Mobile Recording Device Recognition Based Cross-Scale and Multi-Level Representation Learning

This paper introduces a modeling approach that employs multi-level global processing, encompassing both short-term frame-level and long-term sample-level feature scales. In the initial stage of shallow feature extraction, various scales are employed to extract multi-level features, including Mel-Frequency Cepstral Coefficients (MFCC) and pre-Fbank log energy spectrum. The construction of the identification network model involves considering the input two-dimensional temporal features from both frame and sample levels. Specifically, the model initially employs one-dimensional convolution-based Convolutional Long Short-Term Memory (ConvLSTM) to fuse spatiotemporal information and extract short-term frame-level features. Subsequently, bidirectional long Short-Term Memory (BiLSTM) is utilized to learn long-term sample-level sequential representations. The transformer encoder then performs cross-scale, multi-level processing on global frame-level and sample-level features, facilitating deep feature representation and fusion at both levels. Finally, recognition results are obtained through Softmax. Our method achieves an impressive 99.6% recognition accuracy on the CCNU_Mobile dataset, exhibiting a notable improvement of 2% to 12% compared to the baseline system. Additionally, we thoroughly investigate the transferability of our model, achieving an 87.9% accuracy in a classification task on a new dataset.

CAD-NeRF: Learning NeRFs from Uncalibrated Few-view Images by CAD Model Retrieval

Reconstructing from multi-view images is a longstanding problem in 3D vision, where neural radiance fields (NeRFs) have shown great potential and get realistic rendered images of novel views. Currently, most NeRF methods either require accurate camera poses or a large number of input images, or even both. Reconstructing NeRF from few-view images without poses is challenging and highly ill-posed. To address this problem, we propose CAD-NeRF, a method reconstructed from less than 10 images without any known poses. Specifically, we build a mini library of several CAD models from ShapeNet and render them from many random views. Given sparse-view input images, we run a model and pose retrieval from the library, to get a model with similar shapes, serving as the density supervision and pose initializations. Here we propose a multi-view pose retrieval method to avoid pose conflicts among views, which is a new and unseen problem in uncalibrated NeRF methods. Then, the geometry of the object is trained by the CAD guidance. The deformation of the density field and camera poses are optimized jointly. Then texture and density are trained and fine-tuned as well. All training phases are in self-supervised manners. Comprehensive evaluations of synthetic and real images show that CAD-NeRF successfully learns accurate densities with a large deformation from retrieved CAD models, showing the generalization abilities.

Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification

Accurate annotation of educational resources is critical in the rapidly advancing field of online education due to the complexity and volume of content. Existing classification methods face challenges with semantic overlap and distribution imbalance of labels in the multi-label context, which impedes effective personalized learning and resource recommendation. This paper introduces RR2QC, a novel Retrieval Reranking method To multi-label Question Classification by leveraging label semantics and meta-label refinement. Firstly, RR2QC leverages semantic relationships within and across label groups to enhance pre-training strategie in multi-label context. Next, a class center learning task is introduced, integrating label texts into downstream training to ensure questions consistently align with label semantics, retrieving the most relevant label sequences. Finally, this method decomposes labels into meta-labels and trains a meta-label classifier to rerank the retrieved label sequences. In doing so, RR2QC enhances the understanding and prediction capability of long-tail labels by learning from meta-labels frequently appearing in other labels. Addtionally, a Math LLM is used to generate solutions for questions, extracting latent information to further refine the model's insights. Experimental results demonstrate that RR2QC outperforms existing classification methods in Precision@k and F1 scores across multiple educational datasets, establishing it as a potent enhancement for online educational content utilization.

Variable Aperture Bokeh Rendering via Customized Focal Plane Guidance

Bokeh rendering is one of the most popular techniques in photography. It can make photographs visually appealing, forcing users to focus their attentions on particular area of image. However, achieving satisfactory bokeh effect usually presents significant challenge, since mobile cameras with restricted optical systems are constrained, while expensive high-end DSLR lens with large aperture should be needed. Therefore, many deep learning-based computational photography methods have been developed to mimic the bokeh effect in recent years. Nevertheless, most of these methods were limited to rendering bokeh effect in certain single aperture. There lacks user-friendly bokeh rendering method that can provide precise focal plane control and customised bokeh generation. There as well lacks authentic realistic bokeh dataset that can potentially promote bokeh learning on variable apertures. To address these two issues, in this paper, we have proposed an effective controllable bokeh rendering method, and contributed a Variable Aperture Bokeh Dataset (VABD). In the proposed method, user can customize focal plane to accurately locate concerned subjects and select target aperture information for bokeh rendering. Experimental results on public EBB! benchmark dataset and our constructed dataset VABD have demonstrated that the customized focal plane together aperture prompt can bootstrap model to simulate realistic bokeh effect. The proposed method has achieved competitive state-of-the-art performance with only 4.4M parameters, which is much lighter than mainstream computational bokeh models. The contributed dataset and source codes will be released on github https://github.com/MoTong-AI-studio/VABM.

