Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification

Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing

Within the field of instance segmentation, most of the state-of-the-art deep learning networks rely nowadays on cascade architectures, where multiple object detectors are trained sequentially, re-sampling the ground truth at each step. This offers a solution to the problem of exponentially vanishing positive samples. However, it also translates into an increase in network complexity in terms of the number of parameters. To address this issue, we propose Recursively Refined R-CNN (R^3-CNN) which avoids duplicates by introducing a loop mechanism instead. At the same time, it achieves a quality b…

IS-Diff：通过更好的初始种子改进基于扩散的图像修复

IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed

提出IS-Diff，一种无需训练的扩散模型修复方法，通过分布和谐种子和动态选择性精炼机制解决随机噪声导致的语义不匹配问题，在多个数据集上优于现有方法。

Normalized Convolution Upsampling for Refined Optical Flow Estimation

Optical flow is a regression task where convolutional neural networks (CNNs) have led to major breakthroughs. However, this comes at major computational demands due to the use of cost-volumes and pyramidal representations. This was mitigated by producing flow predictions at quarter the resolution, which are upsampled using bilinear interpolation during test time. Consequently, fine details are usually lost and post-processing is needed to restore them. We propose the Normalized Convolution UPsampler (NCUP), an efficient joint upsampling approach to produce the full-resolution flow during the t…

NaRCan：融合扩散先验的自然精细化规范图像用于视频编辑

NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing

本研究提出视频编辑框架NaRCan，融合混合形变场与扩散先验生成高质量自然规范图像，结合LoRA微调与更新调度技术实现14倍训练加速，在多类视频编辑任务上表现优于现有方法。

Coarse-to-fine Semantic Segmentation from Image-level Labels

Deep neural network-based semantic segmentation generally requires large-scale cost extensive annotations for training to obtain better performance. To avoid pixel-wise segmentation annotations which are needed for most methods, recently some researchers attempted to use object-level labels (e.g. bounding boxes) or image-level labels (e.g. image categories). In this paper, we propose a novel recursive coarse-to-fine semantic segmentation framework based on only image-level category labels. For each image, an initial coarse mask is first generated by a convolutional neural network-based unsuper…

Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition

While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domain-relevant data needed for their training. It thus became common to use available synthetic samples along domain adaptation schemes to prepare algorithms for the target domain. Tackling this problem from a different angle, we introduce a pipeline to map unseen target samples into the synthetic domain used to train task-specific methods. Denoising the data and retaining only the features these recognition algorithms are familiar with, our solution greatly…