoint quantization with efficient greedy selection and adaptive point number.
result Outperforms state-of-the-art methods on ImageNet classification and PASCAL VOC object detection.
Fast object detection in JPEG images without decompression.
problem Efficient object detection in compressed JPEG images.
method Modified SSD with DCT coefficients input processing.
result 2x faster detection with promising performance.
Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two direc…
A new method for image annotation using multiview Hessian regularization.
problem Semi-supervised learning biases towards a constant function and is limited to single view data.
method Develops multiview Hessian regularization (mHR) to handle multiview data and improve generalization.
result mHR optimally combines multiple Hessian regularizations from different views to steer the classification function.
Spiking-YOLO improves object detection with low power and fast convergence.
problem Challenging object detection tasks with spiking neural networks.
method Channel-wise normalization and signed neuron with imbalanced threshold.
result Spiking-YOLO achieves comparable results to Tiny YOLO but with significantly less energy consumption.
A new framework for consistent segmentation evaluation reduces operating losses.
problem Inconsistent thresholding-based segmentation methods lead to suboptimal solutions.
method Developed a consistent ranking-based framework (RankDice/RankIoU) using Bayes rules and Dice-/IoU-calibration.
result The proposed framework is Dice-/IoU-calibrated and provides excess risk bounds and convergence rates.
We discuss eight new(?) configuration theorems of classical projective geometry in the spirit of the Pappus and Pascal theorems.
The paper characterizes pedal curves of quadratic curves.
problem Understanding pedal curves of quadratic curves.
method Analyzing the inverse construction of pedal curves.
result Characterization of pedal curves of quadratic curves.
A new method for multi-label image classification using multiple feature views.
problem Limited by single-view feature, traditional matrix completion struggles with multi-label image classification.
method Multi-View Matrix Completion (MVMC) framework, combining weighted MC outputs from different views, using cross-validation for weights.
result MVMC framework improves multi-label image classification by exploiting complementary properties of different features and consistent labels.
A limaçon-like curve, allowing 2π-transition with monotone curvature between concentric curvature elements, is presented. The curve is 4th degree algebraic, 4th degree rational, and shares other common features with Pascal's limaçon.
We use some basic properties of binomial and Stirling numbers to prove that the Euler characteristic is, essentially, the unique numerical topological invariant for compact polyhedra which can be expressed as a linear combination of the numbers of faces of triangulations. We obtain this result converting it into an eig…
Adaptive masked proxies improve few-shot segmentation efficiency.
problem Efficiently segmenting objects with limited labeled data in robotics.
method Constructs segmentation weights from few labelled samples using multi-resolution average pooling and masked embeddings.
result Outperforms state-of-the-art in few-shot semantic segmentation on PASCAL-5i.
Self-training outperforms pre-training on COCO object detection and segmentation datasets.
problem The effectiveness of pre-training in improving object detection and segmentation models is limited.
method Investigated self-training as an alternative method to utilize additional data.
result Self-training consistently improves model performance across various dataset sizes and data augmentation levels.
We construct a CW decomposition Cn of the n-dimensional half cube in a manner compatible with its structure as a polytope. For each 3≤k≤n, the complex Cn has a subcomplex Cn,k, which coincides with the clique complex of the half cube graph if k=4. The homology of Cn,k is concentrated…
Obtaining compact and discriminative features is one of the major challenges in many of the real-world image classification tasks such as face verification and object recognition. One possible approach is to represent input image on the basis of high-level features that carry semantic meaning which humans can understan…
Study Betti and Hodge numbers of solvmanifolds from integer polynomials.
problem Computing Betti and Hodge numbers of solvmanifolds constructed from integer polynomials.
method Analyzing de Rham and Dolbeault cohomology of solvmanifolds under algebraic conditions.
result Explicit generating polynomials for Hodge numbers in quasi full rank case.
Benchmark improves object detection robustness in winter weather.
problem Assessing object detection models' performance under image corruptions.
method Developed three benchmark datasets with various image corruptions; used data augmentation to improve robustness.
result Simple data augmentation significantly enhances model robustness across different corruptions and datasets.
Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a po…
Let n,m,k be positive integers with k=n−m+1. We establish an abstract Morse-Sard-type theorem which allows us to deduce, on the one hand, a previous result of De Pascale's for Sobolev Wlock,p(Rn,Rm) functions with p>n and, on the other hand, also the following new result: i…
Smartfluidnet accelerates Eulerian fluid simulation with neural networks.
problem Current neural network methods for Eulerian fluid simulation lack flexibility and generalization.
method Smartfluidnet automates model generation and dynamic switching to meet user requirements.
result Smartfluidnet achieves 1.46x and 590x speedup compared to state-of-the-art models, with better simulation quality.
A study ranks critical Lean Six Sigma tools for implementation in Portuguese companies.
problem Identifying the most important tools for successful Lean Six Sigma implementation in Portugal.
method An online survey with Portuguese consultants evaluated 37 tools based on frequency of use, difficulty, importance, and impact. A ranking was developed using a procedure to assess consultants' know-how.
result Honshin Kanri, VOC, VSM were identified as the most important tools for Lean Six Sigma implementation.
Auto-Compressing Subset Pruning reduces model size for faster inference.
problem High parameter counts and slow inference times in semantic segmentation models.
method Learning a channel selection mechanism based on temperature annealing schedule.
result Significant compression of segmentation models with acceptable inference performance.
We introduce a new multi-dimensional nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for image segmentation. Based on the theory of sparse signal recovery, piecewise flat embedding with diverse channels attempts to recover a piecewise constant image representation with sparse region boundaries and sparse clust…