Reviewerly/exHarmony huggingface dataset:
mmLayout: Multi-grained MultiModal Transformer for Document Understanding

Recent efforts of multimodal Transformers have improved Visually Rich Document Understanding (VrDU) tasks via incorporating visual and textual information. However, existing approaches mainly focus on fine-grained elements such as words and document image patches, making it hard for them to learn from coarse-grained elements, including natural lexical units like phrases and salient visual regions like prominent image regions. In this paper, we attach more importance to coarse-grained elements containing high-density information and consistent semantics, which are valuable for document understanding. At first, a document graph is proposed to model complex relationships among multi-grained multimodal elements, in which salient visual regions are detected by a cluster-based method. Then, a multi-grained multimodal Transformer called mmLayout is proposed to incorporate coarse-grained information into existing pre-trained fine-grained multimodal Transformers based on the graph. In mmLayout, coarse-grained information is aggregated from fine-grained, and then, after further processing, is fused back into fine-grained for final prediction. Furthermore, common sense enhancement is introduced to exploit the semantic information of natural lexical units. Experimental results on four tasks, including information extraction and document question answering, show that our method can improve the performance of multimodal Transformers based on fine-grained elements and achieve better performance with fewer parameters. Qualitative analyses show that our method can capture consistent semantics in coarse-grained elements.
More is better: Multi-source Dynamic Parsing Attention for Occluded Person Re-identification

Occluded person re-identification (re-ID) has been a long-standing challenge in surveillance systems. Most existing methods tackle this challenge by aligning spatial features of human parts according to external semantic cues, which are inferred from the off-the-shelf semantic models (e.g. human parsing and pose estimation). However, there is a significant domain gap between the images in re-ID datasets and the images used for training the semantic models, such that inevitably making those semantic cues unreliable and deteriorating the re-ID performance. Multi-source knowledge ensemble has been proved to be effective for domain adaptation. Inspired by this, we propose a multi-source dynamic parsing attention (MSDPA) mechanism that leverages knowledge learned from different source datasets to generate reliable semantic cues and dynamically integrate and adapt them in a self-supervised manner by attention mechanism. Specifically, we first design a parsing embedding module (PEM) to integrate and embed the multi-source semantic cues into the patch tokens through a voting procedure. To further exploit correlations among body parts with similar semantics, we design a dynamic parsing attention block (DPAB) to guide the patch sequences aggregation by prior attentions which are dynamically generated from human parsing results. Extensive experiments over occluded, partial, and holistic re-ID datasets show that the MSDPA achieves superior re-ID performance consistently and outperforms the state-of-the-art methods by large margins on occluded datasets.
Unified Multi-modal Pre-training for Few-shot Sentiment Analysis with Prompt-based Learning

Multi-modal sentiment analysis (MSA) has become more and more attractive in both academia and industry. The conventional studies normally require massive labeled data to train the deep neural models. To alleviate the above issue, in this paper, we conduct few-shot MSA with quite a small number of labeled samples. Inspired by the success of textual prompt-based fine-tuning (PF) approaches in few-shot scenario, we introduce a multi-modal prompt-based fine-tuning (MPF) approach. To narrow the semantic gap between language and vision, we propose unified pre-training for multi-modal prompt-based fine-tuning (UP-MPF) with two stages. First, in unified pre-training stage, we employ a simple and effective task to obtain coherent vision-language representations from fixed pre-trained language models (PLMs), i.e., predicting the rotation direction of the input image with a prompt phrase as input concurrently. Second, in multi-modal prompt-based fine-tuning, we freeze the visual encoder to reduce more parameters, which further facilitates few-shot MSA. Extensive experiments and analysis on three coarse-grained and three fine-grained MSA datasets demonstrate the better performance of our UP-MPF against the state-of-the-art of PF, MSA, and multi-modal pre-training approaches.
Relative Alignment Network for Source-Free Multimodal Video Domain Adaptation

