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Found 67 papers, 21 papers with code
Box2Seg: Attention Weighted Loss and Discriminative Feature Learning for Weakly Supervised Segmentation
We propose a weakly supervised approach to semantic segmentation using bounding box annotations.
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
Semantic segmentation models struggle to generalize in the presence of domain shift.
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning
To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation.
Task-Agnostic Robust Representation Learning
To tackle this problem, a great amount of research has been done to study the training procedure of a network to improve its robustness.
Language Matters: A Weakly Supervised Pre-training Approach for Scene Text Detection and Spotting
This paper presents a weakly supervised pre-training method that can acquire effective scene text representations by jointly learning and aligning visual and textual information.
Gradients without Backpropagation
Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning.
Deeply Explain CNN via Hierarchical Decomposition
The proposed framework can generate a deep hierarchy of strongly associated supporting evidence for the network decision, which provides insight into the decision-making process.
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
Specifically, we experimentally show that directly encouraging CIL Learner at the initial phase to output similar representations as the model jointly trained on all classes can greatly boost the CIL performance.
A Continuous Mapping For Augmentation Design
Automated data augmentation (ADA) techniques have played an important role in boosting the performance of deep models.
Overcoming the Convex Barrier for Simplex Inputs
Recent progress in neural network verification has challenged the notion of a convex barrier, that is, an inherent weakness in the convex relaxation of the output of a neural network.
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning
We present an approach to contrastive representation learning for semantic segmentation.
PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer
Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e. g., remote healthcare and affective computing).
Hierarchical interaction network for video object segmentation from referring expressions
In this paper, we investigate the problem of video object segmentation from referring expressions (VOSRE).
Ranked #1 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Optical Flow Estimation
Referring Expression Segmentation
+3
Adversarial Examples on Segmentation Models Can be Easy to Transfer
The high transferability achieved by our method shows that, in contrast to the observations in previous work, adversarial examples on a segmentation model can be easy to transfer to other segmentation models.
TransMix: Attend to Mix for Vision Transformers
The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map.
Mix-MaxEnt: Creating High Entropy Barriers To Improve Accuracy and Uncertainty Estimates of Deterministic Neural Networks
We propose an extremely simple approach to regularize a single deterministic neural network to obtain improved accuracy and reliable uncertainty estimates.
Memory-Driven Text-to-Image Generation
We introduce a memory-driven semi-parametric approach to text-to-image generation, which is based on both parametric and non-parametric techniques.
Towards fast and effective single-step adversarial training
In this work, we methodically revisit the role of noise and clipping in single-step adversarial training.
Communicating via Markov Decision Processes
We consider the problem of communicating exogenous information by means of Markov decision process trajectories.
Data-Dependent Randomized Smoothing
In this work, we revisit Gaussian randomized smoothing and show that the variance of the Gaussian distribution can be optimized at each input so as to maximize the certification radius for the construction of the smooth classifier.
Shape-Tailored Deep Neural Networks With PDEs
ST-DNN are deep networks formulated through the use of partial differential equations (PDE) to be defined on arbitrarily shaped regions.
Vision Transformer with Progressive Sampling
As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens.
Multilevel Knowledge Transfer for Cross-Domain Object Detection
The key ingredients to our approach are -- (a) mapping the source to the target domain on pixel-level; (b) training a teacher network on the mapped source and the unannotated target domain using adversarial feature alignment; and (c) finally training a student network using the pseudo-labels obtained from the teacher.
Open-World Entity Segmentation
By removing the need of class label prediction, the models trained for such task can focus more on improving segmentation quality.
Implicit Communication as Minimum Entropy Coupling
In many common-payoff games, achieving good performance requires players to develop protocols for communicating their private information implicitly -- i. e., using actions that have non-communicative effects on the environment.
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
Visual Parser: Representing Part-whole Hierarchies with Transformers
To model the representations of the two levels, we first encode the information from the whole into part vectors through an attention mechanism, then decode the global information within the part vectors back into the whole representation.
