Reviewerly/exHarmony huggingface dataset:
Cascaded Cross-Modal Transformer for Request and Complaint Detection

We propose a novel cascaded cross-modal transformer (CCMT) that combines speech and text transcripts to detect customer requests and complaints in phone conversations. Our approach leverages a multimodal paradigm by transcribing the speech using automatic speech recognition (ASR) models and translating the transcripts into different languages. Subsequently, we combine language-specific BERT-based models with Wav2Vec2.0 audio features in a novel cascaded cross-attention transformer model. We apply our system to the Requests Sub-Challenge of the ACM Multimedia 2023 Computational Paralinguistics Challenge, reaching unweighted average recalls (UAR) of 65.41% and 85.87% for the complaint and request classes, respectively.
When Masked Image Modeling Meets Source-free Unsupervised Domain Adaptation: Dual-Level Masked Network for Semantic Segmentation

Source-Free domain adaptive Semantic Segmentation (SFSS) aims to transfer knowledge from source domain to the target domain with only pre-trained source segmentation model and the unlabeled target dataset. Only a few works have been researched for SFSS, relying on entropy minimization, pseudo-labeling. Nevertheless, due to the domain bias, these methods tend to suffering from the confusion of classes with a similar visual appearance in different domains. To address the above issue, we propose to enhance discriminability towards target samples with masked image modeling to model spatial context relations as additional recognition clues. Specifically, we design a novel Dual-Level Masked Consistency method, which explicitly encourages the model to learn comprehensive context relations, i.e. patch-wise context and channel-wise context, on the target domain. By randomly masking target images and forcing the model to reconstruct predictions of the entire image with left unmasked part, the model has to make full use of spatially contextual information. To take a step further, we propose a novel masking strategy considering both local context and global context information by applying patch-wise masking on image patches and channel-wise masking on latent features. Notably, patch-wise context learning and channel-wise context learning can complement each other. Extensive experiments demonstrate the effectiveness of our proposed method and our method achieves state-of-the-art performance on two synthetic-to-real benchmarks: GTA5→Cityscapes and SYNTHIA→Cityscapes.
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to image contents such as the background, which are frequently occurring but relatively unimportant in the training dataset. Furthermore, current methods heavily rely on a few dominant forgery regions and may ignore other equally important regions, leading to inadequate uncovering of forgery cues.
Frequency Representation Integration for Camouflaged Object Detection

Recent camouflaged object detection (COD) approaches have been proposed to accurately segment objects blended into surroundings. The most challenging and critical issue in COD is to find out the lines of demarcation between objects and background in the camouflage environment. Because of the similarity between the target object and the background, these lines are difficult to be found accurately. However, these are easy to be observed in different frequency components of the image. To this end, in this paper we rethink COD from the perspective of frequency components and propose a Frequency Representation Integration Network to mine informative cues from them. Specifically, we obtain high-frequency components from the original image by Laplacian pyramid-like decomposition, and then respectively send the image to a transformer-based encoder and frequency components to a tailored CNN-based Residual Frequency Array Encoder. Besides, we utilize the multi-head self-attention in transformer encoder to capture low-frequency signals, which can effectively parse the overall contextual information of camouflage scenes. We also design a Frequency Representation Reasoning Module, which progressively eliminates discrepancies between differentiated frequency representations and integrates them by modeling their point-wise relations. Moreover, to further bridge different frequency representations, we introduce the image reconstruction task to implicitly guide their integration. Sufficient experiments on three widely-used COD benchmark datasets demonstrate that our method surpasses existing state-of-the-art methods by a large margin.
Improving Human-Object Interaction Detection via Virtual Image Learning

Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects, which plays a curtail role in high-level semantic understanding tasks. However, most works pursue designing better architectures to learn overall features more efficiently, while ignoring the long-tail nature of interaction-object pair categories. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL). Firstly, a novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images. In this stage, virtual images are generated based on prompts with specific characterizations and selected by multi-filtering processes. Secondly, we use both virtual and real images to train the model with the teacher-student framework. Considering the initial labels of some virtual images are inaccurate and inadequate, we devise an Adaptive Matching-and-Filtering (AMF) module to construct pseudo-labels. Our method is independent of the internal structure of HOI detectors, so it can be combined with off-the-shelf methods by training merely 10 additional epochs. With the assistance of our method, multiple methods obtain significant improvements, and new state-of-the-art results are achieved on two benchmarks.
360RVW: Fusing Real 360° Videos and Interactive Virtual Worlds

