Reviewerly/exHarmony huggingface dataset:
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

Data mixing augmentation have proved to be effective for improving the generalization ability of deep neural networks. While early methods mix samples by hand-crafted policies (e.g., linear interpolation), recent methods utilize saliency information to match the mixed samples and labels via complex offline optimization. However, there arises a trade-off between precise mixing policies and optimization complexity. To address this challenge, we propose a novel automatic mixup (AutoMix) framework, where the mixup policy is parameterized and serves the ultimate classification goal directly. Specifically, AutoMix reformulates the mixup classification into two sub-tasks (i.e., mixed sample generation and mixup classification) with corresponding sub-networks and solves them in a bi-level optimization framework. For the generation, a learnable lightweight mixup generator, Mix Block, is designed to generate mixed samples by modeling patch-wise relationships under the direct supervision of the corresponding mixed labels. To prevent the degradation and instability of bi-level optimization, we further introduce a momentum pipeline to train AutoMix in an end-to-end manner. Extensive experiments on nine image benchmarks prove the superiority of AutoMix compared with state-of-the-arts in various classification scenarios and downstream tasks.
Text2LIVE: Text-Driven Layered Image and Video Editing

We present a method for zero-shot, text-driven editing of natural images and videos. Given an image or a video and a text prompt, our goal is to edit the appearance of existing objects (e.g., texture) or augment the scene with visual effects (e.g., smoke, fire) in a semantic manner. We train a generator on an internal dataset, extracted from a single input, while leveraging an external pretrained CLIP model to impose our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the input. This allows us to control the generation and maintain high fidelity to the input via novel text-driven losses applied directly to the edit layer. Our method neither relies on a pretrained generator nor requires user-provided masks. We demonstrate localized, semantic edits on high-resolution images and videos across a variety of objects and scenes. Webpage: http://www.text2live.github.io .
Deep Safe Multi-view Clustering: Reducing the Risk of Clustering Performance Degradation Caused by View Increase

Multi-view clustering has been shown to boost clustering performance by effectively mining the complementary information from multiple views. However, we observe that learning from data with more views is not guaranteed to achieve better clustering performance than from data with fewer views. To address this issue, we propose a general deep learning based framework that is guaranteed to reduce the risk of performance degradation caused by view increase. Concretely, the model is trained to simultaneously extract complementary information and discard the meaningless noise by automatically selecting features. These two learning procedures are incorporated into one unified framework by the proposed optimization objective. In theory, the empirical clustering risk of the model is no higher than learning from data before the view increase and data of the new increased single view. Also, the expected clustering risk of the model under divergence-based loss is no higher than that with high probability. Comprehensive experiments on benchmark datasets demonstrate the effectiveness and superiority of the proposed framework in achieving safe multi-view clustering.
Contextual Debiasing for Visual Recognition with Causal Mechanisms

As a common problem in the visual world, contextual bias means the recognition may depend on the co-occurrence context rather than the objects themselves, which is even more severe in multi-label tasks due to multiple targets and the absence of location. Although some studies have focused on tackling the problem, removing the negative effect of context is still challenging because it is difficult to obtain the representation of contextual bias. In this paper, we propose a simple but effective framework employing causal inference to mitigate contextual bias. We first present a Structural Causal Model (SCM) clarifying the causal relation among object representations, context, and predictions. Then, we develop a novel Causal Context Debiasing (CCD) Module to pursue the direct effect of an instance. Specifically, we adopt causal intervention to eliminate the effect of confounder and counterfactual reasoning to obtain a Total Direct Effect (TDE) free from the contextual bias. Note that our CCD framework is orthogonal to existing statistical models and thus can be migrated to any other backbones. Extensive experiments on several multi-label classification datasets demonstrate the superiority of our model over other state-of-the-art baselines.
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation

3D LiDAR semantic segmentation is fundamental for autonomous driving. Several Unsupervised Domain Adaptation (UDA) methods for point cloud data have been recently proposed to improve model generalization for different sensors and environments. Researchers working on UDA problems in the image domain have shown that sample mixing can mitigate domain shift. We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing. CoSMix consists of a two-branch symmetric network that can process labelled synthetic data (source) and real-world unlabelled point clouds (target) concurrently. Each branch operates on one domain by mixing selected pieces of data from the other one, and by using the semantic information derived from source labels and target pseudo-labels. We evaluate CoSMix on two large-scale datasets, showing that it outperforms state-of-the-art methods by a large margin (Our code is available at https://github.com/saltoricristiano/cosmix-uda ).
Iterative Deep Homography Estimation

