Fringe groups and organizations have a long history of using euphemisms—ordinary-sounding words with a secret meaning—to conceal what they are discussing. Nowadays, one common use of euphemisms is to evade content moderation policies enforced by social media platforms. Existing tools for enforcing policy automatically rely on keyword searches for words on a "ban list", but these are notoriously imprecise: even when limited to swearwords, they can still cause embarrassing false positives [1]. When a commonly used ordinary word acquires a euphemistic meaning, adding it to a keyword-based ban list is hopeless: consider "pot" (storage container or marijuana?) or "heater" (household appliance or firearm?) The current generation of social media companies instead hire staff to check posts manually, but this is expensive, inhumane, and not much more effective. It is usually apparent to a human moderator that a word is being used euphemistically, but they may not know what the secret meaning is, and therefore whether the message violates policy. Also, when a euphemism is banned, the group that used it need only invent another one, leaving moderators one step behind.This paper will demonstrate unsupervised algorithms that, by analyzing words in their sentence-level context, can both detect words being used euphemistically, and identify the secret meaning of each word. Compared to the existing state of the art, which uses context-free word embeddings, our algorithm for detecting euphemisms achieves 30–400% higher detection accuracies of unlabeled euphemisms in a text corpus. Our algorithm for revealing euphemistic meanings of words is the first of its kind, as far as we are aware. In the arms race between content moderators and policy evaders, our algorithms may help shift the balance in the direction of the moderators.

Self-Supervised Euphemism Detection and Identification for Content Moderation

Video understanding usually requires expensive computation that prohibits its deployment, yet videos contain significant spatiotemporal redundancy that can be exploited. In particular, operating directly on the motion vectors and residuals in the compressed video domain can significantly accelerate the compute, by not using the raw videos which demand colossal storage capacity. Existing methods approach this task as a multiple modalities problem. In this paper we are approaching the task in a completely different way; we are looking at the data from the compressed stream as a one unit clip and propose that the residual frames can replace the original RGB frames from the raw domain. Furthermore, we are using teacher-student method to aid the network in the compressed domain to mimic the teacher network in the raw domain. We show experiments on three leading datasets (HMDB51, UCF1, and Kinetics) that approach state-of-the-art accuracy on raw video data by using compressed data. Our model MFCD-Net outperforms prior methods in the compressed domain and more importantly, our model has 11X fewer parameters and 3X fewer Flops, dramatically improving the efficiency of video recognition inference. This approach enables applying neural networks exclusively in the compressed domain without compromising accuracy while accelerating performance.

/pdf/mimic-the-raw-domain-accelerating-action-recognition-in-the-1w1o4gunuq.pdf

Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain

Recent years have witnessed a growing interest in compressed video action recognition due to the rapid growth of online videos. It remarkably reduces the storage by replacing raw videos with sparsely sampled RGB frames and other compressed motion cues (motion vectors and residuals). However, existing compressed video action recognition methods face two main issues: First, the inefficiency caused by the usage of coarse-level information under full resolution, and second, the disturbing due to the noisy dynamics in motion vectors. To address the two issues, this paper proposes a dynamic spatial focus method for efficient compressed video action recognition (CoViFocus). Specifically, we first use a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors. Then the selected patch pair will be processed by a high-capacity two-stream deep model for the final prediction. Such a patch selection strategy crops out the irrelevant motion noise in motion vectors, as well as reduces the spatial redundancy of the inputs, leading to the high efficiency of our method in the compressed domain. Moreover, we found that the motion vectors can help our method to address the possibly happened <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static-issue , which means that the focus patches get stuck at some regions related to static objects rather than target actions, which further improves our method. Extensive results on both the HMDB-51 and UCF-101 datasets demonstrate the effectiveness and efficiency of our method in compressed video action recognition tasks.

Dynamic Spatial Focus for Efficient Compressed Video Action Recognition

With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams. Recognising actions and detecting action transitions within an input video are challenging but necessary tasks for applications that require real-time human-machine interaction. By reviewing a large body of recent related work in the literature, we thoroughly analyse, explain and compare action segmentation methods and provide details on the feature extraction and learning strategies that are used on most state-of-the-art methods. We cover the impact of the performance of object detection and tracking techniques on human action segmentation methodologies. We investigate the application of such models to real-world scenarios and discuss several limitations and key research directions towards improving interpretability, generalisation, optimisation, and deployment.

https://dl.acm.org/doi/pdf/10.1145/3587931

Continuous Human Action Recognition for Human-Machine Interaction: A Review

Fine-grained human activities recognition focuses on recognizing event- or action-level activities, which enables a new set of Internet-of-Things (IoT) applications such as behavior analysis. Prior work on fine-grained human activities recognition relies on supervised sensing, which makes the fine-grained labeling labor-intensive and difficult to scale up. On the other hand, it is much more practical to collect coarse-grained label at the level of activity of daily living (e.g., cooking, working), especially for real-world IoT systems. In this paper, we present a framework that learns fine-grained human activities recognition with coarse-grained labeled and a small amount of fine-grained labeled multi-modal data. Our system leverages the implicit physical knowledge on the hierarchy of the coarse- and fine-grained labels and conducts data-driven hierarchical learning that take into account the coarse-grained supervised prediction for fine-grained semi-supervised learning. We evaluated our framework and CFR-TSVM algorithm on the data gathered from real-world experiments. Results show that our CFR-TSVM achieved an 81% recognition accuracy over 10 fine-grained activities, which reduces the prediction error of the semi-supervised learning baseline TSVM by half.