Video domain adaptation aims to transfer knowledge from labeled source videos to unlabeled target videos. Existing video domain adaptation methods require full access to the source videos to reduce the domain gap between the source and target videos, which are impractical in real scenarios where the source videos are not available with concerns in transmission efficiency or privacy issues. To address this problem, in this paper, we propose to solve a source-free domain adaptation task for videos where only a pre-trained source model and unlabeled target videos are available for learning a multimodal video classification model. Existing source-free domain adaptation methods cannot be directly applied to this task, since videos always suffer from domain discrepancy along both the multimodal and temporal aspects, which brings difficulties in domain adaptation especially when the source data are unavailable. In this paper, we propose a Multimodal and Temporal Relative Alignment Network (MTRAN) to deal with the above challenges. To explicitly imitate the domain shifts contained in the multimodal information and the temporal dynamics of the source and target videos, we divide the target videos into two splits according to the self-entropy values of the classification results. The low-entropy videos are deemed to be source-like while the high-entropy videos are deemed to be target-like. Then, we adopt a self-entropy-guided MixUp strategy to generate synthetic samples and hypothetical samples as instance-level based on source-like and target-like videos, and push each synthetic sample to be similar with the corresponding hypothetical sample that is slightly closer to the source-like videos than the synthetic sample by multimodal and temporal relative alignment schemes. We evaluate the proposed model on four public video datasets. The results show that our model outperforms existing state-of-the-art methods.
Boat in the Sky: Background Decoupling and Object-aware Pooling for Weakly Supervised Semantic Segmentation

Previous image-level weakly-supervised semantic segmentation methods based on Class Activation Map (CAM) have two limitations: 1) focusing on partial discriminative foreground regions and 2) containing undesirable background. The above issues are attributed to the spurious correlations between the object and background (semantic ambiguity) and the insufficient spatial perception ability of the classification network (spatial ambiguity). In this work, we propose a novel self-supervised framework to mitigate the semantic and spatial ambiguity from the perspectives of background bias and object perception. First, a background decoupling mechanism (BDM) is proposed to handle the semantic ambiguity by regularizing the consistency of predicted CAMs from the samples with identical foregrounds but different backgrounds. Thus, a decoupled relationship is constructed to reduce the dependence between the object instance and the scene information. Second, a global object-aware pooling (GOP) is introduced to alleviate spatial ambiguity. The GOP utilizes a learnable object-aware map to dynamically aggregate spatial information and further improve the performance of CAMs. Extensive experiments demonstrate the effectiveness of our method by achieving new state-of-the-art results on both the Pascal VOC 2012 and MS COCO 2014 datasets.
Simple Self-supervised Multiplex Graph Representation Learning

Self-supervised multiplex graph representation learning (SMGRL) aims to capture the information from the multiplex graph, and generates discriminative embedding without labels. However, previous SMGRL methods still suffer from the issues of efficiency and effectiveness due to the processes, e.g., data augmentation, negative sample encoding, complex pretext tasks, etc. In this paper, we propose a simple method to achieve efficient and effective SMGRL. Specifically, the proposed method removes the processes (i.e., data augmentation and negative sample encoding) for the SMGRL and designs a simple pretext task, for achieving the efficiency. Moreover, the proposed method also designs an intra-graph decorrelation loss and an inter-graph decorrelation loss, respectively, to capture the common information within individual graphs and the common information across graphs, for achieving the effectiveness. Extensive experimental results verify the efficiency and effectiveness of our method, compared to 11 comparison methods on 4 public benchmark datasets, on the node classification task.
Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild

Camera-captured document images usually suffer from perspective and geometric deformations. It is of great value to rectify them when considering poor visual aesthetics and the deteriorated performance of OCR systems. Recent learning-based methods intensively focus on the accurately cropped document image. However, this might not be sufficient for overcoming practical challenges, including document images either with large marginal regions or without margins. Due to this impracticality, users struggle to crop documents precisely when they encounter large marginal regions. Simultaneously, dewarping images without margins is still an insurmountable problem. To the best of our knowledge, there is still no complete and effective pipeline for rectifying document images in the wild. To address this issue, we propose a novel approach called Marior (Margin Removal and Iterative Content Rectification). Marior follows a progressive strategy to iteratively improve the dewarping quality and readability in a coarse-to-fine manner. Specifically, we divide the pipeline into two modules: margin removal module (MRM) and iterative content rectification module (ICRM). First, we predict the segmentation mask of the input image to remove the margin, thereby obtaining a preliminary result. Then we refine the image further by producing dense displacement flows to achieve content-aware rectification. We determine the number of refinement iterations adaptively. Experiments demonstrate the state-of-the-art performance of our method on public benchmarks. The resources are available at https://github.com/ZZZHANG-jx/Marior for further comparison.
Multi-Level Region Matching for Fine-Grained Sketch-Based Image Retrieval

Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) is to use free-hand sketches as queries to perform instance-level retrieval in an image gallery. Existing works usually leverage only high-level information and perform matching in a single region. However, both low-level and high-level information are helpful to establish fine-grained correspondence. Besides, we argue that matching different regions between each sketch-image pair can further boost model robustness. Therefore, we propose Multi-Level Region Matching (MLRM) for FG-SBIR, which consists of two modules: a Discriminative Region Extraction module (DRE) and a Region and Level Attention module (RLA). In DRE, we propose Light-weighted Attention Map Augmentation (LAMA) to extract local feature from different regions. In RLA, we propose a transformer-based attentive matching module to learn attention weights to explore different importance from different image/sketch regions and feature levels. Furthermore, to ensure that the geometrical and semantic distinctiveness is well modeled, we also explore a novel LAMA overlapping penalty and a local region-negative triplet loss in our proposed MLRM method. Comprehensive experiments conducted on five datasets (i.e., Sketchy, QMUL-ChairV2, QMUL-ShoeV2, QMUL-Chair, QMUL-Shoe) demonstrate effectiveness of our method.
Temporal Sentiment Localization: Listen and Look in Untrimmed Videos

Video sentiment analysis aims to uncover the underlying attitudes of viewers, which has a wide range of applications in real world. Existing works simply classify a video into a single sentimental category, ignoring the fact that sentiment in untrimmed videos may appear in multiple segments with varying lengths and unknown locations. To address this, we propose a challenging task, i.e., Temporal Sentiment Localization (TSL), to find which parts of the video convey sentiment. To systematically investigate fully- and weakly-supervised settings for TSL, we first build a benchmark dataset named TSL-300, which is consisting of 300 videos with a total length of 1,291 minutes. Each video is labeled in two ways, one of which is frame-by-frame annotation for the fully-supervised setting, and the other is single-frame annotation, i.e., only a single frame with strong sentiment is labeled per segment for the weakly-supervised setting. Due to the high cost of labeling a densely annotated dataset, we propose TSL-Net in this work, employing single-frame supervision to localize sentiment in videos. In detail, we generate the pseudo labels for unlabeled frames using a greedy search strategy, and fuse the affective features of both visual and audio modalities to predict the temporal sentiment distribution. Here, a reverse mapping strategy is designed for feature fusion, and a contrastive loss is utilized to maintain the consistency between the original feature and the reverse prediction. Extensive experiments show the superiority of our method against the state-of-the-art approaches.
R-FEC

The demand for video conferencing applications has seen explosive growth while users still often face unsatisfactory quality of experience (QoE). Video conferencing applications adopt Forward Error Correction (FEC) as a recovery mechanism to meet tight latency requirements and overcome packet losses prevalent in the network. However, many studies mainly focused on video rate control by neglecting the complex interactions of this video recovery mechanism on the rate control and its impact on the user QoE. Deciding the right amount of FEC for the current video rate under a dynamically changing network environment is not straightforward. For instance, the higher FEC may enhance the tolerance to packet losses, but it may increase latency due to FEC processing overhead and hurt the video quality due to the additional bandwidth used for FEC. To address this issue, we propose R-FEC which is a reinforcement learning (RL) based framework for video and FEC bitrate decisions in video conferencing. R-FEC aims to improve overall QoE by automatically learning through the results of past decisions and adjusting video and FEC bitrates to maximize the user QoE while minimizing the congestion in the network. Our experiments show that R-FEC outperforms the state-of-the-art solutions in video conferencing, with up to 27% improvement in its video rate and 6dB PSNR improvement in video quality over the default WebRTC.
DAO: Dynamic Adaptive Offloading for Video Analytics

Offloading videos from end devices to edge or cloud servers is the key to enabling computation-intensive video analytics. To ensure the analytics accuracy at the server, the video quality for offloading must be configured based on the specific content and the available network bandwidth. While adaptive video streaming for user viewing has been widely studied, none of the existing works can guarantee the analytics accuracy at the server in bandwidth- and content-adaptive way. To fill in this gap, this paper presents DAO, a dynamic adaptive offloading framework for video analytics that jointly considers the dynamics of network bandwidth and video content. DAO is able to maximize the analytics accuracy at the server by adapting the video bitrate and resolution dynamically. In essence, we shift the context of adaptive video transport from traditional DASH systems to a new dynamic adaptive offloading framework tailored for video analytics. DAO is empowered by some new discoveries about the inherent relationship between analytics accuracy, video content, bitrate, and resolution, as well as by an optimization formulation to adapt the bitrate and resolution dynamically. Results from the real-world implementation of object detection tasks show that DAO's performance is close to the theoretical bound, achieving 20% bandwidth saving and 59% category-wise mAP improvement compared to conventional DASH schemes.
Caption-Aware Medical VQA via Semantic Focusing and Progressive Cross-Modality Comprehension