We propose Iterative Homography Network, namely IHN, a new deep homography estimation architecture. Different from previous works that achieve iterative refinement by network cascading or untrainable IC-LK iterator; the iterator of IHN has tied weights and is completely trainable. IHN achieves state-of-the-art accuracy on several datasets including challenging scenes. We propose 2 versions of IHN: (1) IHN for static scenes, (2) IHN-mov for dynamic scenes with moving objects. Both versions can be arranged in 1-scale for efficiency or 2-scale for accuracy. We show that the basic 1-scale IHN already outperforms most of the existing methods. On a variety of datasets, the 2-scale IHN outperforms all competitors by a large gap. We introduce IHN-mov by producing an inlier mask to further improve the estimation accuracy of moving-objects scenes. We experimentally show that the iterative framework of IHN can achieve 95% error reduction while considerably saving network parameters. When processing sequential image pairs, IHN can achieve 32.7 fps, which is about 8× the speed of IC-LK iterator: Source code is available at https://github.com/imdump178/IHN.
Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning

Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. Despite their performance, they still suffer from interference across tasks which leads to catastrophic forgetting. To ameliorate this problem, we propose to only activate and select sparse neurons for learning current and past tasks at any stage. More parameters space and model capacity can thus be reserved for the future tasks. This minimizes the interference between parameters for different tasks. To do so, we propose a Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers. Full Experience Replay (FER) provides effective supervision in learning the sparse activations of the neurons in different layers. A loss-aware reservoir-sampling strategy is developed to maintain the memory buffer. The proposed method is agnostic as to the network structures and the task boundaries. Experiments on different datasets show that SNCL achieves state-of-the-art result for mitigating forgetting.
Bridging the Gap Across Realities: Visual Transitions Between Virtual and Augmented Reality

Cross-Virtuality applications enabling users to move between different stages of Milgram’s reality-virtuality continuum are a rapidly growing field of research. Modern video see-through head-mounted displays allow users to switch between augmented and virtual reality without removing the headset. This enables for the first time a fluent transition between augmented and virtual reality. Based on insights from literature and preliminary experiments we designed and implemented four transitions: Fade, SimpleCut, TeleportBeam and Portal. These techniques were expected to represent the best suitable concepts for transitioning seamlessly between augmented and virtual reality. After incorporating results from a pre-study, the transition techniques were evaluated in a qualitative user study regarding user experience, simulator sickness, continuity and applicability. Participants were able to freely move between both realities during the study in an immersive analytics scenario for logistics data. In the user study, users preferred Fade in a workplace setting due to its efficiency and simplicity when transitioning frequently between realities. The Portal technique was deemed visually exciting and suitable for infrequent transitions between realities that differ greatly.
SQLite

In the two decades following its initial release, SQLite has become the most widely deployed database engine in existence. Today, SQLite is found in nearly every smartphone, computer, web browser, television, and automobile. Several factors are likely responsible for its ubiquity, including its in-process design, standalone codebase, extensive test suite, and cross-platform file format. While it supports complex analytical queries, SQLite is primarily designed for fast online transaction processing (OLTP), employing row-oriented execution and a B-tree storage format. However, fueled by the rise of edge computing and data science, there is a growing need for efficient in-process online analytical processing (OLAP). DuckDB, a database engine nicknamed "the SQLite for analytics", has recently emerged to meet this demand. While DuckDB has shown strong performance on OLAP benchmarks, it is unclear how SQLite compares. Furthermore, we are aware of no work that attempts to identify root causes for SQLite's performance behavior on OLAP workloads. In this paper, we discuss SQLite in the context of this changing workload landscape. We describe how SQLite evolved from its humble beginnings to the full-featured database engine it is today. We evaluate the performance of modern SQLite on three benchmarks, each representing a different flavor of in-process data management, including transactional, analytical, and blob processing. We delve into analytical data processing on SQLite, identifying key bottlenecks and weighing potential solutions. As a result of our optimizations, SQLite is now up to 4.2X faster on SSB. Finally, we discuss the future of SQLite, envisioning how it will evolve to meet new demands and challenges.
Zero Experience Required: Plug &amp; Play Modular Transfer Learning for Semantic Visual Navigation

