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Found 14 papers, 4 papers with code
Resolving Inconsistent Semantics in Multi-Dataset Image Segmentation
Notably, on four benchmark datasets with label space inconsistencies during inference, we outperform previous methods by 1. 6% mIoU for semantic segmentation, 9. 1% PQ for panoptic segmentation, 12. 1% AP for instance segmentation, and 3. 0% in the newly proposed PIQ metric.
Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation
With efficiency being a high priority for scaling such models, we observed that the state-of-the-art method Mask2Former uses ~50% of its compute only on the transformer encoder.
Efficient Transformer Encoders for Mask2Former-style models
The third step is to use the aforementioned derived dataset to train a gating network that predicts the number of encoder layers to be used, conditioned on the input image.
Efficient Controllable Multi-Task Architectures
Further, we present a simple but effective search algorithm that translates user constraints to runtime width configurations of both the shared encoder and task decoders, for sampling the sub-architectures.
Cross-Domain Video Anomaly Detection without Target Domain Adaptation
Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source to the target domain.
Leveraging Local Patch Differences in Multi-Object Scenes for Generative Adversarial Attacks
Our goal is to design an attack strategy that can learn from such natural scenes by leveraging the local patch differences that occur inherently in such images (e. g. difference between the local patch on the object `person' and the object `bike' in a traffic scene).
GAMA: Generative Adversarial Multi-Object Scene Attacks
Using the joint image-text features to train the generator, we show that GAMA can craft potent transferable perturbations in order to fool victim classifiers in various attack settings.
Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image
In this work, we explore a sparsity and dictionary learning-based approach and present a novel self-supervised learning method for single-image denoising where the noise is approximated as a Poisson process, requiring no clean ground-truth data.
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied.
Deep Quantized Representation for Enhanced Reconstruction
In this paper, we propose a data-driven Deep Quantized Latent Representation (DQLR) methodology for high-quality image reconstruction in the Shoot Apical Meristem (SAM) of Arabidopsis thaliana.
Spatio-Temporal Representation Factorization for Video-based Person Re-Identification
To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Ranked #2 on
Person Re-Identification
on DukeMTMC-VideoReID
Elastic Weight Consolidation (EWC): Nuts and Bolts
In this report, we present a theoretical support of the continual learning method \textbf{Elastic Weight Consolidation}, introduced in paper titled `Overcoming catastrophic forgetting in neural networks'.
ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation
Different from these works, we address a more realistic problem of high frame-rate sharp video synthesis with no prior assumption that input is always blurry.
Non-Adversarial Video Synthesis with Learned Priors
Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames.
