Several recent works have directly extended the image masked autoencoder (MAE) with random masking into video domain, achieving promising results. However, unlike images, both spatial and temporal information are important for video understanding. This suggests that the random masking strategy that is inherited from the image MAE is less effective for video MAE. This motivates the design of a novel masking algorithm that can more efficiently make use of video saliency. Specifically, we propose a motion-guided masking algorithm (MGM) which leverages motion vectors to guide the position of each mask over time. Crucially, these motion-based correspondences can be directly obtained from information stored in the compressed format of the video, which makes our method efficient and scalable. On two challenging large-scale video benchmarks (Kinetics-400 and Something-Something V2), we equip video MAE with our MGM and achieve up to +1.3% improvement compared to previous state-of-the-art methods. Additionally, our MGM achieves equivalent performance to previous video MAE using up to 66% fewer training epochs. Lastly, we show that MGM generalizes better to downstream transfer learning and domain adaptation tasks on the UCF101, HMDB51, and Diving48 datasets, achieving up to +4.9% improvement compared to baseline methods.

Many computer vision challenges require continuous outputs, but tend to be solved by discrete classification. The reason is classification's natural containment within a probability n-simplex, as defined by the popular softmax activation function. Regular regression lacks such a closed geometry, leading to unstable training and convergence to suboptimal local minima. Starting from this insight we revisit regression in convolutional neural networks. We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation. A natural framework for posing such continuous output problems are n-spheres, which are naturally closed geometric manifolds defined in the R^(n+1) space. By introducing a spherical exponential mapping on n-spheres at the regression output, we obtain well-behaved gradients, leading to stable training. We show how our spherical regression can be utilized for several computer vision challenges, specifically viewpoint estimation, surface normal estimation and 3D rotation estimation. For all these problems our experiments demonstrate the benefit of spherical regression. All paper resources are available at https://github.com/leoshine/Spherical_Regression.

The goal of this paper is to search and match the best rendered view of a texture-free 3D shape to an object of interest in a 2D query image. Matching rendered views of 3D shapes to RGB images is challenging because, 1) 3D shapes are not always a perfect match for the image queries, 2) there is great domain difference between rendered and RGB images, and 3) estimating the object scale versus distance is inherently ambiguous in images from uncalibrated cameras. In this work we propose a deeply learned matching function that attacks these challenges and can be used for a search engine that finds the appropriate 3D shape and matches it to objects in 2D query images. We evaluate the proposed matching function and search engine with a series of controlled experiments on the 24 most populated vehicle categories in PASCAL3D+. We test the capability of the learned matching function in transferring to unseen 3D shapes and study overall search engine sensitivity w.r.t available 3D shapes and object localization accuracy, showing promising results in retrieving 3D shapes given 2D image queries.

How to estimate cross-media relevance between a given query and an unlabeled image is a key question in the MSR-Bing Image Retrieval Challenge. We answer the question by proposing cross-media relevance fusion, a conceptually simple framework that exploits the power of individual methods for cross-media relevance estimation. Four base cross-media relevance functions are investigated, and later combined by weights optimized on the development set. With DCG25 of 0.5200 on the test dataset, the proposed image retrieval system secures the first place in the evaluation.

Given the difficulty of acquiring labeled examples for many fine-grained visual classes, there is an increasing interest in zero-shot image tagging, aiming to tag images with novel labels that have no training examples present. Using a semantic space trained by a neural language model, the current state-of-the-art embeds both images and labels into the space, wherein cross-media similarity is computed. However, for labels of relatively low occurrence, its similarity to images and other labels can be unreliable. This paper proposes Hierarchical Semantic Embedding (HierSE), a simple model that exploits the WordNet hierarchy to improve label embedding and consequently image embedding. Moreover, we identify two good tricks, namely training the neural language model using Flickr tags instead of web documents, and using partial match instead of full match for vectorizing a WordNet node. All this lets us outperform the state-of-the-art. On a test set of over 1,500 visual object classes and 1.3 million images, the proposed model beats the current best results (18.3% versus 9.4% in hit@1).

The use of geo tags in recording the location at which a picture was taken is becoming part of image metadata . Therefore , studying approaches to image classification that can favorably exploit both geo tags and the underlying geo context has become an emerging topic. This paper contributes to geo-aware image classification by studying how to encode geo information into image representation. Given a geo-tagged image, we propose to extract geo-aware tag features by tag propagation from the geo and visual neighbors of the given image. Depending on what neighbors are used and how they are weighted, we present and compare eight variants of geo-aware tag features. Using millions of Flickr images as source data for tag feature extraction, experiments on a popular benchmark set justify the effectiveness and robustness of the proposed tag features for geo-aware image classification.

Given the proliferation of geo-tagged images, geo-aware image classification is an emerging topic. To derive a better image representation, tag features which represents an image as a histogram of tags are recently introduced. However, it is unclear whether geo tags can improve the tag features. To resolve the uncertainty, this paper studies geo-aware tag features. Our work is based on previous work which builds tag features by propagating tags from visual neighbors retrieved from many user-tagged images. What is different is that we build tag features by tag propagation from the union of visual and geo neighbors. This simple modification makes the new tag feature both content-aware and geo-aware. Using 1M Flickr images as a source set to construct the tag feature, experiments on the public NUS-WIDE set justify our proposal. The geo-aware tag feature outperforms the previous tag feature and a standard bag of visual words feature. Our geo-aware image classification system beats a recent alternative. For its simplicity and effectiveness, we consider the proposed tag feature promising for geo-aware image classification.