pwc-archive/papers-with-abstracts huggingface dataset:
Self-supervised Video Transformer
Authors: Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Michael Ryoo
Date: 2021-12-02 00:00:00
Proceeding: CVPR 2022 1
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Self-Supervised Action Recognition Linear

In this paper, we propose self-supervised training for video transformers using unlabeled video data. From a given video, we create local and global spatiotemporal views with varying spatial sizes and frame rates. Our self-supervised objective seeks to match the features of these different views representing the same video, to be invariant to spatiotemporal variations in actions. To the best of our knowledge, the proposed approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT). Further, owing to the flexibility of Transformer models, SVT supports slow-fast video processing within a single architecture using dynamically adjusted positional encoding and supports long-term relationship modeling along spatiotemporal dimensions. Our approach performs well on four action recognition benchmarks (Kinetics-400, UCF-101, HMDB-51, and SSv2) and converges faster with small batch sizes. Code: https://git.io/J1juJ
OW-DETR: Open-world Detection Transformer
Authors: Akshita Gupta, Sanath Narayan, K J Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah
Date: 2021-12-02 00:00:00
Proceeding: CVPR 2022 1
Tasks: Inductive Bias, Object, object-detection, Object Detection, Open World Object Detection, Transfer Learning

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring to explicitly address the aforementioned OWOD challenges. Our OW-DETR explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background. Comprehensive experiments are performed on two benchmarks: MS-COCO and PASCAL VOC. The extensive ablations reveal the merits of our proposed contributions. Further, our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall on MS-COCO. In the case of incremental object detection, OW-DETR outperforms the state-of-the-art for all settings on PASCAL VOC. Our code is available at https://github.com/akshitac8/OW-DETR.
Neural Weight Step Video Compression
Authors: Mikolaj Czerkawski, Javier Cardona, Robert Atkinson, Craig Michie, Ivan Andonovic, Carmine Clemente, Christos Tachtatzis
Date: 2021-12-02 00:00:00
Tasks: Video Compression

A variety of compression methods based on encoding images as weights of a neural network have been recently proposed. Yet, the potential of similar approaches for video compression remains unexplored. In this work, we suggest a set of experiments for testing the feasibility of compressing video using two architectural paradigms, coordinate-based MLP (CbMLP) and convolutional network. Furthermore, we propose a novel technique of neural weight stepping, where subsequent frames of a video are encoded as low-entropy parameter updates. To assess the feasibility of the considered approaches, we will test the video compression performance on several high-resolution video datasets and compare against existing conventional and neural compression techniques.
Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases
Authors: Kelvin Kwakye, Emmanuel Dadzie
Date: 2021-12-02 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Coronary heart disease, which is a form of cardiovascular disease (CVD), is the leading cause of death worldwide. The odds of survival are good if it is found or diagnosed early. The current report discusses a comparative approach to the classification of coronary heart disease datasets using machine learning (ML) algorithms. The current study created and tested several machine-learning-based classification models. The dataset was subjected to Smote to handle unbalanced classes and feature selection technique in order to assess the impact on two distinct performance metrics. The results show that logistic regression produced the highest performance score on the original dataset compared to the other algorithms employed. In conclusion, this study suggests that LR on a well-processed and standardized dataset can predict coronary heart disease with greater accuracy than the other algorithms.
Dimensions of Motion: Monocular Prediction through Flow Subspaces
Authors: Richard Strong Bowen, Richard Tucker, Ramin Zabih, Noah Snavely
Date: 2021-12-02 00:00:00
Tasks: Depth Estimation, Depth Prediction, Instance Segmentation, Optical Flow Estimation, Prediction, Semantic Segmentation

We introduce a way to learn to estimate a scene representation from a single image by predicting a low-dimensional subspace of optical flow for each training example, which encompasses the variety of possible camera and object movement. Supervision is provided by a novel loss which measures the distance between this predicted flow subspace and an observed optical flow. This provides a new approach to learning scene representation tasks, such as monocular depth prediction or instance segmentation, in an unsupervised fashion using in-the-wild input videos without requiring camera poses, intrinsics, or an explicit multi-view stereo step. We evaluate our method in multiple settings, including an indoor depth prediction task where it achieves comparable performance to recent methods trained with more supervision.
Learning Spatial-Temporal Graphs for Active Speaker Detection
Authors: Sourya Roy, Kyle Min, Subarna Tripathi, Tanaya Guha, Somdeb Majumdar
Date: 2021-12-02 00:00:00
Tasks: Active Speaker Detection, Audio-Visual Active Speaker Detection, Node Classification

