Reviewerly/exHarmony huggingface dataset:
Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers

The state-of-the-art for monocular 3D human pose estimation in videos is dominated by the paradigm of 2D-to-3D pose uplifting. While the uplifting methods themselves are rather efficient, the true computational complexity depends on the per-frame 2D pose estimation. In this paper, we present a Transformer-based pose uplifting scheme that can operate on temporally sparse 2D pose sequences but still produce temporally dense 3D pose estimates. We show how masked token modeling can be utilized for temporal upsampling within Transformer blocks. This allows to decouple the sampling rate of input 2D poses and the target frame rate of the video and drastically decreases the total computational complexity. Additionally, we explore the option of pre-training on large motion capture archives, which has been largely neglected so far We evaluate our method on two popular benchmark datasets: Human3.6M and MPI-INF-3DHP. With an MPJPE of 45.0 mm and 46.9 mm, respectively, our proposed method can compete with the state-of-the-art while reducing inference time by a factor of 12. This enables real-time throughput with variable consumer hardware in stationary and mobile applications. We release our code and models at https://github.com/goldbricklemon/uplift-upsample-3dhpe
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video

Multiple existing benchmarks involve tracking and segmenting objects in video e.g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to the use of disparate benchmark datasets and metrics (e.g. $\mathcal{J}\& {\mathcal{F}}$, mAP, sMOTSA). As a result, published works usually target a particular benchmark, and are not easily comparable to each another. We believe that the development of generalized methods that can tackle multiple tasks requires greater cohesion among these research sub-communities. In this paper, we aim to facilitate this by proposing BURST, a dataset which contains thousands of diverse videos with high-quality object masks, and an associated benchmark with six tasks involving object tracking and segmentation in video. All tasks are evaluated using the same data and comparable metrics, which enables researchers to consider them in unison, and hence, more effectively pool knowledge from different methods across different tasks. Additionally, we demonstrate several baselines for all tasks and show that approaches for one task can be applied to another with a quantifiable and explainable performance difference. Dataset annotations are available at: https://github.com/Ali2500/BURST-benchmark.
RAST: Restorable Arbitrary Style Transfer via Multi-restoration

Arbitrary style transfer aims to reproduce the target image with the artistic or photo-realistic styles provided. Even though existing approaches can successfully transfer style information, arbitrary style transfer still faces many challenges, such as the content leak issue. Specifically, the embedding of artistic style can lead to content changes. In this paper, we solve the content leak problem from the perspective of image restoration. In particular, an iterative architecture is proposed to achieve the Restorable Arbitrary Style Transfer (RAST), which can realize transmission of both content and style information through multi-restorations. We control the content-style balance in stylized images by the accuracy of image restoration. In order to ensure effectiveness of the proposed RAST architecture, we design two novel loss functions: multi-restoration loss and style difference loss. In addition, we propose a new quantitative evaluation method to measure content preservation performance and style embedding performance. Comprehensive experiments comparing with state-of-the-art methods demonstrate that our proposed architecture can produce stylized images with superior performance on content preservation and style embedding.
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently

Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model’s predictions. However, the transferability has not been understood in terms of to which class target model’s predictions were misled (i.e., class-aware transferability). In this paper, we differentiate the cases in which a target model predicts the same wrong class as the source model ("same mistake") or a different wrong class ("different mistake") to analyze and provide an explanation of the mechanism. We find that (1) AEs tend to cause same mistakes, which correlates with "non-targeted transferability"; how-ever, (2) different mistakes occur even between similar models, regardless of the perturbation size. Furthermore, we present evidence that the difference between same mistakes and different mistakes can be explained by non-robust features, predictive but human-uninterpretable patterns: different mistakes occur when non-robust features in AEs are used differently by models. Non-robust features can thus provide consistent explanations for the class-aware transferability of AEs.
Aerial Image Dehazing with Attentive Deformable Transformers

Aerial imagery is widely utilized in visual data dependent applications such as military surveillance, earthquake assessment, etc. For these applications, minute texture in the aerial image are essential as any disturbance can cause inaccurate prediction. However, atmospheric haze severely reduces the visibility of the scene to be analysed, and hence takes a toll on accuracy of higher level applications. Existing methods either utilize additional prior while training, or produce sub-optimal outputs on different densities of haze degradation, due to absence of local and global dependencies in the extracted features. Therefore, it is essential to have a texture preserving algorithm for aerial image dehazing. In light of this, we propose a work that introduces a novel deformable multi-head attention with spatially attentive offset extraction based solution for aerial image dehazing. Here, the deformable multi-head attention is introduced to reconstruct fine level texture in the restored image. We also introduce spatially attentive offset extractor in the deformable convolution for focusing on relevant contextual information. Further, edge boosting skip connections are proposed for effectively passing edge features from shallow layers to deeper layers of the network. Thorough experimentation on synthetic as well as real-world data, along with extensive ablation study, demonstrate that the proposed method outperforms the prevailing works on aerial image dehazing. The code is provided at https://github.com/AshutoshKulkarni4998/AIDTransformer.
Multi-View Action Recognition using Contrastive Learning