In reinforcement learning for visual navigation, it is common to develop a model for each new task, and train that model from scratch with task-specific interactions in 3D environments. However, this process is expensive; mas-sive amounts of interactions are needed for the model to generalize well. Moreover, this process is repeated when-ever there is a change in the task type or the goal modality. We present a unified approach to visual navigation using a novel modular transfer learning model. Our model can ef-fectively leverage its experience from one source task and apply it to multiple target tasks (e.g., ObjectNav, Room-Nav, Vi ewNav) with various goal modalities (e.g., image, sketch, audio, label). Furthermore, our model enables zero-shot experience learning, whereby it can solve the target tasks without receiving any task-specific interactive training. Our experiments on multiple photorealistic datasets and challenging tasks show that our approach learns faster, generalizes better, and outperforms SoTA models by a sig-nificant margin. Project page: https://vision.cs.utexas.edu/projects/zsel/
SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization

Recently self-supervised representation learning has drawn considerable attention from the scene text recognition community. Different from previous studies using contrastive learning, we tackle the issue from an alternative perspective, i.e., by formulating the representation learning scheme in a generative manner. Typically, the neighboring image patches among one text line tend to have similar styles, including the strokes, textures, colors, etc. Motivated by this common sense, we augment one image patch and use its neighboring patch as guidance to recover itself. Specifically, we propose a Similarity-Aware Normalization (SimAN) module to identify the different patterns and align the corresponding styles from the guiding patch. In this way, the network gains representation capability for distinguishing complex patterns such as messy strokes and cluttered backgrounds. Experiments show that the proposed SimAN significantly improves the representation quality and achieves promising performance. Moreover, we surprisingly find that our self-supervised generative network has impressive potential for data synthesis, text image editing, and font interpolation, which suggests that the proposed SimAN has a wide range of practical applications.
FABLE: Fast Approximate Quantum Circuits for Block-Encodings

Block-encodings of matrices have become an essential element of quantum algorithms derived from the quantum singular value transformation. This includes a variety of algorithms ranging from the quantum linear systems problem to quantum walk, Hamiltonian simulation, and quantum machine learning. Many of these algorithms achieve optimal complexity in terms of black box matrix oracle queries, but so far the problem of computing quantum circuit implementations for block-encodings of matrices has been under-appreciated. In this paper we propose FABLE, a method to generate approximate quantum circuits for block-encodings of matrices in a fast manner. FABLE circuits have a simple structure and are directly formulated in terms of one- and two-qubit gates. For small and structured matrices they are feasible in the NISQ era, and the circuit parameters can be easily generated for problems up to fifteen qubits. Furthermore, we show that FABLE circuits can be compressed and sparsified. We provide a compression theorem that relates the compression threshold to the error on the block-encoding. We benchmark our method for Heisenberg and Hubbard Hamiltonians, and Laplacian operators to illustrate that they can be implemented with a reduced gate complexity without approximation error.
Robust Face Hallucination Algorithm Using Motion Blur Embedded Nearest Proximate Patch Representation

Face hallucination (FH) techniques have received a lot of attention in recent years for generating high-resolution (HR) face images from captured low-resolution (LR), noisy, and blurry images. However, existing FH techniques are incapable of dealing with motion blur, which is commonly introduced in captured images due to camera defocussing and other factors. Therefore, to make the FH process more resistant to motion blur, in this article, we present a novel learning-based FH algorithm called Motion Blur Embedded Nearest Proximate Patch Representation (MBENPPR). The MBENPPR algorithm begins by estimating the motion blur kernel from a motion-blurred LR test face. The estimated kernel is then embedded in training images to make them compatible with test images. It assists in reducing the effect of motion blur in the reconstruction process. Furthermore, the nearest proximate patches are selected from the training space to represent the test image patches as a weighted linear combination of selected patches. It facilitates the proposed algorithm in preserving sharp edges and texture information in the resulting faces. The results of simulations on standard datasets and locally captured real-life faces show that the MBENPPR algorithm outperforms the compared existing algorithms.
Learned Vertex Descent: A New Direction for 3D Human Model Fitting