We propose a system to generate 360° realistic virtual worlds (360RVW) for the interactive spatial exploration of omnidirectional street-view videos. Our 360RVW enables users to explore photorealistic scenes with digital avatars, and interact with others. To create the virtual worlds our system only requires 360° videos with annotations of the start and end camera coordinate as input. We first detect street intersections to divide the input videos and remove the camera operator from the recordings using a video completion technique. Next, we analyze the 3D structure of the scene using semantic segmentation to define walkable areas. Finally, we render the environment using an ellipsoid projection surface to achieve a more realistic integration of the avatar into real-world 360° videos. The whole process is largely automated, enabling users to produce realistic and interactive virtual worlds without specialized skills or time-consuming manual interventions.
Self-Relational Graph Convolution Network for Skeleton-Based Action Recognition

Using a Graph convolution network (GCN) for constructing and aggregating node features has been helpful for skeleton-based action recognition. The strength of the nodes' relation of an action sequence distinguishes it from other actions. This work proposes a novel spatial module called Multi-scale self-relational graph convolution (MS-SRGC) for dynamically modeling joint relations of action instances. Modeling the joints' relations is crucial in determining the spatial distinctiveness between skeleton sequences; hence MS-SRGC shows effectiveness for activity recognition. We also propose a Hybrid multi-scale temporal convolution network (HMS-TCN) that captures different ranges of time steps along the temporal dimension of the skeleton sequence. In addition, we propose a Spatio-temporal blackout (STB) module that randomly zeroes some continue frames for selected strategic joint groups. We sequentially stack our spatial (MS-SRGC) and temporal (HMS-TCN) modules to form a Self-relational graph convolution network (SR-GCN) block, which we use to construct our SR-GCN model. We append our STB on the SR-GCN model top for the randomized operation. With the effectiveness of ensemble networks, we perform extensive experiments on single and multiple ensembles. Our results beat the state-of-the-art methods on the NTU RGB-D, NTU RGB-D 120, and Northwestern-UCLA datasets.
Enhancing Domain-Invariant Parts for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) aims to recognize unseen classes, which are not observable during training, with auxiliary semantic information, e.g., attributes. Attributes play an important role in GZSL due to their ability to provide rich semantic information and visual guidance. In this paper, we will review the characteristics of attributes. First, the visual appearance of the same attribute varies greatly between different classes, which leads to the projection domain shift problem and inaccurate attribute localization. Second, the predefined semantic prototypes are not able to faithfully represent the credible visual information of each sample, which leads to suboptimal visual-semantic alignment. Therefore, we propose a novel framework called Enhancing doMain-invariant Parts (EMP) to solve above issues. To be specific, we mitigate the projection domain shift problem by the feature disentanglement technology in domain generalization, which can disentangle the attribute-based visual features into domain-invariant and domain-specific parts. So that the model can pay more attention to the essential parts of attributes rather than using all the information learned from the seen classes to identify the unseen classes. Then we achieve better visual-semantic alignment by refining the predefined semantic prototypes with the extracted credible visual information from corresponding sample. To make the extracted visual information be more in line with the image content rather than overfitting the semantic prototype, we draw on the idea of self-paced learning to help the model learn attributes from easy to complex. Experimental results show that our method achieves a new state-of-the-art performance on three generalized zero-shot learning benchmarks.
QA-CLIMS: Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model's ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt Engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets.
Self-supervised Video Summarization Guided by Semantic Inverse Optimal Transport

