Reviewerly/exHarmony huggingface dataset:
Audio-driven Talking Head Generation with Transformer and 3D Morphable Model

In the task of talking head generation, it is hard to learn the mapping relationship between generated head image and input audio signal. To tackle this challenge, we propose to learn the mapping relationship between input audio signal and the parameters of three-dimensional morphable face model (3DMM) first, which is easier to learn. Then the parameters of 3DMM are used to guide the generation of high-quality talking head images. Prior works mostly encode audio features from short audio windows, which may influence the accuracy of lip movements sometimes because of the limited context. In this paper, we propose a transformer-based audio encoder to take full use of the long-term context from audio and then predict a sequence of 3DMM parameters accurately. Unlike prior works that only use the 3DMM parameters of expression, rotation and translation, we propose to include the parameters of identity. Since the location of 3D facial mesh point is decided by the expression and identity parameters, it is helpful to supply more subtle control of lip movement by considering the identity parameters. The experimental results reveal that our method ranks first in 4 of the total 11 evaluation metrics, which ranks first in the talking head generation track.
Weakly-supervised Disentanglement Network for Video Fingerspelling Detection

Fingerspelling detection, which aims to localize and recognize fingerspelling gestures in raw, untrimmed videos, is a nascent but important research area that could help bridge the communication gap between deaf people and others. Many existing works tend to exploit additional knowledge, such as pose annotations, and newly datasets for performance improvement. However, in real-world applications, additional data collection and annotation require tremendous human efforts that are not always affordable. In this paper, we propose the Weakly-supervised Disentanglement Network, namely WED, that requires no additional knowledge, and better exploits the video-sentence weak supervisions. Specifically, WED incorporates two critical components: 1) Masked Disentanglement Module, which employs a Variational Autoencoder for signed letters disentanglement. Each latent factor in the VAE corresponds to a particular signed letter, and we mask latent factors corresponding to letters that do not appear in the video during decoding. Compared to the vanilla VAE, the masked reconstruction leverages the video-sentence weak supervision, leading to a better sign language oriented disentanglement; and 2) the Dynamic Memory Network module, which leverages the disentangled sign knowledge as prior knowledge and reference for sign-related frame identification and gesture recognition through a carefully designed memory reading component. We conduct extensive experiments on the benchmark ChicagoFSWild and ChicagoFSWild+ datasets. Empirical studies validate that the WED network achieves effective sign gesture disentanglement, contributing to the state-of-the-art performance for fingerspelling detection and recognition.
Rethinking the Vulnerability of DNN Watermarking

Training Deep Neural Networks (DNN) is a time-consuming process and requires a large amount of training data, which motivates studies working on protecting the intellectual property (IP) of DNN models by employing various watermarking techniques. Unfortunately, in recent years, adversaries have been exploiting the vulnerabilities of the employed watermarking techniques to remove the embedded watermarks. In this paper, we investigate and introduce a novel watermark removal attack, called AdvNP, against all the existing four different types of DNN watermarking schemes via input preprocessing by injecting <u>Adv</u>ersarial <u>N</u>aturalness-aware <u>P</u>erturbations. In contrast to the prior studies, our proposed method is the first work that generalizes all the existing four watermarking schemes well without involving any model modification, which preserves the fidelity of the target model. We conduct the experiments against four state-of-the-art (SOTA) watermarking schemes on two real tasks (e.g., image classification on ImageNet, face recognition on CelebA) across multiple DNN models. Overall, our proposed AdvNP significantly invalidates the watermarks against the four watermarking schemes on two real-world datasets, i.e., 60.9% on the average attack success rate and up to 97% in the worse case. Moreover, our AdvNP could well survive the image denoising techniques and outperforms the baseline in both the fidelity preserving and watermark removal. Furthermore, we introduce two defense methods to enhance the robustness of DNN watermarking against our AdvNP. Our experimental results pose real threats to the existing watermarking schemes and call for more practical and robust watermarking techniques to protect the copyright of pre-trained DNN models. The source code and models are available at ttps://github.com/GitKJ123/AdvNP.
Hybrid Conditional Deep Inverse Tone Mapping

