Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

外形

PLMCL: Partial-Label Momentum Curriculum Learning for Multi-label Image Classificationthe fact that it could be expensive in practice to annotate all labels in every training image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subset of its labels. A special case is to annotate only one positive label in each training i

lobster

parallelism

SW-VAE: Weakly Supervised Learn Disentangled Representation via Latent Factor Swappingny unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training appro

indigenous

Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolfrom the absence of paired dataset. One of the most common approaches is synthesizing noisy LR images using GANs (i.e., degradation generators) and utilizing a synthetic dataset to train the model in a supervised manner. Although the goal of training the degradation generator is to approximate the d

leniency

Out-of-Distribution Detection Without Class Labelsularly in the case where the normal data distribution consist of multiple semantic classes (e.g. multiple object categories). To overcome this challenge, current approaches require manual labeling of the normal images provided during training. In this work, we tackle multi-class novelty detection .

nonradioactive

Unsupervised Domain Adaptive Object Detection with Class Label Shift Weighted Local Features achieved compelling results in unsupervised domain adaptive object detection. However, such marginal feature alignment suffers from the class label shift between source and target domains. Existing class label shift correction methods focus on image classification, and cannot be directly applied to

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Allege

Semi-supervised Domain Adaptation by Similarity Based Pseudo-Label Injection causing the model to be biased towards the source domain. Recent works in SSDA show that aligning only the labeled target samples with the source samples potentially leads to incomplete domain alignment of the target domain to the source domain. In our approach, to align the two domains, we leverag

–FER

Evaluating Image Super-Resolution Performance on Mobile Devices: An Online Benchmark. With the ubiquitous use of AI-accelerators on mobile devices (...., smartphones), increasing attention has been received to develop mobile-friendly SR models. Because of the complicated and tedious routines to deploy SR models on mobile devices, researchers have to use indirect indices, such as FL

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Style Adaptive Semantic Image Editing with Transformers approaches typically lack control over the style of the editing, resulting in insufficient flexibility to support the desired level of customization, ., to turn an object into a particular style or to pick a specific instance. In this work, we propose Style Adaptive Semantic Image Editing (SASIE),