Multi-Scale Deformable Transformers for Student Learning Behavior Detection in Smart Classroom

The integration of Artificial Intelligence into the modern educational system is rapidly evolving, particularly in monitoring student behavior in classrooms, a task traditionally dependent on manual observation. This conventional method is notably inefficient, prompting a shift toward more advanced solutions like computer vision. However, existing target detection models face significant challenges such as occlusion, blurring, and scale disparity, which are exacerbated by the dynamic and complex nature of classroom settings. Furthermore, these models must adeptly handle multiple target detection. To overcome these obstacles, we introduce the Student Learning Behavior Detection with Multi-Scale Deformable Transformers (SCB-DETR), an innovative approach that utilizes large convolutional kernels for upstream feature extraction, and multi-scale feature fusion. This technique significantly improves the detection capabilities for multi-scale and occluded targets, offering a robust solution for analyzing student behavior. SCB-DETR establishes an end-to-end framework that simplifies the detection process and consistently outperforms other deep learning methods. Employing our custom Student Classroom Behavior (SCBehavior) Dataset, SCB-DETR achieves a mean Average Precision (mAP) of 0.626, which is a 1.5% improvement over the baseline model's mAP and a 6% increase in AP50. These results demonstrate SCB-DETR's superior performance in handling the uneven distribution of student behaviors and ensuring precise detection in dynamic classroom environments.

Visual Prompting in LLMs for Enhancing Emotion Recognition

Vision Large Language Models (VLLMs) are transforming the intersection of computer vision and natural language processing. Nonetheless, the potential of using visual prompts for emotion recognition in these models remains largely unexplored and untapped. Traditional methods in VLLMs struggle with spatial localization and often discard valuable global context. To address this problem, we propose a Set-of-Vision prompting (SoV) approach that enhances zero-shot emotion recognition by using spatial information, such as bounding boxes and facial landmarks, to mark targets precisely. SoV improves accuracy in face count and emotion categorization while preserving the enriched image context. Through a battery of experimentation and analysis of recent commercial or open-source VLLMs, we evaluate the SoV model's ability to comprehend facial expressions in natural environments. Our findings demonstrate the effectiveness of integrating spatial visual prompts into VLLMs for improving emotion recognition performance.

LLDif: Diffusion Models for Low-light Emotion Recognition

This paper introduces LLDif, a novel diffusion-based facial expression recognition (FER) framework tailored for extremely low-light (LL) environments. Images captured under such conditions often suffer from low brightness and significantly reduced contrast, presenting challenges to conventional methods. These challenges include poor image quality that can significantly reduce the accuracy of emotion recognition. LLDif addresses these issues with a novel two-stage training process that combines a Label-aware CLIP (LA-CLIP), an embedding prior network (PNET), and a transformer-based network adept at handling the noise of low-light images. The first stage involves LA-CLIP generating a joint embedding prior distribution (EPD) to guide the LLformer in label recovery. In the second stage, the diffusion model (DM) refines the EPD inference, ultilising the compactness of EPD for precise predictions. Experimental evaluations on various LL-FER datasets have shown that LLDif achieves competitive performance, underscoring its potential to enhance FER applications in challenging lighting conditions.

Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News

In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos. Traditional methods relying on RGB images are resource-intensive, whereas our approach with Facial Landmark Emotion Recognition (FLER) offers a simplified yet effective alternative. By leveraging Graph Neural Networks (GNNs) to analyze the geometric and spatial relationships of facial landmarks, our method enhances the understanding and accuracy of emotion recognition. We discuss the advancements and challenges in deep learning techniques for emotion recognition, particularly focusing on Graph Neural Networks (GNNs) and Transformers. Our experimental results demonstrate the viability and potential of our dataset as a benchmark, setting a new direction for future research in emotion recognition technologies. The codes and models are at: https://github.com/wangzhifengharrison/benchmark_real_news

Dual-Path Coupled Image Deraining Network via Spatial-Frequency Interaction

Transformers have recently emerged as a significant force in the field of image deraining. Existing image deraining methods utilize extensive research on self-attention. Though showcasing impressive results, they tend to neglect critical frequency information, as self-attention is generally less adept at capturing high-frequency details. To overcome this shortcoming, we have developed an innovative Dual-Path Coupled Deraining Network (DPCNet) that integrates information from both spatial and frequency domains through Spatial Feature Extraction Block (SFEBlock) and Frequency Feature Extraction Block (FFEBlock). We have further introduced an effective Adaptive Fusion Module (AFM) for the dual-path feature aggregation. Extensive experiments on six public deraining benchmarks and downstream vision tasks have demonstrated that our proposed method not only outperforms the existing state-of-the-art deraining method but also achieves visually pleasuring results with excellent robustness on downstream vision tasks.

LRDif: Diffusion Models for Under-Display Camera Emotion Recognition

This study introduces LRDif, a novel diffusion-based framework designed specifically for facial expression recognition (FER) within the context of under-display cameras (UDC). To address the inherent challenges posed by UDC's image degradation, such as reduced sharpness and increased noise, LRDif employs a two-stage training strategy that integrates a condensed preliminary extraction network (FPEN) and an agile transformer network (UDCformer) to effectively identify emotion labels from UDC images. By harnessing the robust distribution mapping capabilities of Diffusion Models (DMs) and the spatial dependency modeling strength of transformers, LRDif effectively overcomes the obstacles of noise and distortion inherent in UDC environments. Comprehensive experiments on standard FER datasets including RAF-DB, KDEF, and FERPlus, LRDif demonstrate state-of-the-art performance, underscoring its potential in advancing FER applications. This work not only addresses a significant gap in the literature by tackling the UDC challenge in FER but also sets a new benchmark for future research in the field.