Emerging modern displays are capable to render ultra-high definition (UHD) media contents with high dynamic range (HDR) and wide color gamut (WCG). Although more and more native contents as such have been getting produced, the total amount is still in severe lack. Considering the massive amount of legacy contents with standard dynamic range (SDR) which may be exploitable, the urgent demand for proper conversion techniques thus springs up. In this paper, we try to tackle the conversion task from SDR to HDR-WCG for media contents and consumer displays. We propose a deep learning based SDR-to-HDR solution, Hybrid Conditional Deep Inverse Tone Mapping (HyCondITM), which is an end-to-end trainable framework including global transform, local adjustment, and detail refinement in a single unified pipeline. We present a hybrid condition network that can simultaneously extract both global and local priors for guidance to achieve scene-adaptive and spatially-variant manipulations. Experiments show that our method achieves state-of-the-art performance in both quantitative comparisons and visual quality, out-performing the previous methods.
Quantum kinematics in terms of observable quantities and the chirality of entangled two-qubit states

We consider the kinematics of bipartite quantum states as determined by observable quantities, in particular the Bloch vectors of the subsystems. In examining the simplest case of a pair of two-level systems, there is a remarkable connection between the presence of non-classical correlations and the chirality of the two bases generated by the singular value decomposition of the correlation matrix of the Bloch vectors. We investigate the limits imposed by quantum mechanics of this effect and its relationship with other methods on quantifying the system's non-classical behavior.
Adaptively-weighted Integral Space for Fast Multiview Clustering

Multiview clustering has been extensively studied to take advantage of multi-source information to improve the clustering performance. In general, most of the existing works typically compute an $n\times n$ affinity graph by some similarity/distance metrics (e.g. the Euclidean distance) or learned representations, and explore the pairwise correlations across views. But unfortunately, a quadratic or even cubic complexity is often needed, bringing about difficulty in clustering large-scale datasets. Some efforts have been made recently to capture data distribution in multiple views by selecting view-wise anchor representations with k-means, or by direct matrix factorization on the original observations. Despite the significant success, few of them have considered the view-insufficiency issue, implicitly holding the assumption that each individual view is sufficient to recover the cluster structure. Moreover, the latent integral space as well as the shared cluster structure from multiple insufficient views is not able to be simultaneously discovered. In view of this, we propose an \underlineA daptively-weighted \underlineI ntegral Space for Fast \underlineM ultiview \underlineC lustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space with diverse adaptive contributions. Meanwhile, a centroid representation with orthogonality constraint and cluster partition are seamlessly constructed to approximate the latent integral space. An alternate minimizing algorithm is developed to solve the optimization problem, which is proved to have linear time complexityw.r.t. the sample size. Extensive experiments conducted on several real-world datasets confirm the superiority of the proposed AIMC method compared with the state-of-the-art methods.
Comprehensive Relationship Reasoning for Composed Query Based Image Retrieval

Composed Query Based Image Retrieval (CQBIR) aims at searching images relevant to a composed query, i.e., a reference image together with a modifier text. Compared with conventional image retrieval, which takes a single image or text to retrieve desired images, CQBIR encounters more challenges as it requires not only effective semantic correspondence between the heterogeneous query and target, but also synergistic understanding of the composed query. To establish robust CQBIR model, four critical types of relational information can be included, i.e., cross-modal, intra-sample, inter-sample, and cross-sample relationships. Pioneer studies mainly exploit parts of the information, which are hard to make them enhance and complement each other. In this paper, we propose a comprehensive relationship reasoning network by fully exploring the four types of information for CQBIR, which mainly includes two key designs. First, we introduce a memory-augmented cross-modal attention module, in which the representation of the composed query is augmented by considering the cross-modal relationship between the reference image and the modification text. Second, we design a multi-scale matching strategy to optimize our network, aiming at harnessing information from the intra-sample, inter-sample, and cross-sample relationships. To the best of our knowledge, this is the first work to fully explore the four pieces of relationships in a unified deep model for CQBIR. Comprehensive experimental results on five standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art models.
Prompt-based Zero-shot Video Moment Retrieval