https://dl.acm.org/doi/pdf/10.1145/3410530.3414320

Fine-grained activities recognition with coarse-grained labeled multi-modal data

Fine-grained image classification and retrieval become topical in both computer vision and information retrieval. In real-life scenarios, fine-grained tasks tend to appear along with coarse-grained tasks when the observed object is coming closer. However, in previous works, the combination of fine-grained and coarse-grained tasks was often ignored. In this paper, we define a new problem called coarse-to-fine grained classification (C2FGC) which aims to recognize the classes of objects in multiple resolutions (from low to high). To solve this problem, we propose a novel Multi-linear Pooling with Hierarchy (MLPH) model. Specifically, we first design a multi-linear pooling module to include both trilinear and bilinear pooling, and then formulate the coarse-grained and fine-grained tasks within a unified framework. Experiments on two benchmark datasets show that our model achieves state-of-the-art results.

Coarse-to-Fine Grained Classification

Self-Supervised monocular visual odometry (VO) is often cast into a view synthesis problem based on depth and camera pose estimation. One of the key challenges is to accurately and robustly estimate depth with occlusions and moving objects in the scene. Existing methods simply detect and mask out regions of occlusions locally by several convolutional layers, and then perform only partial view synthesis in the rest of the image. However, occlusion and moving object detection is an unsolved problem itself which requires global layout information. Inaccurate detection inevitably results in incorrect depth as well as pose estimation. In this work, instead of locally detecting and masking out occlusions and moving objects, we propose to alleviate their negative effects on monocular VO implicitly but more effectively from two global perspectives. First, a multi-scale non-local attention module, consisting of both intra-stage augmented attention and cascaded across-stage attention, is proposed for robust depth estimation given occlusions, alleviating the impacts of occlusions via global attention modeling. Second, adversarial learning is introduced in view synthesis for monocular VO. Unlike existing methods that use pixel-level losses on the quality of synthesized views, we enforce the synthetic view to be indistinguishable from the real one at the scene-level. Such a global constraint again helps cope with occluded and moving regions. Extensive experiments on the KITTI dataset show that our approach achieves new state-of-the-art in both pose estimation and depth recovery.

/pdf/a-global-occlusion-aware-approach-to-self-supervised-4l4vmsbq5v.pdf

A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry

Detecting subtle intention such as deception and subtext of a person in a long dialog video, or implicit intention detection (IID), is a challenging problem. The transcript (textual cues) often reveals little, so audio-visual cues including voice tone as well as facial and body behaviour are the main focuses for automated IID. Contextual cues are also crucial, since a person’s implicit intentions are often correlated and context-dependent when the person moves from one question-answer pair to the next. However, no such dataset exists which contains fine-grained questionanswer pair (video segment) level annotation. The first contribution of this work is thus a new benchmark dataset, called Vid2Int-Deception to fill this gap. A novel multigrain representation model is also proposed to capture the subtle movement changes of eyes, face, and body (relevant for inferring intention) from a long dialog video. Moreover, to model the temporal correlation between the implicit intentions across video segments, we propose a Videoto-Intention network (Vid2Int) based on attentive recurrent neural network (RNN). Extensive experiments show that our model achieves state-of-the-art results.

/pdf/vid2int-detecting-implicit-intention-from-long-dialog-videos-23vbl7zglt.pdf

Vid2Int: Detecting Implicit Intention from Long Dialog Videos

Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing stairs. Recent works focus on action recognition with deep neural networks that achieve state-of-the-art results in need of high-performance platforms. Despite the fast development of mobile computing, video action recognition on mobile devices has not been fully discussed. In this paper, we focus on the novel mobile video action recognition task, where only the computational capabilities of mobile devices are accessible. Instead of raw videos with huge storage, we choose to extract multiple modalities (including I-frames, motion vectors, and residuals) directly from compressed videos. By employing MobileNetV2 as backbone, we propose a novel Temporal Trilinear Pooling (TTP) module to fuse the multiple modalities for mobile video action recognition. In addition to motion vectors, we also provide a temporal fusion method to explicitly induce the temporal context. The efficiency test on a mobile device indicates that our model can perform mobile video action recognition at about 40FPS. The comparative results on two benchmarks show that our model outperforms existing action recognition methods in model size and time consuming, but with competitive accuracy.

Mobile Video Action Recognition.

Most existing action recognition models are large convolutional neural networks that work only with raw RGB frames as input. However, practical applications require lightweight models that directly process compressed videos. In this work, for the first time, such a model is developed, which is lightweight enough to run in real-time on embedded AI devices without sacrifices in recognition accuracy. A new Aligned Temporal Trilinear Pooling (ATTP) module is formulated to fuse three modalities in a compressed video. To remedy the weaker motion vectors (compared to optical flow computed from raw RGB streams) for representing dynamic content, we introduce a temporal fusion method to explicitly induce the temporal context, as well as knowledge distillation from a model trained with optical flows via feature alignment. Compared to existing compressed video action recognition models, it is much more compact and faster thanks to adopting a lightweight CNN backbone.

/pdf/lightweight-action-recognition-in-compressed-videos-3jgoyka85q.pdf

Lightweight Action Recognition in Compressed Videos

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