We address the problem of active speaker detection through a new framework, called SPELL, that learns long-range multimodal graphs to encode the inter-modal relationship between audio and visual data. We cast active speaker detection as a node classification task that is aware of longer-term dependencies. We first construct a graph from a video so that each node corresponds to one person. Nodes representing the same identity share edges between them within a defined temporal window. Nodes within the same video frame are also connected to encode inter-person interactions. Through extensive experiments on the Ava-ActiveSpeaker dataset, we demonstrate that learning graph-based representation, owing to its explicit spatial and temporal structure, significantly improves the overall performance. SPELL outperforms several relevant baselines and performs at par with state of the art models while requiring an order of magnitude lower computation cost.
Neural Point Light Fields
Authors: Julian Ost, Issam Laradji, Alejandro Newell, Yuval Bahat, Felix Heide
Date: 2021-12-02 00:00:00
Proceeding: CVPR 2022 1
Tasks: Novel View Synthesis

We introduce Neural Point Light Fields that represent scenes implicitly with a light field living on a sparse point cloud. Combining differentiable volume rendering with learned implicit density representations has made it possible to synthesize photo-realistic images for novel views of small scenes. As neural volumetric rendering methods require dense sampling of the underlying functional scene representation, at hundreds of samples along a ray cast through the volume, they are fundamentally limited to small scenes with the same objects projected to hundreds of training views. Promoting sparse point clouds to neural implicit light fields allows us to represent large scenes effectively with only a single radiance evaluation per ray. These point light fields are a function of the ray direction, and local point feature neighborhood, allowing us to interpolate the light field conditioned training images without dense object coverage and parallax. We assess the proposed method for novel view synthesis on large driving scenarios, where we synthesize realistic unseen views that existing implicit approaches fail to represent. We validate that Neural Point Light Fields make it possible to predict videos along unseen trajectories previously only feasible to generate by explicitly modeling the scene.
Altering Facial Expression Based on Textual Emotion
Authors: Mohammad Imrul Jubair, Md. Masud Rana, Md. Amir Hamza, Mohsena Ashraf, Fahim Ahsan Khan, Ahnaf Tahseen Prince
Date: 2021-12-02 00:00:00
Tasks: Generative Adversarial Network

Faces and their expressions are one of the potent subjects for digital images. Detecting emotions from images is an ancient task in the field of computer vision; however, performing its reverse -- synthesizing facial expressions from images -- is quite new. Such operations of regenerating images with different facial expressions, or altering an existing expression in an image require the Generative Adversarial Network (GAN). In this paper, we aim to change the facial expression in an image using GAN, where the input image with an initial expression (i.e., happy) is altered to a different expression (i.e., disgusted) for the same person. We used StarGAN techniques on a modified version of the MUG dataset to accomplish this objective. Moreover, we extended our work further by remodeling facial expressions in an image indicated by the emotion from a given text. As a result, we applied a Long Short-Term Memory (LSTM) method to extract emotion from the text and forwarded it to our expression-altering module. As a demonstration of our working pipeline, we also create an application prototype of a blog that regenerates the profile picture with different expressions based on the user's textual emotion.
SCNet: A Generalized Attention-based Model for Crack Fault Segmentation
Authors: Hrishikesh Sharma, Prakhar Pradhan, Balamuralidhar P
Date: 2021-12-02 00:00:00
Tasks: Anomaly Detection, Anomaly Segmentation, Crack Segmentation, Segmentation, Semantic Segmentation