Video moment retrieval aims at localizing a specific moment from an untrimmed video by a sentence query. Most methods rely on heavy annotations of video moment-query pairs. Recent zero-shot methods reduced annotation cost, yet they neglected the global visual feature due to the separation of video and text learning process. To avoid the lack of visual features, we propose a Prompt-based Zero-shot Video Moment Retrieval (PZVMR) method. Motivated by the frame of prompt learning, we design two modules: 1) Proposal Prompt (PP): We randomly masks sequential frames to build a prompt to generate proposals; 2) Verb Prompt (VP): We provide patterns of nouns and the masked verb to build a prompt to generate pseudo queries with verbs. Our PZVMR utilizes task-relevant knowledge distilled from pre-trained CLIP and adapts the knowledge to VMR. Unlike the pioneering work, we introduce visual features into each module. Extensive experiments show that our PZVMR not only outperforms the existing zero-shot method (PSVL) on two public datasets (Charades-STA and ActivityNet-Captions) by 4.4% and 2.5% respectively in mIoU, but also outperforms several methods using stronger supervision.
A Unified Framework against Topology and Class Imbalance

The Area Under ROC curve (AUC) is widely used as an evaluation metric in various applications. Due to its insensitivity towards class distribution, directly optimizing AUC performs well on the class imbalance problem. However, existing AUC optimization methods are limited to regular data such as text, images, and video. AUC optimization on graph data, which is ubiquitous and important, is seldom studied. Different from regular data, AUC optimization on graphs suffers from not only the class imbalance but also topology imbalance. To solve the complicated imbalance problem, we propose a unified topology-aware AUC optimization (TOPOAUC) framework, which could simultaneously deal with the topology and class imbalance problem in graph learning. We develop a multi-class AUC optimization work to deal with the class imbalance problem. With respect to topology imbalance, we propose a T opology-A ware I mportance L earning mechanism (TAIL), which considers the topology of pairwise nodes and different contributions of topology information to pairwise node neighbors. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method.
HEART: Towards Effective Hash Codes under Label Noise

Hashing, which encodes raw data into compact binary codes, has grown in popularity for large-scale image retrieval due to its storage and computation efficiency. Although deep supervised hashing has lately shown promising performance, they mostly assume that the semantic labels of training data are ideally noise-free, which is often unrealistic in real-world applications. In this paper, considering the practical application, we focus on the problem of learning to hash with label noise and propose a novel method called HEART to address the problem. HEART is a holistic framework which explores latent semantic distributions to select both clean samples and pairs of high confidence for mitigating the impacts of label noise. From a statistical perspective, our HEART characterizes each image by its multiple augmented views that can be considered as examples from its latent distribution and then calculates semantic distances between images using energy distances between their latent distributions. With semantic distances, we can select confident similar pairs to guide hashing contrastive learning for high-quality hash codes. Moreover, to prevent the memorization of noisy examples, we propose a novel strategy to identify clean samples which have small variations of losses on the latent distributions and train the network on clean samples using a pointwise loss. Experimental results on several popular benchmark datasets demonstrate the effectiveness of our HEART compared with a wide range of baselines.
Engaging Museum Visitors with Gamification of Body and Facial Expressions

In this demo we present two applications designed for the cultural heritage domain that exploit gamification techniques in order to improve fruition and learning of museum artworks. The two applications encourage users to replicate the poses and facial expressions of characters from paintings or statues, to help museum visitors make connections with works of art. Both applications challenge the user to fulfill a task in a funny way and provide the user with a visual report of the his/her experience that can be shared on social media, improving the engagement of the museums, and providing information on the artworks replicated in the challenge.
Self-Aligned Concave Curve

Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. All of our data, code, and results will be available online upon publication of the paper.
Adaptive Dual Motion Model for Facial Micro-Expression Generation

Facial micro-expression (ME) refers to a brief spontaneous facial movement that can reveal the genuine emotion of a person. The absence of data is a major problem for ME. Thankfully, generative deep neural network models can aid in producing desired samples. In this work, we proposed a deep learning based adaptive dual motion model (ADMM) for generating facial ME samples. A dual motion extraction (DME) module extracts robust motions from two modalities: original color images and edge-based grayscale images, with dual streams. Using edge-based grayscale images can help the method focus on learning subtle movements by eliminating the influences of noises and illumination variants. The motions extracted by the dual streams are fed into an adaptive motion fusion (AMF) module for combing the motions adaptively to generate the dense motion. Our method was trained on the CASME II, SMIC, and SAMM datasets. The evaluation and analysis of the results demonstrated the effectiveness of our method.
Depth-inspired Label Mining for Unsupervised RGB-D Salient Object Detection

