Emos Yalp

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136780420.pdfAbstractMost image denoising networks apply a single set of static convolutional kernels across the entire input image. This is sub-optimal for natural images, as they often consist of heterogeneous visual patterns. Dynamic convolution tries to address this issue by using per-pixel convolution kernels, but this greatly increas..

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136750176.pdfAbstractImage-based volumetric humans using pixel-aligned features promise generalization to unseen poses and identities. Prior work leverages global spatial encodings and multi-view geometric consistency to reduce spatial ambiguity. However, global encodings often suffer from overfitting to the distribution of the training da..

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136640485.pdfAbstractSelf-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding. Inspired by this success, we investigate the application of Transformer networks for temporal action localization in videos. To this end, we present Acti..

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136740450.pdfAbstractVisible-Infrared Re-Identification $($VI-ReID$)$ is challenging in image retrievals. The modality discrepancy will easily make huge intraclass variations. Most existing methods either bridge different modalities through modality-invariance or generate the intermediate modality for better performance. Differently, this ..

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136840126.pdfAbstractLearning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The key success of LNL lies in identifying as many clean samples as possible from massive noisy data, while rectifying the wrongly assigned noisy ..

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136800336.pdfAbstractWe tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all the objects of the target class. This task shares the same supervision as the few-shot counting but additionally outputs the object bounding boxes along with the ..

https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136810034.pdfAbstractRecently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks. However, how to effectively combine those operators to form high-performance hybrid visual architectures still remains a challenge. In this work, we study the learnable combination of convoluti..