Video summarization is a critical task in video analysis that aims to create a brief yet informative summary of the original video (i.e., a set of keyframes) while retaining its primary content. Supervised summarization methods rely on time-consuming keyframe labeling and thus often suffer from the insufficiency issue of training data. In contrast, the performance of unsupervised summarization methods is often unsatisfactory due to the lack of semantically-meaningful guidance on the keyframe selection. In this study, we propose a novel self-supervised video summarization framework with the help of computational optimal transport techniques. Specifically, we generate textual descriptions from video shots and learn the projection from the textual embeddings to the visual ones together with an optimal transport plan between them via solving an inverse optimal transport problem. We propose an alternating optimization algorithm to solve this problem efficiently and design an effective mechanism in the algorithm to avoid trivial solutions. Given the optimal transport plan and the underlying distance between the projected textual embeddings and the visual ones, we synthesize pseudo-significance scores for video frames and leverage the scores as offline supervision to train a keyframe selector. Without subjective and error-prone manual annotations, the proposed framework surpasses previous unsupervised methods in producing high-quality results for generic and instructional video summarization tasks, whose performance even is comparable to those supervised competitors. The code is available at https://github.com/Dixin-s-Lab/Video-Summary-IOT.
Concerto: Client-server Orchestration for Real-Time Video Analytics

The delay to obtain analysis results is an important metric in video analytics. Previous work has focused on reducing frame transmission and inference delay to optimize total delay. However, network fluctuations can cause frames to arrive at the backend simultaneously, leading to backend queuing delays. To address this issue, we propose Concerto, a joint front-and backend video analytics pipeline that optimizes both network transmission and backend queuing delays. The backend controls the frame queue, accelerating or skipping inference as needed to mitigate backend queuing delay. The frontend considers both delays when configuring frames to send, resulting in better total delay. Experiments show that Concerto significantly reduces backend queuing delay with minimal loss of accuracy.
Sparse Sharing Relation Network for Panoptic Driving Perception

Efficient and accurate perception system is critical for autonomous driving, including traffic object detection, drivable area segmentation, and lane detection. Most previous works do not consider the spatial and semantic cues in traffic scenes. In this paper, we propose a novel multi-task learning network to exploit these priors. Specifically, to model the co-occurrence and spatial relationships of traffic objects, we propose to use a Graph Convolutional Network (GCN) block operating on the patches of feature maps. It enables adaptive discovery and incorporation of semantic and spatial relationships in the feature space. Furthermore, we propose a sub-feature sharing method to mitigate negative transfer in multi-task learning. On the basis of a fully shared base network, we split the feature space of different tasks along the channel dimension, resulting in the shared and private features for each task. It allows the network parameters to be selectively updated by different tasks during training. Experimental results on the challenging BDD100K dataset demonstrate that our proposed approach gets consistent improvement with fewer parameters, and achieves new state-of-the-art performance in terms of accuracy and speed.
Train One, Generalize to All: Generalizable Semantic Segmentation from Single-Scene to All Adverse Scenes

Unsupervised Domain Adaptation (UDA) for semantic segmentation has received widespread attention for its ability to transfer knowledge from the source to target domains without a high demand for annotations. However, semantic segmentation under adverse conditions still poses significant challenges for autonomous driving, as bad weather observation data may introduce unforeseeable problems. Although previous UDA works are devoted to adverse scene tasks, their adaptation process is redundant. For instance, unlabeled snow scene training data is a must for the model to achieve fair segmentation performance in snowy scenarios. We propose calling this type of adaptation process the Single to Single (STS) strategy. Clearly, STS is time-consuming and may show weaknesses in some comprehensive scenes, such as a night scene of sleet. Motivated by the concept of Domain Generalization (DG), we propose the Single to All (STA) model. Unlike DG, which trains models on one or multiple source domains without target domains, the STA model is based on UDA and employs one source domain, one target domain, and one introduced domain to achieve generalization to all adverse conditions by training on a single-scene dataset. Specifically, the STA model is advantageous as it learns from the source domain, reserves the style factors via a Reservation domain, and adapts the unified factors by the Randomization module. An Output Space Refusion module is also further incorporated to strengthen STA. Our STA achieves state-of-the-art performance in the Foggy Driving benchmark and demonstrates great domain generalizability in all conditions of the ACDC and Foggy Zurich benchmarks.
Exploring the Adversarial Robustness of Video Object Segmentation via One-shot Adversarial Attacks

