Mutual Generative Transformer Learning for Cross-view Geo-localization

Cross-view geo-localization (CVGL), which aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aerial (e.g., satellite) images, remains extremely challenging due to the drastic appearance differences across views. Existing methods mainly employ Siamese-like CNNs to extract global descriptors without examining the mutual benefits between the two modes. In this paper, we present a novel approach using cross-modal knowledge generative tactics in combination with transformer, namely mutual generative transformer learning (MGTL), for CVGL. Specifically, MGTL develops two separate generative modules--one for aerial-like knowledge generation from ground-level semantic information and vice versa--and fully exploits their mutual benefits through the attention mechanism. Experiments on challenging public benchmarks, CVACT and CVUSA, demonstrate the effectiveness of the proposed method compared to the existing state-of-the-art models.

Mutual Graph Learning for Camouflaged Object Detection

Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background surroundings make the features extracted by deep model indistinguishable. To overcome this challenge, an ideal model should be able to seek valuable, extra clues from the given scene and incorporate them into a joint learning framework for representation co-enhancement. With this inspiration, we design a novel Mutual Graph Learning (MGL) model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. Specifically, MGL decouples an image into two task-specific feature maps -- one for roughly locating the target and the other for accurately capturing its boundary details -- and fully exploits the mutual benefits by recurrently reasoning their high-order relations through graphs. Importantly, in contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations to maximize information interactions. Experiments on challenging datasets, including CHAMELEON, CAMO and COD10K, demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods.

Place Clustering-based Feature Recombination for Visual Place Recognition

Visual place recognition is an important problem in both computer vision and robotics, and image content changes caused by occlusion and viewpoint changes in natural scenes still pose challenges to place recognition. This paper aims at the problem by proposing novel feature recombination based on place clustering. Firstly, a general pyramid extension scheme, called Pyramid Principal Phases Feature (Tri-PF), is extracted based on the histogram feature. Further to maximize the role of the new feature, we evaluate the similarity by clustering images with a certain threshold as a 'place'. Extensive experiments have been conducted to verify the effectiveness of the proposed approach and the results demonstrate that our method can achieve consistently better performance than state-of-the-art on two standard place recognition benchmarks.