We propose a novel optimization-based paradigm for 3D human model fitting on images and scans. In contrast to existing approaches that directly regress the parameters of a low-dimensional statistical body model (e.g.SMPL) from input images, we train an ensemble of per vertex neural fields network. The network predicts, in a distributed manner, the vertex descent direction towards the ground truth, based on neural features extracted at the current vertex projection. At inference, we employ this network, dubbed LVD, within a gradient-descent optimization pipeline until its convergence, which typically occurs in a fraction of a second even when initializing all vertices into a single point. An exhaustive evaluation demonstrates that our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art. LVD is also applicable to 3D model fitting of humans and hands, for which we show a significant improvement to the SOTA with a much simpler and faster method. Code is released at https://www.iri.upc.edu/people/ecorona/lvd/
Scene Consistency Representation Learning for Video Scene Segmentation

A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a scene starts and ends. To this end, we propose an effective Self-Supervised Learning (SSL) framework to learn better shot representations from unlabeled long-term videos. More specifically, we present an SSL scheme to achieve scene consistency, while exploring considerable data augmentation and shuffling methods to boost the model generalizability. Instead of explicitly learning the scene boundary features as in the previous methods, we introduce a vanilla temporal model with less inductive bias to verify the quality of the shot features. Our method achieves the state-of-the-art performance on the task of Video Scene Segmentation. Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods. The code is made available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/TencentYoutuResearch/SceneSegmentation-SCRL.
F2DNet: Fast Focal Detection Network for Pedestrian Detection

Two-stage detectors are state-of-the-art in object detection as well as pedestrian detection. However, the current two-stage detectors are inefficient as they do bounding box regression in multiple steps i.e. in region proposal networks and bounding box heads. Also, the anchor-based region proposal networks are computationally expensive to train. We propose F2DNet, a novel two-stage detection architecture which eliminates redundancy of current two-stage detectors by replacing the region proposal network with our focal detection network and bounding box head with our fast suppression head. We benchmark F2DNet on top pedestrian detection datasets, thoroughly compare it against the existing state-of-the-art detectors and conduct cross dataset evaluation to test the generalizability of our model to unseen data. Our F2DNet achieves 8.7%, 2.2%, and 6.1% MR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> on City Persons, Caltech Pedestrian, and Euro City Person datasets respectively when trained on a single dataset and reaches 20.4% and 26.2% MR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in heavy occlusion setting of Caltech Pedestrian and City Persons datasets when using progressive fine-tunning. Furthermore, F2DNet have significantly lesser inference time compared to the current state-of-the-art. Code and trained models will be available at https://github.com/AbdulHannanKhan/F2DNet.
Toward a Green Blockchain: Engineering Merkle Tree and Proof of Work for Energy Optimization

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while significantly mitigating cybersecurity risks. Tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory and computation) required by blockchain-enabled hosts. This paper applies an energy-reducing algorithmic engineering technique for Merkle Tree (MT) root calculations and the Proof of Work (PoW) algorithm, two principal elements of blockchain computations, as a means to preserve the promised security benefits but with less compromise to system availability. Using pyRAPL, a python library to measure the energy consumption of a computation, we experiment with both the standard and energy-reduced implementations of both algorithms for different input sizes. Our results show that up to 98% reduction in energy consumption is possible within the blockchain’s MT construction module, with the benefits typically increasing with larger input sizes. For the PoW algorithm, our results show up to 20% reduction in energy consumption, with the benefits being lower for higher difficulty levels. The proposed energy-reducing technique is also applicable to other key elements of blockchain computations, potentially affording even “greener” blockchain-powered systems than implied by only the results obtained thus far on the MT and PoW algorithms.
Feature Statistics Mixing Regularization for Generative Adversarial Networks

In generative adversarial networks, improving discriminators is one of the key components for generation performance. As image classifiers are biased toward texture and debiasing improves accuracy, we investigate 1) if the discriminators are biased, and 2) if debiasing the discriminators will improve generation performance. Indeed, we find empirical evidence that the discriminators are sensitive to the style (e.g., texture and color) of images. As a remedy, we propose feature statistics mixing regularization (FSMR) that encourages the discriminator's prediction to be invariant to the styles of input images. Specifically, we generate a mixed feature of an original and a reference image in the discriminator's feature space and we apply regularization so that the prediction for the mixed feature is consistent with the prediction for the original image. We conduct extensive experiments to demonstrate that our regularization leads to reduced sensitivity to style and consistently improves the performance of various GAN architectures on nine datasets. In addition, adding FSMR to recently-proposed augmentation-based GAN methods further improves image quality. Our code is available at https://github.com/naver-ai/FSMR.