Medical Visual Question Answering as a specific-domain task requires substantive prior knowledge of medicine. However, deep learning techniques encounter severe problems of limited supervision due to the scarcity of well-annotated large-scale medical VQA datasets. As an alternative to facing the data limitation problem, image captioning can be introduced to learn summary information about the picture, which is beneficial to question answering. To this end, we propose a caption-aware VQA method that can read the summary information of image content and clinic diagnoses from plenty of medical images and answer the medical question with richer multimodality features. The proposed method consists of two novel components emphasizing semantic locations and semantic content respectively. Firstly, to extract and leverage the semantic locations implied in image captioning, similarity analysis is designed to summarize the attention maps generated from image captioning by their relevance and guide the visual model to focus on the semantic-rich regions. Besides, to combine the semantic content in the generated captions, we propose a Progressive Compact Bilinear Interactions structure to achieve cross-modality comprehension over the image, question and caption features by performing bilinear attention in a gradual manner. Qualitative and quantitative experiments on various medical datasets exhibit the superiority of the proposed approach compared to the state-of-the-art methods.
Decoupling Recognition from Detection: Single Shot Self-Reliant Scene Text Spotter

Typical text spotters follow the two-stage spotting strategy: detect the precise boundary for a text instance first and then perform text recognition within the located text region. While such strategy has achieved substantial progress, there are two underlying limitations. 1) The performance of text recognition depends heavily on the precision of text detection, resulting in the potential error propagation from detection to recognition. 2) The RoI cropping which bridges the detection and recognition brings noise from background and leads to information loss when pooling or interpolating from feature maps. In this work we propose the single shot Self-Reliant Scene Text Spotter (SRSTS), which circumvents these limitations by decoupling recognition from detection. Specifically, we conduct text detection and recognition in parallel and bridge them by the shared positive anchor point. Consequently, our method is able to recognize the text instances correctly even though the precise text boundaries are challenging to detect. Additionally, our method reduces the annotation cost for text detection substantially. Extensive experiments on regular-shaped benchmark and arbitrary-shaped benchmark demonstrate that our SRSTS compares favorably to previous state-of-the-art spotters in terms of both accuracy and efficiency.
Progressive Tree-Structured Prototype Network for End-to-End Image Captioning

Studies of image captioning are shifting towards a trend of a fully end-to-end paradigm by leveraging powerful visual pre-trained models and transformer-based generation architecture for more flexible model training and faster inference speed. State-of-the-art approaches simply extract isolated concepts or attributes to assist description generation. However, such approaches do not consider the hierarchical semantic structure in the textual domain, which leads to an unpredictable mapping between visual representations and concept words. To this end, we propose a novel Progressive Tree-Structured prototype Network (dubbed PTSN), which is the first attempt to narrow down the scope of prediction words with appropriate semantics by modeling the hierarchical textual semantics. Specifically, we design a novel embedding method called tree-structured prototype, producing a set of hierarchical representative embeddings which capture the hierarchical semantic structure in textual space. To utilize such tree-structured prototypes into visual cognition, we also propose a progressive aggregation module to exploit semantic relationships within the image and prototypes. By applying our PTSN to the end-to-end captioning framework, extensive experiments conducted on MSCOCO dataset show that our method achieves a new state-of-the-art performance with 144.2% (single model) and 146.5% (ensemble of 4 models) CIDEr scores on 'Karpathy' split and 141.4% (c5) and 143.9% (c40) CIDEr scores on the official online test server. Trained models and source code have been released at: https://github.com/NovaMind-Z/PTSN.
Generating Transferable Adversarial Examples against Vision Transformers

Vision transformers (ViTs) are prevailing among several visual recognition tasks, therefore drawing intensive interest in generating adversarial examples against them. Different from CNNs, ViTs enjoy unique architectures, e.g., self-attention and image-embedding, which are commonly-shared features among various types of transformer-based models. However, existing adversarial methods suffer from weak transferable attacking ability due to the overlook of these architectural features. To address the problem, we propose an Architecture-oriented Transferable Attacking (ATA) framework to generate transferable adversarial examples by activating the uncertain attention and perturbing the sensitive embedding.Specifically, we first locate the patch-wise attentional regions that mostly affect model perception, therefore intensively activating the uncertainty of the attention mechanism and confusing the model decisions in turn.Furthermore, we search the pixel-wise attacking positions that are more likely to derange the embedded tokens using sensitive embedding perturbation, which could serve as a strong transferable attacking pattern.By jointly confusing the unique yet widely-used architectural features among transformer-based models, we can activate strong attacking transferability among diverse ViTs. Extensive experiments on large-scale dataset ImageNet using various popular transformers demonstrate that our ATA outperforms other baselines by large margins (at least +15% Attack Success Rate). Our code is available at https://github.com/nlsde-safety-team/ATA