Reviewerly/exHarmony huggingface dataset:
Single-Stage Multi-human Parsing via Point Sets and Center-based Offsets

This work studies the multi-human parsing problem. Existing methods, either following top-down or bottom-up two-stage paradigms, usually involve expensive computational costs. We instead present a high-performance Single-stage Multi-human Parsing (SMP) deep architecture that decouples the multi-human parsing problem into two fine-grained sub-problems,i.e., locating the human body and parts. SMP leverages the point features in the barycenter positions to obtain their segmentation and then generates a series of offsets from the barycenter of the human body to the barycenters of parts, thus performing human body and parts matching without the grouping process. Within the SMP architecture, we propose a Refined Feature Retain module to extract the global feature of instances through generated mask attention and a Mask of Interest Reclassify module as a trainable plug-in module to refine the classification results with the predicted segmentation. Extensive experiments on the MHPv2.0 dataset demonstrate the best effectiveness and efficiency of the proposed method, surpassing the state-of-the-art method by 2.1% in AP50p, 1.0% in APvolpsup>, and 1.2% in PCP50. Moreover, SMP also achieves superior performance in DensePose-COCO, verifying generalization of the model. In particular, the proposed method requires fewer training epochs and a less complex model architecture. Our codes are released in https://github.com/cjm-sfw/SMP.
My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection

Social platforms such as Twitter are under siege from a multitude of fraudulent users. In response, social bot detection tasks have been developed to identify such fake users. Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks. In this study, we propose a node injection-based adversarial attack method designed to deceive bot detection models. Notably, neither the target bot nor the newly injected bot can be detected when a new bot is added around the target bot. This attack operates in a black-box fashion, implying that any information related to the victim model remains unknown. To our knowledge, this is the first study exploring the resilience of bot detection through graph node injection. Furthermore, we develop an attribute recovery module to revert the injected node embedding from the graph embedding space back to the original feature space, enabling the adversary to manipulate node perturbation effectively. We conduct adversarial attacks on four commonly used GNN structures for bot detection on two widely used datasets: Cresci-2015 and TwiBot-22. The attack success rate is over 73% and the rate of newly injected nodes being detected as bots is below 13% on these two datasets.
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution

Recent years have witnessed a few attempts of vision transformers for single image super-resolution (SISR). Since the high resolution of intermediate features in SISR models increases memory and computational requirements, efficient SISR transformers are more favored. Based on some popular transformer backbone, many methods have explored reasonable schemes to reduce the computational complexity of the self-attention module while achieving impressive performance. However, these methods only focus on the performance on the training platform (e.g., Pytorch/Tensorflow) without further optimization for the deployment platform (e.g., TensorRT). Therefore, they inevitably contain some redundant operators, posing challenges for subsequent deployment in real-world applications. In this paper, we propose a deployment-friendly transformer unit, namely UFONE (i.e., UnFolding ONce is Enough), to alleviate these problems. In each UFONE, we introduce an Inner-patch Transformer Layer (ITL) to efficiently reconstruct the local structural information from patches and a Spatial-Aware Layer (SAL) to exploit the long-range dependencies between patches. Based on UFONE, we propose a Deployment-friendly Inner-patch Transformer Network (DITN) for the SISR task, which can achieve favorable performance with low latency and memory usage on both training and deployment platforms. Furthermore, to further boost the deployment efficiency of the proposed DITN on TensorRT, we also provide an efficient substitution for layer normalization and propose a fusion optimization strategy for specific operators. Extensive experiments show that our models can achieve competitive results in terms of qualitative and quantitative performance with high deployment efficiency.
CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting

Contrastive Language-Image Pre-training (CLIP) starts to emerge in many computer vision tasks and has achieved promising performance. However, it remains underexplored whether CLIP can be generalized to 3D hand pose estimation, as bridging text prompts with pose-aware features presents significant challenges due to the discrete nature of joint positions in 3D space. In this paper, we make one of the first attempts to propose a novel 3D hand pose estimator from monocular images, dubbed as CLIP-Hand3D, which successfully bridges the gap between text prompts and irregular detailed pose distribution. In particular, the distribution order of hand joints in various 3D space directions is derived from pose labels, forming corresponding text prompts that are subsequently encoded into text representations. Simultaneously, 21 hand joints in the 3D space are retrieved, and their spatial distribution (in x, y, and z axes) is encoded to form pose-aware features. Subsequently, we maximize semantic consistency for a pair of pose-text features following a CLIP-based contrastive learning paradigm. Furthermore, a coarse-to-fine mesh regressor is designed, which is capable of effectively querying joint-aware cues from the feature pyramid. Extensive experiments on several public hand benchmarks show that the proposed model attains a significantly faster inference speed while achieving state-of-the-art performance compared to methods utilizing the similar scale backbone. Code is available at: https://github.com/ShaoXiang23/CLIP_Hand_Demo.
CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model

Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. In this paper, we propose a Consistency Model-based Speech synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality. The consistency constraint is applied to distill a consistency model from a well-designed diffusion-based teacher model, which ultimately yields superior performances in the distilled CoMoSpeech. Our experiments show that by generating audio recordings by a single sampling step, the CoMoSpeech achieves an inference speed more than 150 times faster than real-time on a single NVIDIA A100 GPU, which is comparable to FastSpeech2, making diffusion-sampling based speech synthesis truly practical. Meanwhile, objective and subjective evaluations on text-to-speech and singing voice synthesis show that the proposed teacher models yield the best audio quality, and the one-step sampling based CoMoSpeech achieves the best inference speed with better or comparable audio quality to other conventional multi-step diffusion model baselines. Audio samples and codes are available at https://comospeech.github. https://comospeech.github.io/.
WRAP: Watermarking Approach Robust Against Film-coating upon Printed Photographs

Recently, print-resist watermarking has attracted much interest. Many watermarking schemes have been proposed to achieve robustness against printing and camera-capturing. Though these studies have shown promising results overall, they overlook the scenario of film-coating photographs, which is a significant and common scenario in real-world. The film-coating process can introduce severe distortions to the original image and easily incapacitate the watermark. To address this issue, we propose WRAP, a novel Watermarking scheme Robust Against film-coating upon Printed photographs. We first construct a large dataset with 120,000 film-coating images to train a style-transfer-based film-coating simulation network. Based on the network, we propose a comprehensive distortion layer which includes film-coating simulation and common disturbances in the printing and camera-capturing process. With the distortion layer, the entire embedding and extraction network can be trained end-to-end to gain robustness against film-coating upon printed photographs. Extensive experiments demonstrate the superior performances of our model in terms of robustness and generalization capability. Our model outperforms state-of-the-art print-resist watermarking schemes when testing in film-coating scenario and achieves outstanding performance across various datasets, types of films, and cameras. To the best of our knowledge, we are the first to conduct research on digital watermarking in film-coating scenario.
HELIOS: Hyper-Relational Schema Modeling from Knowledge Graphs

Knowledge graph (KG) schema, which prescribes a high-level structure and semantics of a KG, is significantly helpful for KG completion and reasoning problems. Despite its usefulness, open-domain KGs do not practically have a unified and fixed schema. Existing approaches usually extract schema information using entity types from a KG where each entity e can be associated with a set of types {Te, by either heuristically taking one type for each entity or exhaustively combining the types of all entities in a fact (to get entity-typed tuples, (h_type, r, t_type) for example). However, these two approaches either overlook the role of multiple types of a single entity across different facts or introduce non-negligible noise as not all the type combinations actually support the fact, thus failing to capture the sophisticated schema information. Against this background, we study the problem of modeling hyper-relational schema, which is formulated as mixed hyper-relational tuples ({Th}, r, {Tt}, k, {Tv1},...) with two-fold hyper-relations: each type set T may contain multiple types and each schema tuple may contain multiple key-type set pairs (k, Tv). To address this problem, we propose HELIOS, a hyper-relational schema model designed to subtly learn from such hyper-relational schema tuples by capturing not only the correlation between multiple types of a single entity, but also the correlation between types of different entities and relations in a schema tuple. We evaluate HELIOS on three real-world KG datasets in different schema prediction tasks. Results show that HELIOS consistently outperforms state-of-the-art hyper-relational link prediction techniques by 20.0-29.7%, and is also much more robust than baselines in predicting types and relations across different positions in a hyper-relational schema tuple.
Face Encryption via Frequency-Restricted Identity-Agnostic Attacks

