Reviewerly/exHarmony huggingface dataset:
Concept Correlation and Its Effects on Concept-Based Models

Concept-based learning approaches for image classification, such as Concept Bottleneck Models, aim to enable interpretation and increase robustness by directly learning high-level concepts which are used for predicting the main class. They achieve competitive test accuracies compared to standard end-to-end models. However, with multiple concepts per image and binary concept annotations (without concept localization), it is not evident if the output of the concept model is truly based on the predicted concepts or other features in the image. Additionally, high correlations between concepts would allow the model to predict a concept with high test accuracy by simply using a correlated concept as a proxy. In this paper, we analyze these correlations between concepts in the CUB and GTSRB datasets and propose methods beyond test accuracy for evaluating their effects on the performance of a concept-based model trained on this data. To this end, we also perform a more detailed analysis on the effects of concept correlation using synthetically generated datasets of 3D shapes. We see that high concept correlation increases the risk of a model’s inability to distinguish these concepts. Yet simple techniques, like loss weighting, show promising initial results for mitigating this issue.
Normality Guided Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

Weakly supervised Video Anomaly Detection (wVAD) aims to distinguish anomalies from normal events based on video-level supervision. Most existing works utilize Multiple Instance Learning (MIL) with ranking loss to tackle this task. These methods, however, rely on noisy predictions from a MIL-based classifier for target instance selection in ranking loss, degrading model performance. To overcome this problem, we propose Normality Guided Multiple Instance Learning (NG-MIL) framework, which encodes diverse normal patterns from noise-free normal videos into prototypes for constructing a similarity-based classifier. By ensembling predictions of two classifiers, our method could refine the anomaly scores, reducing training instability from weak labels. Moreover, we introduce normality clustering and normality guided triplet loss constraining inner bag instances to boost the effect of NG-MIL and increase the discriminability of classifiers. Extensive experiments on three public datasets (ShanghaiTech, UCF-Crime, XD-Violence) demonstrate that our method is comparable to or better than existing weakly supervised methods, achieving state-of-the-art results.
Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition

Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes. It has also been shown that 2D Convolutional Neural Networks (CNNs) tend to be biased toward texture rather than shape in still image recognition tasks [19], in contrast to humans. Taken together, this raises suspicion that large video models partly learn spurious spatial texture correlations rather than to track relevant shapes over time to infer generalizable semantics from their movement. A natural way to avoid parameter explosion when learning visual patterns over time is to make use of recurrence. Biological vision consists of abundant recurrent circuitry, and is superior to computer vision in terms of domain shift generalization. In this article, we empirically study whether the choice of low-level temporal modeling has consequences for texture bias and cross-domain robustness. In order to enable a light-weight and systematic assessment of the ability to capture temporal structure, not revealed from single frames, we provide the Temporal Shape (TS) dataset, as well as modified domains of Diving48 allowing for the investigation of spatial texture bias in video models. The combined results of our experiments indicate that sound physical inductive bias such as recurrence in temporal modeling may be advantageous when robustness to domain shift is important for the task.
Real-time Concealed Weapon Detection on 3D Radar Images for Walk-through Screening System

This paper presents a framework for real-time concealed weapon detection (CWD) on 3D radar images for walk-through screening systems. The walk-through screening system aims to ensure security in crowded areas by performing CWD on walking persons, hence it requires an accurate and real-time detection approach. To ensure accuracy, a weapon needs to be detected irrespective of its 3D orientation, thus we use the 3D radar images as detection input. For achieving real-time, we reformulate classic U-Net based segmentation networks to perform 3D detection tasks. Our 3D segmentation network predicts peak-shaped probability map, instead of voxel-wise masks, to enable position inference by elementary peak detection operation on the predicted map. In the peak-shaped probability map, the peak marks the weapon’s position. So, weapon detection task translates to peak detection on the probability map. A Gaussian function is used to model weapons in the probability map. We experimentally validate our approach on realistic 3D radar images obtained from a walk-through weapon screening system prototype. Extensive ablation studies verify the effectiveness of our proposed approach over existing conventional approaches. The experimental results demonstrate that our proposed approach can perform accurate and real-time CWD, thus making it suitable for practical applications of walk-through screening.
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification

We propose MASTAF, a Model-Agnostic Spatio-Temporal Attention Fusion network for few-shot video classification. MASTAF takes input from a general video spatial and temporal representation,e.g., using 2D CNN, 3D CNN, and Video Transformer. Then, to make the most of such representations, we use self- and cross-attention models to highlight the critical spatio-temporal region to increase the inter-class variations and decrease the intra-class variations. Last, MASTAF applies a lightweight fusion network and a nearest neighbor classifier to classify each query video. We demonstrate that MASTAF improves the state-of-the-art performance on three few-shot video classification benchmarks(UCF101, HMDB51, and Something-Something-V2), e.g., by up to 91.6%, 69.5%, and 60.7% for five-way one-shot video classification, respectively.
Spatio-Temporal Action Detection Under Large Motion

Current methods for spatio-temporal action tube detection often extend a bounding box proposal at a given key-frame into a 3D temporal cuboid and pool features from nearby frames. However, such pooling fails to accumulate meaningful spatio-temporal features if the position or shape of the actor shows large 2D motion and variability through the frames, due to large camera motion, large actor shape deformation, fast actor action and so on. In this work, we aim to study the performance of cuboid-aware feature aggregation in action detection under large action. Further, we propose to enhance actor feature representation under large motion by tracking actors and performing temporal feature aggregation along the respective tracks. We define the actor motion with intersection-over-union (IoU) between the boxes of action tubes/tracks at various fixed time scales. The action having a large motion would result in lower IoU over time, and slower actions would maintain higher IoU. We find that track-aware feature aggregation consistently achieves a large improvement in action detection performance, especially for actions under large motion compared to cuboid-aware baseline. As a result, we also report state-of-the-art on the large-scale MultiSports dataset.
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning-Based Synthetic Face Detection

Can deep learning models achieve greater generalization if their training is guided by reference to human perceptual abilities? And how can we implement this in a practical manner? This paper proposes a training strategy to ConveY Brain Oversight to Raise Generalization (CYBORG). This new approach incorporates human-annotated saliency maps into a loss function that guides the model’s learning to focus on image regions that humans deem salient for the task. The Class Activation Mapping (CAM) mechanism is used to probe the model’s current saliency in each training batch, juxtapose this model saliency with human saliency, and penalize large differences. Results on the task of synthetic face detection, selected to illustrate the effectiveness of the approach, show that CYBORG leads to significant improvement in accuracy on unseen samples consisting of face images generated from six Generative Adversarial Networks across multiple classification network architectures. We also show that scaling to even seven times the training data, or using non-human-saliency auxiliary information, such as segmentation masks, and standard loss cannot beat the performance of CYBORG-trained models. As a side effect of this work, we observe that the addition of explicit region annotation to the task of synthetic face detection increased human classification accuracy. This work opens a new area of research on how to incorporate human visual saliency into loss functions in practice. All data, code and trained models used in this work are offered with this paper.
Training Auxiliary Prototypical Classifiers for Explainable Anomaly Detection in Medical Image Segmentation

Machine learning-based algorithms using fully convolutional networks (FCNs) have been a promising option for medical image segmentation. However, such deep networks silently fail if input samples are drawn far from the training data distribution, thus causing critical problems in automatic data processing pipelines. To overcome such out-of-distribution (OoD) problems, we propose a novel OoD score formulation and its regularization strategy by applying an auxiliary add-on classifier to an intermediate layer of an FCN, where the auxiliary module is helfpul for analyzing the encoder output features by taking their class information into account. Our regularization strategy train the module along with the FCN via the principle of outlier exposure so that our model can be trained to distinguish OoD samples from normal ones without modifying the original network architecture. Our extensive experiment results demonstrate that the proposed approach can successfully conduct effective OoD detection without loss of segmentation performance. In addition, our module can provide reasonable explanation maps along with OoD scores, which can enable users to analyze the reliability of predictions.
Watching the News: Towards VideoQA Models that can Read

Video Question Answering methods focus on common-sense reasoning and visual cognition of objects or persons and their interactions over time. Current VideoQA approaches ignore the textual information present in the video. Instead, we argue that textual information is complementary to the action and provides essential contextualisation cues to the reasoning process. To this end, we propose a novel VideoQA task that requires reading and understanding the text in the video. To explore this direction, we focus on news videos and require QA systems to comprehend and answer questions about the topics presented by combining visual and textual cues in the video. We introduce the "NewsVideoQA" dataset that comprises more than 8, 600 QA pairs on 3, 000+ news videos obtained from diverse news channels from around the world. We demonstrate the limitations of current Scene Text VQA and VideoQA methods and propose ways to incorporate scene text information into VideoQA methods.
Addressing Feature Suppression in Unsupervised Visual Representations

Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive learning is susceptible to feature suppression – i.e., it may discard important information relevant to the task of interest, and learn irrelevant features. Past work has addressed this limitation via handcrafted data augmentations that eliminate irrelevant information. This approach however does not work across all datasets and tasks. Further, data augmentations fail in addressing feature suppression in multi-attribute classification when one attribute can suppress features relevant to other attributes. In this paper, we analyze the objective function of contrastive learning and formally prove that it is vulnerable to feature suppression. We then present Predictive Contrastive Learning (PrCL), a framework for learning unsupervised representations that are robust to feature suppression. The key idea is to force the learned representation to predict the input, and hence prevent it from discarding important information. Extensive experiments verify that PrCL is robust to feature suppression and outperforms state-of-the-art contrastive learning methods on a variety of datasets and tasks.
Automated Line Labelling: Dataset for Contour Detection and 3D Reconstruction

Understanding the finer details of a 3D object, its contours, is the first step toward a physical understanding of an object. Many real-world application domains require adaptable 3D object shape recognition models, usually with little training data. For this purpose, we develop the first automatically generated contour labeled dataset, bypassing manual human labeling. Using this dataset, we study the performance of current state-of-the-art instance segmentation algorithms on detecting and labeling the contours. We produce promising visual results with accurate contour prediction and labeling. We demonstrate that our finely labeled contours can help downstream tasks in computer vision, such as 3D reconstruction from a 2D image.
A URL-Based Social Semantic Attacks Detection With Character-Aware Language Model

Social engineering attacks rely on human errors and behavioral choices. The semantic attack, a subcategory of social engineering attacks, utilizes behavioral or cosmetic deception vectors (e.g., attacker creates a malicious website that looks like and behaves like the legitimate one). The most common types of social semantic attacks include phishing, spamming, defacement, and malware attacks. We investigate the feasibility of developing URL-based social semantic attack detection models utilizing character-aware language models. Specifically, we developed three types of models: long short-term memory (LSTM)-based detection model, convolutional neural network (CNN)-based detection model, and CharacterBERT-based detection model. We benchmarked performances of different models for different attacks. Using the characterBERT-based detection model, the overall evaluation recorded a high detection accuracy of 99.65% by averaging the results of performing a 5-fold cross-validation. Considering the model performance per class, the CharacterBERT model ranked the best model among our 3 models in detecting the social semantic attacks, reaching best accuracy of 99.90% in detecting defacement attack.
Improving Diversity with Adversarially Learned Transformations for Domain Generalization

To be successful in single source domain generalization (SSDG), maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Recent success in SSDG comes from methods that pre-specify diversity inducing image augmentations during training, so that it may lead to better generalization on new domains. However, naïve pre-specified augmentations are not always effective, either because they cannot model large domain shift, or be-cause the specific choice of transforms may not cover the types of shift commonly occurring in domain generalization. To address this issue, we present a novel framework called ALT: adversarially learned transformations, that uses an adversary neural network to model plausible, yet hard image transformations that fool the classifier. ALT learns image transformations by randomly initializing the adversary net-work for each batch and optimizing it for a fixed number of steps to maximize classification error. The classifier is trained by enforcing a consistency between its predictions on the clean and transformed images. With extensive empirical analysis, we find that this new form of adversarial transformations achieves both objectives of diversity and hardness simultaneously, outperforming all existing techniques on competitive benchmarks for SSDG. We also show that ALT can seamlessly work with existing diversity modules to produce highly distinct, and large transformations of the source domain leading to state-of-the-art performance. Code: https://github.com/tejas-gokhale/ALT
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks

In "vision and language" problems, multimodal inputs are simultaneously processed for combined visual and textual understanding for image-text embedding. In this paper, we discuss the necessity of considering the difference between the feature space and the distribution when performing multimodal learning. We deal with this problem through deep learning and a generative model approach. We introduce a novel network, GAFNet (Global Attention Fourier Net), which learns through large-scale pre-training over three image-text datasets (COCO, SBU, and CC-3M), for achieving high performance on downstream vision and language tasks. We propose a GAF (Global Attention Fourier) module, which integrates multiple modalities into one latent space. GAF module is independent of the type of modality, and it allows combining shared representations at each stage. Various ways of thinking about the relationships between different modalities directly affect the model’s design. In contrast to previous research, our work considers visual grounding as a pretrainable and transferable quality instead of something that must be trained from scratch. We show that GAFNet is a versatile network that can be used for a wide range of downstream tasks. Experimental results demonstrate that our technique achieves state-of-the-art performance on multimodal classification on the CrisisMD dataset and image generation on the COCO dataset. For image-text retrieval, our technique achieves competitive performance.
StAIn: Stealthy Avenues of Attacks on Horizontally Collaborated Convolutional Neural Network Inference and Their Mitigation