Video object segmentation (VOS) is a fundamental task for computer vision and multimedia. Despite significant progress of VOS models in recent works, there has been little research on the VOS models' adversarial robustness, posing serious security risks in the VOS models' practical applications (e.g., autonomous driving and video surveillance). Adversarial robustness refers to the ability of the model to resist malicious attacks on adversarial examples. To address this gap, we propose a one-shot adversarial robustness evaluation framework (i.e., the adversary only perturbs the first frame) for VOS models, including white-box and black-box attacks. For white-box attacks, we introduce Objective Attention (OA) and Boundary Attention (BA) mechanisms to enhance the attention of attack on objects from both pixel and object levels while mitigating issues such as multi-objects attack imbalance, attack bias towards the background, and boundary reservation. For black-box attacks, we propose the Video Diverse Input (VDI) module, which utilizes data augmentation to simulate historical information, improving our method's black-box transferability. We conduct extensive experiments to evaluate the adversarial robustness of VOS models with different structures. Our experimental results reveal that existing VOS models are more vulnerable to our attacks (both white-box and black-box) compared to other state-of-the-art attacks. We further analyze the influence of different designs (e.g., memory and matching mechanisms) on adversarial robustness. Finally, we provide insights for designing more secure VOS models in the future.
Lite-MKD: A Multi-modal Knowledge Distillation Framework for Lightweight Few-shot Action Recognition

Existing few-shot action recognition methods have placed primary focus on improving the recognition accuracy while neglecting another important indicator in practical scenarios, i.e., model efficiency. In this paper, we make the first attempt and propose a Lightweight Multi-modal Knowledge Distillation framework (Lite-MKD) for few-shot action recognition. In this framework, the teacher model conducts multi-modal learning to achieve a comprehensive fusion of the optical flow, depth, and appearance features of human movements, thus achieving a more robust representation of actions. The student model is utilized to learn to recognize actions from the single RGB modality at a lower computational cost under the guidance of the teacher. To fully explore and integrate multi-modal information, a hierarchical Multi-modal Fusion Module (MFM) is introduced in the teacher model. Besides, a multi-level Distinguish-to-Mimic (D2M) knowledge distillation component is proposed for the student model. D2M improves the ability of the student model to mimic the action classification probabilities of the teacher model by enhancing the distinguishability of the student model for different video categories in the support set. Extensive experiments on three action recognition datasets Kinetics, HMDB51, and UCF101 demonstrate our framework's effectiveness and stable generalization ability. With a much more lightweight network for inference, we achieve comparable performance to previous state-of-the-art methods. Our source code is available at https://github.com/HuiGuanLab/Lite-MKD
Language-Guided Visual Aggregation Network for Video Question Answering

Video Question Answering (VideoQA) aims to comprehend intricate relationships, actions, and events within video content, as well as the inherent links between objects and scenes, to answer text-based questions accurately. Transferring knowledge from the cross-modal pre-trained model CLIP is a natural approach, but its dual-tower structure hinders fine-grained modality interaction, posing challenges for direct application to VideoQA tasks. To address this issue, we introduce a Language-Guided Visual Aggregation (LGVA) network. It employs CLIP as an effective feature extractor to obtain language-aligned visual features with different granularities and avoids resource-intensive video pre-training. The LGVA network progressively aggregates visual information in a bottom-up manner, focusing on both regional and temporal levels, and ultimately facilitating accurate answer prediction. More specifically, it employs local cross-attention to combine pre-extracted question tokens and region embeddings, pinpointing the object of interest in the question. Then, graph attention is utilized to aggregate regions at the frame level and integrate additional captions for enhanced detail. Following this, global cross-attention is used to merge sentence and frame-level embeddings, identifying the video segment relevant to the question. Ultimately, contrastive learning is applied to optimize the similarities between aggregated visual and answer embeddings, unifying upstream and downstream tasks. Our method conserves resources by avoiding large-scale video pre-training and simultaneously demonstrates commendable performance on the NExT-QA, MSVD-QA, MSRVTT-QA, TGIF-QA, and ActivityNet-QA datasets, even outperforming some end-to-end trained models. Our code is available at https://github.com/ecoxial2007/LGVA_VideoQA.