Anomaly detection and localization is an important vision problem, having multiple applications. Effective and generic semantic segmentation of anomalous regions on various different surfaces, where most anomalous regions inherently do not have any obvious pattern, is still under active research. Periodic health monitoring and fault (anomaly) detection in vast infrastructures, which is an important safety-related task, is one such application area of vision-based anomaly segmentation. However, the task is quite challenging due to large variations in surface faults, texture-less construction material/background, lighting conditions etc. Cracks are critical and frequent surface faults that manifest as extreme zigzag-shaped thin, elongated regions. They are among the hardest faults to detect, even with deep learning. In this work, we address an open aspect of automatic crack segmentation problem, that of generalizing and improving the performance of segmentation across a variety of scenarios, by modeling the problem differently. We carefully study and abstract the sub-problems involved and solve them in a broader context, making our solution generic. On a variety of datasets related to surveillance of different infrastructures, under varying conditions, our model consistently outperforms the state-of-the-art algorithms by a significant margin, without any bells-and-whistles. This performance advantage easily carried over in two deployments of our model, tested against industry-provided datasets. Even further, we could establish our model's performance for two manufacturing quality inspection scenarios as well, where the defect types are not just crack equivalents, but much more and different. Hence we hope that our model is indeed a truly generic defect segmentation model.
3D-Aware Semantic-Guided Generative Model for Human Synthesis
Authors: Jichao Zhang, Enver Sangineto, Hao Tang, Aliaksandr Siarohin, Zhun Zhong, Nicu Sebe, Wei Wang
Date: 2021-12-02 00:00:00
Tasks: 3D-Aware Image Synthesis, Image Generation

Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D representations from 2D images, have recently been shown to produce realistic images representing rigid/semi-rigid objects, such as human faces or cars. However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications. This paper proposes a 3D-aware Semantic-Guided Generative Model (3D-SGAN) for human image synthesis, which combines a GNeRF with a texture generator. The former learns an implicit 3D representation of the human body and outputs a set of 2D semantic segmentation masks. The latter transforms these semantic masks into a real image, adding a realistic texture to the human appearance. Without requiring additional 3D information, our model can learn 3D human representations with a photo-realistic, controllable generation. Our experiments on the DeepFashion dataset show that 3D-SGAN significantly outperforms the most recent baselines. The code is available at https://github.com/zhangqianhui/3DSGAN
Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction
Authors: Matthew Stevenson, Christophe Mues, Cristián Bravo
Date: 2021-12-02 00:00:00

LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. However, the geographically granular and open-source nature of this data also lends itself to an array of societal, organizational and business applications where geo-demographic type data is utilised. Arguably, the complexity involved in processing this multi-dimensional data has thus far restricted its broader adoption. In this paper, we propose a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted.
Active Learning for Domain Adaptation: An Energy-Based Approach
Authors: Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, Guoren Wang
Date: 2021-12-02 00:00:00
Tasks: Active Learning, Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit \textit{free energy biases} when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of target data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https://github.com/BIT-DA/EADA.
Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation
Authors: Dipika Singhania, Rahul Rahaman, Angela Yao
Date: 2021-12-02 00:00:00
Tasks: Action Segmentation, Representation Learning, Segmentation, Semi-Supervised Video Classification, Temporal Action Segmentation

Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. Our method hinges on unsupervised representation learning, which, for temporal action segmentation, poses unique challenges. Actions in untrimmed videos vary in length and have unknown labels and start/end times. Ordering of actions across videos may also vary. We propose a novel way to learn frame-wise representations from temporal convolutional networks (TCNs) by clustering input features with added time-proximity condition and multi-resolution similarity. By merging representation learning with conventional supervised learning, we develop an "Iterative-Contrast-Classify (ICC)" semi-supervised learning scheme. With more labelled data, ICC progressively improves in performance; ICC semi-supervised learning, with 40% labelled videos, performs similar to fully-supervised counterparts. Our ICC improves MoF by {+1.8, +5.6, +2.5}% on Breakfast, 50Salads and GTEA respectively for 100% labelled videos.
TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation
Authors: Tan M. Dinh, Rang Nguyen, Binh-Son Hua
Date: 2021-12-02 00:00:00
Tasks: Benchmarking, Image Generation, Image Quality Assessment, Object