Ranked #159 on
Image Classification
on ImageNet
Large-scale Unsupervised Semantic Segmentation
Powered by the ImageNet dataset, unsupervised learning on large-scale data has made significant advances for classification tasks.
Don't be picky, all students in the right family can learn from good teachers
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models.
General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment
Specifically, compared with previous methods, we propose a more efficient pixel-level training constraint to weaken the hardness of aligning adversarial samples to clean samples, which can thus obviously enhance the robustness on adversarial samples.
How Benign is Benign Overfitting ?
We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models.
Shape-Tailored Deep Neural Networks Using PDEs for Segmentation
We present Shape-Tailored Deep Neural Networks (ST-DNN).
Point Transformer
For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
Ranked #2 on
Semantic Segmentation
on S3DIS Area5
STEER : Simple Temporal Regularization For Neural ODE
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
Learning to Sample the Most Useful Training Patches from Images
Some image restoration tasks like demosaicing require difficult training samples to learn effective models.
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
Diagnosing and Preventing Instabilities in Recurrent Video Processing
Recurrent models are becoming a popular choice for video enhancement tasks such as video denoising.
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
Multi-Object Tracking (MOT) has been notoriously difficult to evaluate.
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Object detection has witnessed significant progress by relying on large, manually annotated datasets.
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
Using Hindsight to Anchor Past Knowledge in Continual Learning
In continual learning, the learner faces a stream of data whose distribution changes over time.
Global Texture Enhancement for Fake Face Detection in the Wild
In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets.
Domain-invariant Stereo Matching Networks
State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast, and texture.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
ShardNet: One Filter Set to Rule Them All
Deep CNNs have achieved state-of-the-art performance for numerous machine learning and computer vision tasks in recent years, but as they have become increasingly deep, the number of parameters they use has also increased, making them hard to deploy in memory-constrained environments and difficult to interpret.
The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks
When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation
Many applications require a camera to be relocalised online, without expensive offline training on the target scene.
Res2Net: A New Multi-scale Backbone Architecture
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
Ranked #7 on
RGB Salient Object Detection
on PASCAL-S
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
DGPose: Deep Generative Models for Human Body Analysis
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
Long-term Tracking in the Wild: A Benchmark
We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms.
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
It is empirically demonstrated that such learned potentials can improve segmentation accuracy and that certain label class interactions are indeed better modelled by a non-Gaussian potential.
Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation
We focus on the following three aspects of EM: (i) initialization; (ii) latent posterior estimation (E-step) and (iii) the parameter update (M-step).
Online Real-time Multiple Spatiotemporal Action Localisation and Prediction
To the best of our knowledge, ours is the first real-time (up to 40fps) system able to perform online S/T action localisation and early action prediction on the untrimmed videos of UCF101-24.
Deeply supervised salient object detection with short connections
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs).
Ranked #4 on
RGB Salient Object Detection
on SBU
Learning to Navigate the Energy Landscape
We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization.
Joint Object-Material Category Segmentation from Audio-Visual Cues
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.
Ranked #13 on
Visual Object Tracking
on TrackingNet
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and then use these relationships to draw inference on new unseen classes at test time.
Higher Order Conditional Random Fields in Deep Neural Networks
Recent deep learning approaches have incorporated CRFs into Convolutional Neural Networks (CNNs), with some even training the CRF end-to-end with the rest of the network.
Ranked #45 on
Semantic Segmentation
on PASCAL Context
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm.
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF inference problems.
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation
Many methods have been proposed to recover the intrinsic scene properties such as shape, reflectance and illumination from a single image.
ImageSpirit: Verbal Guided Image Parsing
This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images.
Learning Anchor Planes for Classification
It provides a set of anchor points which form a local coordinate system, such that each data point on the manifold can be approximated by a linear combination of its anchor points, and the linear weights become the local coordinate coding.
Improved Moves for Truncated Convex Models
Compared to previous approaches based on the LP relaxation, e. g. interior-point algorithms or tree-reweighted message passing (TRW), our method is faster as it uses only the efficient st-mincut algorithm in its design.