Billions of people are sharing their daily live images on social media everyday. However, malicious collectors use deep face recognition systems to easily steal their biometric information (e.g., faces) from these images. Some studies are being conducted to generate encrypted face photos using adversarial attacks by introducing imperceptible perturbations to reduce face information leakage. However, existing studies need stronger black-box scenario feasibility and more natural visual appearances, which challenge the feasibility of privacy protection. To address these problems, we propose a frequency-restricted identity-agnostic (FRIA) framework to encrypt face images from unauthorized face recognition without access to personal information. As for the weak black-box scenario feasibility, we obverse that representations of the average feature in multiple face recognition models are similar, thus we propose to utilize the average feature via the crawled dataset from the Internet as the target to guide the generation, which is also agnostic to identities of unknown face recognition systems; in nature, the low-frequency perturbations are more visually perceptible by the human vision system. Inspired by this, we restrict the perturbation in the low-frequency facial regions by discrete cosine transform to achieve the visual naturalness guarantee. Extensive experiments on several face recognition models demonstrate that our FRIA outperforms other state-of-the-art methods in generating more natural encrypted faces while attaining high black-box attack success rates of 96%. In addition, we validate the efficacy of FRIA using real-world black-box commercial API, which reveals the potential of FRIA in practice. Our codes can be found in https://github.com/XinDong10/FRIA.
Modality Profile - A New Critical Aspect to be Considered When Generating RGB-D Salient Object Detection Training Set

It is widely acknowledged that selecting appropriate training data is crucial for obtaining good results in real-world testing, more so than utilizing complex network architectures. However, in the field of RGB-D SOD research, researchers have primarily focused on enhancing network architectures and have given less consideration to the choice of training and testing datasets, which may not translate well in practical applications. This paper aims to address an existing issue - how can we automatically generate a data-driven RGB-D SOD training dataset? We propose that in addition to scene similarity, the concept of "modality profile'' should be taken into account. The term "modality profile'' refers to the complementary status of modalities within a given dataset. A training dataset with a modality profile similar to the test dataset can significantly improve performance. To address this, we present a viable solution for automatically generating a training dataset with any desired modality profile in a weakly supervised manner. Our method also provides high-quality pseudo-GTs for all RGB-D images obtained from the web, making it suitable for training RGB-D SOD models. Extensive quantitative evaluations demonstrate the significance of the proposed "modality profile'' and confirm the superiority of the newly constructed training set guided by our "modality profile''. All codes, datasets, and results are available at this link.
A Simple Baseline for Open-World Tracking via Self-training

Open-World Tracking (OWT) presents a challenging yet emerging problem, aiming to track every object of any category. Different from traditional Multi-Object Tracking (MOT), OWT needs to additionally track targets beyond predefined categories in the training set. To address the problem, we propose a simple baseline, SimOWT. We simplify the recently proposed OWT algorithm by streamlining the association module and accelerating the inference speed. By leveraging the self-training paradigm, SimOWT can distinguish unknown-class targets from the background, fully unleashing the potential of TAO-OW dataset. Furthermore, we enhance SimOWT from the perspectives of Pseudo Boxes Merging and Re-Weighting, thereby discovering more targets belonging to unknown classes and reducing the sensitivity of the model to low-quality pseudo-labels. Benefiting from the proposed approaches, SimOWT demonstrates a significant improvement in tracking performance on unknown classes. Moreover, the comprehensive experiments on the TAO-OW benchmark demonstrate that our model outperforms the state-of-the-art OWT method, OWTB, with an absolute gain of 11.2% OWTA and 16.4% detection recall respectively on unknown classes. The code is released at https://github.com/22109095/SimOWT.
LiFT: Transfer Learning in Vision-Language Models for Downstream Adaptation and Generalization

Pre-trained Vision-Language Models (VLMs) on large-scale image-text pairs, e.g., CLIP, have shown promising performance on zero-shot knowledge transfer. Recently, fine-tuning pre-trained VLMs to downstream few-shot classification with limited image annotation data yields significant gains. However, there are two limitations. First, most of the methods for fine-tuning VLMs only update newly added parameters while keeping the whole VLM frozen. Thus, it remains unclear how to directly update the VLM itself. Second, fine-tuning VLMs to a specific set of base classes would deteriorate the well-learned representation space such that the VLMs generalize poorly on novel classes. To address these issues, we first propose Layer-wise Fine-Tuning (LiFT) which achieves average gains of 3.9%, 4.3%, 4.2% and 4.5% on base classes under 2-, 4-, 8- and 16-shot respectively compared to the baseline CoOp over 11 datasets. Alternatively, we provide a parameter-efficient LiFT-Adapter exhibiting favorable performance while updating only 1.66% of total parameters. Further, we design scalable LiFT-NCD to identify both base classes and novel classes, which boosts the accuracy by an average of 5.01% over zero-shot generalization of CLIP, exploring the potential of VLMs in discovering novel classes.
Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation

Few-shot image generation, which aims to produce plausible and diverse images for one category given a few images from this category, has drawn extensive attention. Existing approaches either globally interpolate different images or fuse local representations with pre-defined coefficients. However, such an intuitive combination of images/features only exploits the most relevant information for generation, leading to poor diversity and coarse-grained semantic fusion. To remedy this, this paper proposes a novel textural modulation (TexMod) mechanism to inject external semantic signals into internal local representations. Parameterized by the feedback from the discriminator, our TexMod enables more fined-grained semantic injection while maintaining the synthesis fidelity. Moreover, a global structural discriminator (StructD) is developed to explicitly guide the model to generate images with reasonable layout and outline. Furthermore, the frequency awareness of the model is reinforced by encouraging the model to distinguish frequency signals. Together with these techniques, we build a novel and effective model for few-shot image generation. The effectiveness of our model is identified by extensive experiments on three popular datasets and various settings. Besides achieving state-of-the-art synthesis performance on these datasets, our proposed techniques could be seamlessly integrated into existing models for a further performance boost. Our code and models are available at \hrefhttps://github.com/kobeshegu/SDTM-GAN-ACMMM-2023 here.
BranchClash: A Fully On-Chain Tower Defense Blockchain Game with New Collaboration Mechanism

Mainstream blockchain games have drawn criticism for prioritizing economic systems over gameplay experience. Influenced by these economically-centered games, existing research on blockchain games predominantly focuses on the financial sector. We have developed BranchClash, a fully on-chain tower defense game on the Sepolia testnet of Ethereum. It introduces chain collaboration, a novel non-economically-centered game mechanism inspired by blockchain technology. BranchClash aims to expand unique game mechanics in blockchain games and explore innovative cooperative modes within the decentralized ecosystem.
Lifelong Scene Text Recognizer via Expert Modules

Scene text recognition (STR) has been actively studied in recent years, with a wide range of applications in autonomous driving, image retrieval and much more. However, when a pre-trained deep STR model learns a new task, its performance on previous tasks may drop dramatically, due to catastrophic forgetting in deep neural networks. A potential solution to combat the forgetting of prior knowledge is incremental learning (IL), which has shown its effectiveness and significant progress in image classification. Yet, exploiting IL in the context of STR has been barely visited, probably because the forgetting problem is even worse in STR. To address this issue, we propose the lifelong scene text recognizer (LSTR) that learns STR tasks incrementally while alleviating forgetting. Specifically, LSTR assigns each task a set of task-specific expert modules at different stages of an STR model, while other parameters are shared among tasks. These shared parameters are only learned in the first task and remain unchanged during subsequent learning to ensure that no learned knowledge is overlooked. Moreover, in real applications, there is no prior knowledge about which task an input image belongs to, making it impossible to precisely select the corresponding expert modules. To this end, we propose the incremental task prediction network (ITPN) to identify the most related task category by pulling the features of the same task closer and pushing those of different tasks farther apart. To validate the proposed method in our newly-introduced IL setting, we collected a large-scale dataset consisting of both real and synthetic multilingual STR data. Extensive experiments on this dataset clearly show the superiority of our LSTR over state-of-the-art IL methods.
Exploring the Knowledge Transferred by Response-Based Teacher-Student Distillation

Response-based Knowledge Distillation refers to the technique of supervising the student network with the teacher networks' predictions. The method is motivated by observing that the predicted probabilities reflect the relation among labels, which is the knowledge to be transferred. This paper explores the transferred knowledge from a novel perspective: comparing the knowledge transferred through different teachers. Two intriguing properties are observed. First, higher confidence scores of teachers' predictions lead to better distillation results, and second, teachers' incorrectly predicted training samples should be kept for distillation. We then analyze the phenomenon by studying teachers' decision boundaries, of which some can help the student generalize while some may not. Based on the observations, we further propose an embarrassingly simple distillation framework named Efficient Distillation, which is effective on ImageNet with different teacher-student pairs: When using ResNet34 as the teacher, the student ResNet18 trained from scratch reaches 74.07% Top-1 accuracy within 98 GPU hours (RTX 3090), outperforming current state-of-the-art result (73.19%) by a large margin. Our code is available at https://github.com/lsongx/EffDstl.