In this work, we present a method for RGB-based action recognition using multi-view videos. We present a supervised contrastive learning framework to learn a feature embedding robust to changes in viewpoint, by effectively leveraging multi-view data. We use an improved supervised contrastive loss and augment the positives with those coming from synchronized viewpoints. We also propose a new approach to use classifier probabilities to guide the selection of hard negatives in the contrastive loss, to learn a more discriminative representation. Negative samples from confusing classes based on posterior are weighted higher. We also show that our method leads to better domain generalization compared to the standard supervised training based on synthetic multi-view data. Extensive experiments on real (NTU-60, NTU-120, NUMA) and synthetic (RoCoG) data demonstrate the effectiveness of our approach.
Fashion Image Retrieval with Text Feedback by Additive Attention Compositional Learning

Effective fashion image retrieval with text feedback stands to impact a range of real-world applications, such as e-commerce. Given a source image and text feedback that describes the desired modifications to that image, the goal is to retrieve the target images that resemble the source yet satisfy the given modifications by composing a multi-modal (image-text) query. We propose a novel solution to this problem, Additive Attention Compositional Learning (AACL), that uses a multi-modal transformer-based architecture and effectively models the image-text contexts. Specifically, we propose a novel image-text composition module based on additive attention that can be seamlessly plugged into deep neural networks. We also introduce a new challenging benchmark derived from the Shopping100k dataset. AACL is evaluated on three large-scale datasets (FashionIQ, Fashion200k, and Shopping100k), each with strong baselines. Extensive experiments show that AACL achieves new state-of-the-art results on all three datasets.
Audio-Visual Face Reenactment

This work proposes a novel method to generate realistic talking head videos using audio and visual streams. We animate a source image by transferring head motion from a driving video using a dense motion field generated using learnable keypoints. We improve the quality of lip sync using audio as an additional input, helping the network to attend to the mouth region. We use additional priors using face segmentation and face mesh to improve the structure of the reconstructed faces. Finally, we improve the visual quality of the generations by incorporating a carefully designed identity-aware generator module. The identity-aware generator takes the source image and the warped motion features as input to generate a high-quality output with fine-grained details. Our method produces state-of-the-art results and generalizes well to unseen faces, languages, and voices. We comprehensively evaluate our approach using multiple metrics and outperforming the current techniques both qualitative and quantitatively. Our work opens up several applications, including enabling low bandwidth video calls. We release a demo video and additional information at http://cvit.iiit.ac.in/research/projects/cvit-projects/avfr.
Discriminative feature mining with relation regularization for person re-identification

The appearance attribute and pose are two important and complementary features, so integrating them can effectively alleviate the impact of misalignment and occlusion on re-identification. In this paper, we deeply investigate the inner relation between attribute features and the spatial semantic relation between key-point region features of the pose in a person image and propose a person re-identification method based on discriminative feature mining with relation regularization. Firstly, an attribute relation detector based on nonlinear graph convolution is built on mining the inner correlation between attribute features of a person, providing relational attribute features for more effectively distinguishing persons with a similar appearance. Then, we construct a hierarchical pose pyramid to model the multi-grained semantic features of key-point regions of the pose and propose intra-graph and cross-graph node relation information propagation structures to infer the spatial semantic relation between node features within-graph and between-graph. This module is robust to complex pose changes and can suppress noise background redundancy caused by inaccurate key point detection and occlusion. Finally, a refined feature model is proposed to effectively fuse the global appearance feature with the relational attribute and multi-grained pose features, thus providing a more discriminative fusion feature for person re-identification. Many experiments on three large-scale datasets verify the effectiveness and state-of-the-art performance of the proposed method.
Adaptive Spatial Tokenization Transformer for Salient Object Detection in Optical Remote Sensing Images