Existing deep learning-based unsupervised Salient Object Detection (SOD) methods heavily rely on the pseudo labels predicted from handcrafted features. However, the pseudo ground truth obtained only from RGB space would easily bring undesirable noises, especially in some complex scenarios. This naturally leads to the incorporation of extra depth modality with RGB images for more robust object identification, namely RGB-D SOD. Compared with the well-studied unsupervised SOD in the RGB domain, deep unsupervised RGB-D SOD is a less explored direction in the literature. In this paper, we propose to tackle this task by introducing a novel systemic design for high-quality pseudo-label mining. Our framework consists of two key components, Depth-inspired Label Generation (DLG) and Multi-source Uncertainty-aware Label Optimization (MULO). In DLG, a lightweight deep network is designed for automatically producing pseudo labels from depth maps in a self-supervised manner. Then, MULO introduces an effective pseudo label optimization strategy by learning the uncertainty of the pseudo labels from the depth domain and heuristic features. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art unsupervised methods on mainstream benchmarks.
Patch-based Knowledge Distillation for Lifelong Person Re-Identification

The task of lifelong person re-identification aims to match a person across multiple cameras given continuous data streams. Similar to other lifelong learning tasks, it severely suffers from the so-called catastrophic forgetting problem, which refers to the notable performance degradation on previously-seen data after adapting the model to some newly incoming data. To alleviate it, a few existing methods have utilized knowledge distillation to enforce consistency between the original and adapted models. However, the effectiveness of such a strategy can be largely reduced facing the data distribution discrepancy between seen and new data. The hallmark of our work is using adaptively-chosen patches (rather than whole images as in other works) to pilot the forgetting-resistant distillation. Specifically, the technical contributions of our patch-based new solution are two-fold: first, a novel patch sampler is proposed. It is fully differentiable and trained to select a diverse set of image patches that stay crucial and discriminative under streaming data. Secondly, with those patches we curate a novel knowledge distillation framework. Valuable patch-level knowledge within individual patch features and mutual relations is well preserved by the two newly introduced distillation modules, further mitigating catastrophic forgetting. Extensive experiments on twelve person re-identification datasets clearly validate the superiority of our method over state-of-the-art competitors by large performance margins.
Phase-based Memory Network for Video Dehazing

Video dehazing using deep-learning based methods has just received increasing attention in recent years. However, most existing methods tackle temporal consistency in the color domain only, which are less sensitive to small and imperceptible motions in a video, due to fog's drift and diffusion. In this work, we investigate in the frequency domain, which enables us to capture small motions effectively, and find that the phase component contains more semantic structures yet less haze information than the amplitude component of the hazy image. Based on these observations, we propose a novel phase-based memory network (PM-Net) to integrate the phase and color memory information for boosting video dehazing. Apart from the color memory from consecutive video frames, our PM-Net constructs a phase memory, which stores phase features of past video frames, and devise a cross-modal memory read (CMR) module, which fully leverages features from the color memory and the phase memory to boost features extracted from the current video frame for dehazing. Experimental results on the benchmark dataset of real hazy videos and a newly collected dataset of synthetic videos, show that the proposed PM-Net clearly outperforms the state-of-the-art image and video dehazing methods. Code is available at https://github.com/liuye123321/PM-Net.
Feeling Without Sharing

The explosion of video data brings new opportunities and challenges for emotion recognition. Video emotion applications have great commercial value, but the potential to involve illegal snooping on personal feelings has led to controversy over privacy protection. The federated learning (FL) paradigm can substantially address the growing public concerns about data privacy in video emotion recognition. However, conventional FL methods perform poorly due to the uniqueness of the task: the data are heterogeneous across clients induced by emotional label skew and cross-culture expression differences. To mitigate the heterogeneous data, we propose EmoFed, a practical framework of federated learning video-based emotion recognition via multi-group clustering and privacy-agnostic hybrid aggregation. It yields a generically applicable and improved model while protecting privacy, which trains local models under group-aware personalized aggregation. To further encourage communicating comprehensive and privacy-agnostic information among clients, we upload model parameters of both the global layers and personalization layers to the server. We utilize the homomorphically encrypted method for personalization layers, which incurs no learning accuracy loss since no noise is added to the model updates during the encryption/decryption process. The proposed method works on video-based emotion recognition tasks to predict actors' emotional expressions and induced emotion by viewers. Extensive experiments and ablation studies on four benchmarks have demonstrated the efficacy and practicability of our method.