In this paper, we conduct a study on the state-of-the-art methods for text-to-image synthesis and propose a framework to evaluate these methods. We consider syntheses where an image contains a single or multiple objects. Our study outlines several issues in the current evaluation pipeline: (i) for image quality assessment, a commonly used metric, e.g., Inception Score (IS), is often either miscalibrated for the single-object case or misused for the multi-object case; (ii) for text relevance and object accuracy assessment, there is an overfitting phenomenon in the existing R-precision (RP) and Semantic Object Accuracy (SOA) metrics, respectively; (iii) for multi-object case, many vital factors for evaluation, e.g., object fidelity, positional alignment, counting alignment, are largely dismissed; (iv) the ranking of the methods based on current metrics is highly inconsistent with real images. To overcome these issues, we propose a combined set of existing and new metrics to systematically evaluate the methods. For existing metrics, we offer an improved version of IS named IS* by using temperature scaling to calibrate the confidence of the classifier used by IS; we also propose a solution to mitigate the overfitting issues of RP and SOA. For new metrics, we develop counting alignment, positional alignment, object-centric IS, and object-centric FID metrics for evaluating the multi-object case. We show that benchmarking with our bag of metrics results in a highly consistent ranking among existing methods that is well-aligned with human evaluation. As a by-product, we create AttnGAN++, a simple but strong baseline for the benchmark by stabilizing the training of AttnGAN using spectral normalization. We also release our toolbox, so-called TISE, for advocating fair and consistent evaluation of text-to-image models.
InsCLR: Improving Instance Retrieval with Self-Supervision
Authors: Zelu Deng, Yujie Zhong, Sheng Guo, Weilin Huang
Date: 2021-12-02 00:00:00
Tasks: Retrieval

This work aims at improving instance retrieval with self-supervision. We find that fine-tuning using the recently developed self-supervised (SSL) learning methods, such as SimCLR and MoCo, fails to improve the performance of instance retrieval. In this work, we identify that the learnt representations for instance retrieval should be invariant to large variations in viewpoint and background etc., whereas self-augmented positives applied by the current SSL methods can not provide strong enough signals for learning robust instance-level representations. To overcome this problem, we propose InsCLR, a new SSL method that builds on the \textit{instance-level} contrast, to learn the intra-class invariance by dynamically mining meaningful pseudo positive samples from both mini-batches and a memory bank during training. Extensive experiments demonstrate that InsCLR achieves similar or even better performance than the state-of-the-art SSL methods on instance retrieval. Code is available at https://github.com/zeludeng/insclr.
MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment
Authors: Jie Ren, Wenteng Liang, Ran Yan, Luo Mai, Shiwen Liu, Xiao Liu
Date: 2021-12-02 00:00:00
Tasks: GPU

Large-scale Bundle Adjustment (BA) requires massive memory and computation resources which are difficult to be fulfilled by existing BA libraries. In this paper, we propose MegBA, a GPU-based distributed BA library. MegBA can provide massive aggregated memory by automatically partitioning large BA problems, and assigning the solvers of sub-problems to parallel nodes. The parallel solvers adopt distributed Precondition Conjugate Gradient and distributed Schur Elimination, so that an effective solution, which can match the precision of those computed by a single node, can be efficiently computed. To accelerate BA computation, we implement end-to-end BA computation using high-performance primitives available on commodity GPUs. MegBA exposes easy-to-use APIs that are compatible with existing popular BA libraries. Experiments show that MegBA can significantly outperform state-of-the-art BA libraries: Ceres (41.45$\times$), RootBA (64.576$\times$) and DeepLM (6.769$\times$) in several large-scale BA benchmarks. The code of MegBA is available at https://github.com/MegviiRobot/MegBA.
3rd Place Solution for NeurIPS 2021 Shifts Challenge: Vehicle Motion Prediction
Authors: Ching-Yu Tseng, Po-Shao Lin, Yu-Jia Liou, Kuan-Chih Huang, Winston H. Hsu
Date: 2021-12-02 00:00:00
Tasks: motion prediction

Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift is a competition held by NeurIPS 2021. The objective of this competition is to search for methods to solve the motion prediction problem in cross-domain. In the real world dataset, It exists variance between input data distribution and ground-true data distribution, which is called the domain shift problem. In this report, we propose a new architecture inspired by state of the art papers. The main contribution is the backbone architecture with self-attention mechanism and predominant loss function. Subsequently, we won 3rd place as shown on the leaderboard.