Convolutional neural network (CNN)-based salient object detection (SOD) models have achieved promising performance in optical remote sensing images (ORSIs) in recent years. However, the restriction concerning the local sliding window operation of CNN has caused many existing CNN-based ORSI SOD models to still struggle with learning long-range relationships. To this end, a novel transformer framework is proposed for ORSI SOD, which is inspired by the powerful global dependency relationships of transformer networks. This is the first attempt to explore global and local details using transformer architecture for SOD in ORSIs. Concretely, we design an adaptive spatial tokenization transformer encoder to extract global–local features, which can accurately sparsify tokens for each input image and achieve competitive performance in ORSI SOD tasks. Then, a specific dense token aggregation decoder (DTAD) is proposed to generate saliency results, including three cascade decoders to integrate the global–local tokens and contextual dependencies. Extensive experiments indicate that the proposed model greatly surpasses 20 state-of-the-art (SOTA) SOD approaches on two standard ORSI SOD datasets under seven evaluation metrics. We also report comparison results to demonstrate the generalization capacity on the latest challenging ORSI datasets. In addition, we validate the contributions of different modules through a series of ablation analyses, especially the proposed adaptive spatial tokenization module (ASTM), which can halve the computational budget.
Multi-channel nuclear norm minus Frobenius norm minimization for color image denoising

Color image denoising is frequently encountered in various image processing and computer vision tasks. One traditional strategy is to convert the RGB image to a less correlated color space and denoise each channel of the new space separately. However, such a strategy can not fully exploit the correlated information between channels and is inadequate to obtain satisfactory results. To address this issue, this paper proposes a new multi-channel optimization model for color image denoising under the nuclear norm minus Frobenius norm minimization framework. Specifically, based on the block-matching, the color image is decomposed into overlapping RGB patches. For each patch, we stack its similar neighbors to form the corresponding patch matrix. The proposed model is performed on the patch matrix to recover its noise-free version. During the recovery process, a) a weight matrix is introduced to fully utilize the noise difference between channels; b) the singular values are shrunk adaptively without additionally assigning weights. With them, the proposed model can achieve promising results while keeping simplicity. To solve the proposed model, an accurate and effective algorithm is built based on the alternating direction method of multipliers (ADMM) framework. The solution of each updating step can be analytically expressed in closed-from. Rigorous theoretical analysis proves that the solution sequences generated by the proposed algorithm converge to their respective stationary points. Experimental results on both synthetic and real noise data sets demonstrate the proposed model outperforms state-of-the-art models. MATLAB code is available at https://www.github.com/wangzhi-swu/MCNNFNM.
A Novel Two-Stream Transformer-Based Framework for Multi-Modality Human Action Recognition

Due to the great success of Vision Transformer (ViT) in image classification tasks, many pure Transformer architectures for human action recognition have been proposed. However, very few works have attempted to use Transformer to conduct bimodal action recognition, i.e., both skeleton and RGB modalities for action recognition. As proved in many previous works, RGB modality and skeleton modality are complementary to each other in human action recognition tasks. How to use both RGB and skeleton modalities for action recognition in a Transformer-based framework is a challenge. In this paper, we propose RGBSformer, a novel two-stream pure Transformer-based framework for human action recognition using both RGB and skeleton modalities. Using only RGB videos, we can acquire skeleton data and generate corresponding skeleton heatmaps. Then, we input skeleton heatmaps and RGB frames to Transformer at different temporal and spatial resolutions. Because the skeleton heatmaps are primary features compared to the original RGB frames, we use fewer attention layers in the skeleton stream. At the same time, two ways are proposed to fuse the information of two streams. Experiments demonstrate that the proposed framework achieves the state of the art on four benchmarks: three widely used datasets, Kinetics400, NTU RGB+D 60, and NTU RGB+D 120, and the fine-grained dataset FineGym99.
Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method.
Health Implications of Virtual Architecture: An Interdisciplinary Exploration of the Transferability of Findings from Neuroarchitecture

Virtual architecture has been increasingly relied on to evaluate the health impacts of physical architecture. In this health research, exposure to virtual architecture has been used as a proxy for exposure to physical architecture. Despite the growing body of research on the health implications of physical architecture, there is a paucity of research examining the long-term health impacts of prolonged exposure to virtual architecture. In response, this paper considers: what can proxy studies, which use virtual architecture to assess the physiological response to physical architecture, tell us about the impact of extended exposure to virtual architecture on human health? The paper goes on to suggest that the applicability of these findings to virtual architecture may be limited by certain confounding variables when virtual architecture is experienced for a prolonged period of time. This paper explores the potential impact of two of these confounding variables: multisensory integration and gravitational perception. This paper advises that these confounding variables are unique to extended virtual architecture exposure and may not be captured by proxy studies that aim to capture the impact of physical architecture on human health through acute exposure to virtual architecture. While proxy studies may be suitable for measuring some aspects of the impact of both physical and virtual architecture on human health, this paper argues that they may be insufficient to fully capture the unintended consequences of extended exposure to virtual architecture on human health. Therefore, in the face of the increasing use of virtual architectural environments, the author calls for the establishment of a subfield of neuroarchitectural health research that empirically examines the physiological impacts of extended exposure to virtual architecture in its own right.
Multimodal learning analytics of collaborative patterns during pair programming in higher education

Abstract Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.