Foundation Vision Encoders have become indispensable across a wide range of dense vision tasks. However, their operation at low spatial feature resolutions necessitates subsequent feature decompression to enable full-resolution processing. To address this limitation, we introduce JAFAR, a lightweight and flexible feature upsampler designed to enhance the spatial resolution of visual features from any Foundation Vision Encoder to any target resolution. JAFAR features an attention-based upsampling module that aligns the spatial representations of high-resolution queries with semantically enriched low-resolution keys via Spatial Feature Transform modulation. Despite the absence of high-resolution feature ground truth; we find that learning at low upsampling ratios and resolutions generalizes surprisingly well to much higher scales. Extensive experiments demonstrate that JAFAR recovers intricate pixel-level details and consistently outperforms existing feature upsampling techniques across a diverse set of dense downstream applications.

Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised semantic segmentation, they significantly lag behind supervised models. In this paper, we use a diffusion UNet encoder as a foundation vision encoder and introduce DiffCut, an unsupervised zero-shot segmentation method that solely harnesses the output features from the final self-attention block. Through extensive experimentation, we demonstrate that using these diffusion features in a graph based segmentation algorithm, significantly outperforms previous state-of-the-art methods on zero-shot segmentation. Specifically, we leverage a recursive Normalized Cut algorithm that regulates the granularity of detected objects and produces well-defined segmentation maps that precisely capture intricate image details. Our work highlights the remarkably accurate semantic knowledge embedded within diffusion UNet encoders that could then serve as foundation vision encoders for downstream tasks.

Recent approaches of graph comparison consider graphs as sets of paths. Kernels on graphs are then computed from kernels on paths. A common strategy for graph retrieval is to perform pairwise comparisons. In this paper, we propose to follow a different strategy, where we collect a set of paths into a dictionary, and then project each graph to this dictionary. Then, graphs can be classified using powerful classification methods, such as SVM. Furthermore, we collect the paths through interaction with a user. This strategy is ten times faster than a straight comparisons of paths. Experiments have been carried out on a database of city windows.

The development of street-level geoviewers become recently a very active and challenging research topic. In this context, the detection, representation and classification of windows can be beneficial for the identification of the respective facade. In this paper, a novel method for windows and facade retrieval is presented. This method, based on a similarity of graph of contours, introduces a new kernel on graph for inexact graph matching. We design a kernel similarity function for structured sets of contours which will take into account the variations of contour orientation inside a structure set, as well as spatial proximity. Then we are able to extract a window as a sub-graph of the graph of all contours of the facade image and to retrieve similar windows from a database of images of facades.