As datasets grow, expert-based annotation becomes impractical, making crowdsourcing a scalable alternative. In crowdsourcing, samples are typically annotated by multiple workers and aggregated via majority voting, which ignores annotator-specific biases and introduces noisy labels that impair downstream models. Traditional multi-rater methods attempt to model annotator biases but often overfit with many classes or few, noisy annotators. Learning with Noisy Labels (LNL) methods offer more robust strategies for handling noisy labels, but their assumption of a single noisy label per sample makes extending them to multi-annotator settings non-trivial. To bridge this gap, we propose the Reciprocal Teacher-student Learning from Multi-rater Noisy Annotation (RETINA), which trains annotator-specific models and employs a dynamic teacher–student process to separate clean from noisy samples. Progress in multi-rater learning has also been limited by benchmarks with few classes, fixed noise rates, and no control over annotators. To address this, we introduce the Synthetic MRL (SynMRL) benchmark that contains many classes and controllable noise and annotator settings for systematic evaluation. Experiments on synthetic and real-world data show that RETINA outperforms existing multi-rater methods, particularly in high-noise, low-annotator, many-class settings.

Understanding and analyzing human behaviors (actions and interactions of people), voices, and sounds in chaotic events is crucial in many applications, e.g., crowd management, emergency response services. Different from human behaviors in daily life, human behaviors in chaotic events are generally different in how they behave and influence others, and hence are often much more complex. However, currently there is lack of a large video dataset for analyzing human behaviors in chaotic situations. To this end, we create the first large and challenging multi-modal dataset, Chaotic World, that simultaneously provides different levels of fine-grained and dense spatio-temporal annotations of sounds, individual actions and group interaction graphs, and even text descriptions for each scene in each video, thereby enabling a thorough analysis of complicated behaviors in crowds and chaos. Our dataset consists of a total of 299,923 annotated instances for detecting human behaviors for Spatiotemporal Action Localization in chaotic events, 224,275 instances for identifying interactions between people for Behavior Graph Analysis in chaotic events, 336,390 instances for localizing relevant scenes of interest in long videos for Spatiotemporal Event Grounding, and 378,093 instances for triangulating the source of sound for Event Sound Source Localization. Given the practical complexity and challenges in chaotic events (e.g., large crowds, serious occlusions, complicated interaction patterns), our dataset shall be able to facilitate the community to develop, adapt, and evaluate various types of advanced models for analyzing human behaviors in chaotic events. We also design a simple yet effective IntelliCare model with a Dynamic Knowledge Pathfinder module that intelligently learns from multiple tasks and can analyze various aspects of a chaotic scene in a unified architecture. This method achieves promising results in experiments. Dataset and code can be found at https://github.com/sutdcv/Chaotic-World.

In real-world scenarios, many factors may harm face recognition performance,e.g., large pose, bad illumination, low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose anMulti-DegradationFaceRestoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-basedPoseNormalizationModule (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration.

We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to “imagine” a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identity-preserving, and visually pleasant face video clips from a single source face image.

Deep Convolutional Neural Networks (DCNNs) have achieved impressive performance in Single Image Super-Resolution (SISR). To further improve the performance, existing CNN-based methods generally focus on designing deeper architecture of the network. However, we argue blindly increasing network's depth is not the most sensible way. In this paper, we propose a novel end-to-end Residual Neuron Attention Networks (RNAN) for more efficient and effective SISR. Structurally, our RNAN is a sequential integration of the well-designed Global Context-enhanced Residual Groups (GCRGs), which extracts super-resolved features from coarse to fine. Our GCRG is designed with two novelties. Firstly, the Residual Neuron Attention (RNA) mechanism is proposed in each block of GCRG to reveal the relevance of neurons for better feature representation. Furthermore, the Global Context (GC) block is embedded into RNAN at the end of each GCRG for effectively modeling the global contextual information. Experiments results demonstrate that our RNAN achieves the comparable results with state-of-the-art methods in terms of both quantitative metrics and visual quality, however, with simplified network architecture.