With significant potential improvement in device-to-device (D2D) communication due to improved wireless link capacity (e.g., 5G and NextG systems), a collaboration of multiple edge devices (called horizontal collaboration (HC)) is becoming a reality for real-time Edge Intelligence (EI). The distributed nature of HC offers an advantage against traditional adversarial attacks because the adversary does not have access to the entire deep learning architecture (DLA). Due to the involvement of multiple untrusted edge devices in HC environment, the possibility of malicious devices cannot be eliminated. In this paper, we unearth some attacks that are very effective and stealthy even when the attacker has minimal knowledge of the DLA as is the case in HC-based DLA. We are also providing novel filtering methods to mitigate such attacks. Our novel attacks leverage local information available on output feature maps (FMs) of a targeted edge device to modify the regular adversarial attacks (e.g. Fast Gradient Signed Method (FGSM) and Jacobian-based Saliency Map Attack (JSMA)). Similarly, a customized convolutional neural network (CNN) based filter is empirically designed, developed, and tested. Four different CNN models (LeNet, CapsuleNet, MiniVGGNet, and VGG16) are used to validate the proposed attacks and defense methodologies. Our three attacks on four different CNN models (with two variations of each attack) show a substantial accuracy drop of 62% on average. The proposed filtering approach is able to mitigate the attack by recovering the actual accuracy back to 75.1% on average. To the best of our knowledge, this is the first work that investigates the security vulnerability of DLA in the HC environment, and all three of our attacks are scalable and agnostic to the partition location within the DLA.
Multi-level Contrastive Learning for Self-Supervised Vision Transformers

Recent studies aim to establish contrastive self-supervised learning (CSL) algorithms specialized for the family of Vision Transformers (ViTs) to make them function normally as ordinary convolutional-based backbones in the training progress. Despite obtaining promising performance on related downstream tasks, one compelling property of the ViTs is ignored in those approaches. As previous studies have demonstrated, vision transformers benefit from the early stage global attention mechanics, obtaining feature representations that contain information from distant patches, even in their shallow layers. Motivated by this, we present a simple yet effective framework to facilitate the self-supervised feature learning of transformer based vision architectures, namely, Multi-level Contrastive learning for Vision Transformers (MCVT). Specifically, we equip the vision transformers with individual-based (InfoNCE) and prototypical-based (ProtoNCE) contrastive loss in different stages of the architecture to capture low-level invariance and high-level invariance between views of samples, respectively. We conduct extensive experiments to demonstrate the effectiveness of the proposed method, using two well-known vision transformer backbones, on several vision downstream tasks, including linear classification, detection, and semantic segmentation.
MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification

In this paper, we present the Multi-view Extended Videos with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild. To our knowledge, MEVID represents the most-varied video person ReID dataset, spanning an extensive indoor and outdoor environment across nine unique dates in a 73-day window, various camera viewpoints, and entity clothing changes. Specifically, we label the identities of 158 unique people wearing 598 outfits taken from 8, 092 tracklets, average length of about 590 frames, seen in 33 camera views from the very-large-scale MEVA person activities dataset. While other datasets have more unique identities, MEVID emphasizes a richer set of information about each individual, such as: 4 outfits/identity vs. 2 outfits/identity in CCVID, 33 viewpoints across 17 locations vs. 6 in 5 simulated locations for MTA, and 10 million frames vs. 3 million for LS-VID. Being based on the MEVA video dataset, we also inherit data that is intentionally demographically balanced to the continental United States. To accelerate the annotation process, we developed a semi-automatic annotation framework and GUI that combines state-of-the-art real-time models for object detection, pose estimation, person ReID, and multi-object tracking. We evaluate several state-of-the-art methods on MEVID challenge problems and comprehensively quantify their robustness in terms of changes of outfit, scale, and background location. Our quantitative analysis on the realistic, unique aspects of MEVID shows that there are significant remaining challenges in video person ReID and indicates important directions for future research.