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Found 13 papers, 7 papers with code
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
REPEAT leverages the stochasticity of current R-XAI methods to produce multiple estimates of importance, thus considering each pixel in an image as a Bernoulli random variable that is either important or unimportant.
Prototypical Self-Explainable Models Without Re-training
Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on the other hand, self-explainable models (SEMs) which are trained directly to provide explanations alongside their predictions.
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Our method outperforms classical intensity augmentations as well as the intensity augmentation pipeline of the popular nn-UNet on multiple datasets.
DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain.
Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos
Multi-person 3D mesh recovery from videos is a critical first step towards automatic perception of group behavior in virtual reality, physical therapy and beyond.
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
To address this, we present DeepMVC, a unified framework for deep MVC that includes many recent methods as instances.
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Distance-based classification is frequently used in transductive few-shot learning (FSL).
ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design
ARMANI discretizes an image into uniform tokens based on a learned cross-modal codebook in its first stage and uses a Transformer to model the distribution of image tokens for a real image given the tokens of the control signals in its second stage.
RELAX: Representation Learning Explainability
Our approach can also model the uncertainty in its explanations, which is essential to produce trustworthy explanations.
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets.
WAS-VTON: Warping Architecture Search for Virtual Try-on Network
Despite recent progress on image-based virtual try-on, current methods are constraint by shared warping networks and thus fail to synthesize natural try-on results when faced with clothing categories that require different warping operations.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet.
