However, model training for contrastive learning is quite inefficient. In the high-dimensional vector space of the images, images can differ from each other in many ways. We address this problem with heuristic attention pixel-level contrastive loss for representation learning (HAPiCLR), a self-...
We also propose a progressive residual feature fusion network (PRFFN) with combined contrastive loss, and the main contributions include: 1) A general pixel-wise loss function based on contrastive learning is proposed, which can improve the fidelity and visual quality of SR images; 2) A light...
这篇文章主要是通过大量的实验研究,针对目前关于pixel-level输入的状态表征提取方法进行了对比分析,指出其中关于表征提取最重要的是预测reward和transition的能力,并提出了一种简单有效的表征提取方法。该论文最大的优势在于其通过大量的实验验证对状态表征提取方法进行了较为全面的梳理总结,但由于所提方法创新性不足,其在...
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decoder takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixellevel and tok...
3.2 Pixel-Level Cycle Association 如上图所示,对于随机采样的 source 和 target 图片,我们首先建立他们像素级别的关联。我们利用像素级别的循环一致性(pixel-level cycle consistency)来建立这种关联。 具体来说,对于任一 source 图片中的像素 S1,我们在 target 图片中选择与之相似度最高的像素 T。然后,对于选择的...
1. Introduction Built upon the success of Large Language Models (LLMs) [6, 24, 25, 37], large multimodal models (LMMs) 26374 have significantly enhanced high-level visual perception and user interaction experiences [2, 15, 17, 41]. Yet, most of them generate textual descriptions ...
contrastive cycle-consistency loss on the level of pixels. Fi- nally, [56] performs image-to-image translation for UDA in frequency space rather than pixel space using a Fourier transform. Beyond cycle-consistency, [12] enforces cross-domain consistent predict...
4.1). The gray level that maps into the output pixel at (x, y) is uniquely determined by interpolation among these four input pixels. Some output pixels may map to locations that fall outside the borders of the input image. In this case an arbitrary constant gray level (e.g., zero) ...
4.1). The gray level that maps into the output pixel at (x, y) is uniquely determined by interpolation among these four input pixels. Some output pixels may map to locations that fall outside the borders of the input image. In this case an arbitrary constant gray level (e.g., zero) ...
First, pixel-wise information may help overcome occlusion based on low-level clues [57, 44, 45]. Moreover, recent transformer architecture demonstrates strong performance in pixel-wise prediction [35, 9, 8]. From another perspective, pixel-wise prediction preserves more low-confident details, ...