Reviewerly/exHarmony huggingface dataset:
Plausible Uncertainties for Human Pose Regression

Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical domains involving human-machine interaction, such as autonomous driving or semi-automated work environments. Avoiding costly mistakes is synonymous with anticipating failure in model predictions, which necessitates meta-judgments on the accuracy of the applied models. Here, we propose a straightforward human pose regression framework to examine the behavior of two established methods for simultaneous aleatoric and epistemic uncertainty estimation: maximum a-posteriori (MAP) estimation with Monte-Carlo variational inference and deep evidential regression (DER). First, we evaluate both approaches on the quality of their predicted variances and whether these truly capture the expected model error. The initial assessment indicates that both methods exhibit the overconfidence issue common in deep probabilistic models. This observation motivates our implementation of an additional recalibration step to extract reliable confidence intervals. We then take a closer look at deep evidential regression, which, to our knowledge, is applied comprehensively for the first time to the HPE problem. Experimental results indicate that DER behaves as expected in challenging and adverse conditions commonly occurring in HPE and that the predicted uncertainties match their purported aleatoric and epistemic sources. Notably, DER achieves smooth uncertainty estimates without the need for a costly sampling step, making it an attractive candidate for uncertainty estimation on resource-limited platforms.
Embedded Device Forensics with a Case Study of Raspberry Pi

The ever-evolving digital forensics is now coping up with latest trends in cyber-attacks. Among them Embedded Device forensics is a trending topic and that needs more research and development. Today, with the prevalence of miniaturized computers, the possibility of launching a cyber-attack using a physically hidden device on a premises is a facile task. Embedded device forensics involves identification, data extraction and analysis from miniaturised computers involved in a cyber-crime. This paper includes the research conducted on such a cyber-crime using the most convenient and widely used microcomputer, Raspberry Pi(RPi). In this research, RPi device is used to create a MITM attack and methods for extracting and analysing the suspect device storage media is well explained. Initially the files with evidential artifacts are explained and later the techniques and tools for extracting and analysing these files are explained in detail. The paper also explains the scope of Live Forensics and password recovery in embedded devices. This paper provides an insight for investigators and crime analysts who come across with a case where micro computers are involved as suspect device.
Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising

Real-world single image denoising is crucial and practical in computer vision. Bayesian inversions combined with score priors now have proven effective for single image denoising but are limited to white Gaussian noise. Moreover, applying existing score-based methods for real-world denoising requires not only the explicit train of score priors on the target domain but also the careful design of sampling procedures for posterior inference, which is complicated and impractical. To address these limitations, we propose a score priors-guided deep variational inference, namely ScoreDVI, for practical real-world denoising. By considering the deep variational image posterior with a Gaussian form, score priors are extracted based on easily accessible minimum MSE Non-i.i.d Gaussian denoisers and variational samples, which in turn facilitate optimizing the variational image posterior. Such a procedure adaptively applies cheap score priors to denoising. Additionally, we exploit a Non-i.i.d Gaussian mixture model and variational noise posterior to model the real-world noise. This scheme also enables the pixel-wise fusion of multiple image priors and variational image posteriors. Besides, we develop a noise-aware prior assignment strategy that dynamically adjusts the weight of image priors in the optimization. Our method outperforms other single image-based real-world denoising methods and achieves comparable performance to dataset-based unsupervised methods.
Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction

Deep learning is commonly used to reconstruct HDR images from LDR images. LDR stack-based methods are used for single-image HDR reconstruction, generating an HDR image from a deep learning-generated LDR stack. However, current methods generate the stack with predetermined exposure values (EVs), which may limit the quality of HDR reconstruction. To address this, we propose the continuous exposure value representation (CEVR), which uses an implicit function to generate LDR images with arbitrary EVs, including those unseen during training. Our approach generates a continuous stack with more images containing diverse EVs, significantly improving HDR reconstruction. We use a cycle training strategy to supervise the model in generating continuous EV LDR images without corresponding ground truths. Our CEVR model outperforms existing methods, as demonstrated by experimental results.
Humans in 4D: Reconstructing and Tracking Humans with Transformers

We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficult to reconstruct from single images. To analyze video, we use 3D reconstructions from HMR 2.0 as input to a tracking system that operates in 3D. This enables us to deal with multiple people and maintain identities through occlusion events. Our complete approach, 4DHumans, achieves state-of-the-art results for tracking people from monocular video. Furthermore, we demonstrate the effectiveness of HMR 2.0 on the downstream task of action recognition, achieving significant improvements over previous pose-based action recognition approaches. Our code and models are available on the project website: https://shubham-goel.github.io/4dhumans/.
En-BB84: Advancements in Secure Quantum Key Distribution with Improved Error Resilience and Built-in Authentication

Quantum key distribution (QKD) is a method that enables secure communication between two parties by applying the ideas of quantum physics. By minimizing information leakage that can happen during the key generation process, privacy amplification is a technique used to improve the security of QKD. The technique involves processing the raw key obtained from QKD through a randomization procedure to remove any residual information that may have been leaked during the quantum transmission. While privacy amplification can significantly improve the security of QKD, the effectiveness of the technique is limited by the quality of the initial raw key. The BB84 QKD protocol has been improved with the proposed En-BB84 protocol, which includes built-in authentication and integrity checks. The En-BB84 protocol ensures secure distribution of secret keys and protects against tampering by attackers. Key distillation and error correction techniques are employed to extract a shorter, more secure shared secret key and correct errors introduced during qubit transmission. The protocol utilizes a four-state QKD approach, expanding on the traditional two-state scheme of BB84. A simulation of the En-BB84 protocol is demonstrated using a quantum circuit implemented with Qiskit in Python. The circuit employs various gates and operations, including Hadamard, CX, controlled swap, and RZ gates, to achieve encoding and error correction. Furthermore, we have designed a better protocol with improved error rates by incorporating improvisations on the BB84 protocol. Experimental results indicate the successful integration of the B92 error correction scheme into the modified BB84 protocol, enhancing the security and performance of QKD systems.
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution

Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in high-resolution (HR) RGB images from the same scene. The critical step of this task is to effectively extract domain-shared and domain-private RGB/depth features. In addition, three detailed issues, namely blurry edges, noisy surfaces, and over-transferred RGB texture, need to be addressed. In this paper, we propose the Spherical Space feature Decomposition Network (SSDNet) to solve the above issues. To better model cross-modality features, Restormer block-based RGB/depth encoders are employed for extracting local-global features. Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features. Shared features of RGB are fused with the depth features to complete the GDSR task. Subsequently, a spherical contrast refinement (SCR) module is proposed to further address the detail issues. Patches that are classified according to imperfect categories are input into the SCR module, where the patch features are pulled closer to the ground truth and pushed away from the corresponding imperfect samples in the spherical feature space via contrastive learning. Extensive experiments demonstrate that our method can achieve state-of-the-art results on four test datasets, as well as successfully generalize to real-world scenes. The code is available at https://github.com/Zhaozixiang1228/GDSR-SSDNet.
Deep Incubation: Training Large Models by Divide-and-Conquering

Recent years have witnessed a remarkable success of large deep learning models. However, training these models is challenging due to high computational costs, painfully slow convergence, and overfitting issues. In this paper, we present Deep Incubation, a novel approach that enables the efficient and effective training of large models by dividing them into smaller sub-modules which can be trained separately and assembled seamlessly. A key challenge for implementing this idea is to ensure the compatibility of the independently trained sub-modules. To address this issue, we first introduce a global, shared meta model, which is leveraged to implicitly link all the modules together, and can be designed as an extremely small network with negligible computational overhead. Then we propose a module incubation algorithm, which trains each sub-module to replace the corresponding component of the meta model and accomplish a given learning task. Despite the simplicity, our approach effectively encourages each sub-module to be aware of its role in the target large model, such that the finally-learned sub-modules can collaborate with each other smoothly after being assembled. Empirically, our method can outperform end-to-end (E2E) training in well-established training setting and shows transferable performance gain for downstream tasks (e.g., object detection and image segmentation on COCO and ADE20K). Our code is available at https://github.com/LeapLabTHU/Deep-Incubation.
Chord: Category-level Hand-held Object Reconstruction via Shape Deformation

In daily life, humans utilize hands to manipulate objects. Modeling the shape of objects that are manipulated by the hand is essential for AI to comprehend daily tasks and to learn manipulation skills. However, previous approaches have encountered difficulties in reconstructing the precise shapes of hand-held objects, primarily owing to a deficiency in prior shape knowledge and inadequate data for training. As illustrated, given a particular type of tool, such as a mug, despite its infinite variations in shape and appearance, humans have a limited number of ‘effective’ modes and poses for its manipulation. This can be attributed to the fact that humans have mastered the shape prior of the ‘mug’ category, and can quickly establish the corresponding relations between different mug instances and the prior, such as where the rim and handle are located. In light of this, we propose a new method, Chord, for Category-level Hand-held Object Reconstruction via shape Deformation. Chord deforms a categorical shape prior for reconstructing the intra-class objects. To ensure accurate reconstruction, we empower Chord with three types of awareness: appearance, shape, and interacting pose. In addition, we have constructed a new dataset, Comic, of category-level hand-object interaction. Comic contains a rich array of object instances, materials, hand interactions, and viewing directions. Extensive evaluation shows that Chord outperforms state-of-the-art approaches in both quantitative and qualitative measures. Code, model, and datasets are available at https://kailinli.github.io/CHORD
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers

Vector-quantized image modeling has shown great potential in synthesizing high-quality images. However, generating high-resolution images remains a challenging task due to the quadratic computational overhead of the self-attention process. In this study, we seek to explore a more efficient two-stage framework for high-resolution image generation with improvements in the following three aspects. (1) Based on the observation that the first quantization stage has solid local property, we employ a local attention-based quantization model instead of the global attention mechanism used in previous methods, leading to better efficiency and reconstruction quality. (2) We emphasize the importance of multi-grained feature interaction during image generation and introduce an efficient attention mechanism that combines global attention (long-range semantic consistency within the whole image) and local attention (fined-grained details). This approach results in faster generation speed, higher generation fidelity, and improved resolution. (3) We propose a new generation pipeline incorporating autoencoding training and autoregressive generation strategy, demonstrating a better paradigm for image synthesis. Extensive experiments demonstrate the superiority of our approach in high-quality and high-resolution image reconstruction and generation.
On the use of Vision-Language models for Visual Sentiment Analysis: a study on CLIP

This work presents a study on how to exploit the CLIP embedding space to perform Visual Sentiment Analysis. We experiment with two architectures built on top of the CLIP embedding space, which we denote by CLIP-E. We train the CLIP-E models with WEBEmo, the largest publicly available and manually labeled benchmark for Visual Sentiment Analysis, and perform two sets of experiments. First, we test on WEBEmo and compare the CLIP-E architectures with state-of-the-art (SOTA) models and with CLIP Zero-Shot. Second, we perform cross dataset evaluation, and test the CLIP-E architectures trained with WEBEmo on other Visual Sentiment Analysis benchmarks. Our results show that the CLIP-E approaches outperform SOTA models in WEBEmo fine grained categorization, and they also generalize better when tested on datasets that have not been seen during training. Interestingly, we observed that for the FI dataset, CLIP Zero-Shot produces better accuracies than SOTA models and CLIP-E trained on WEBEmo. These results motivate several questions that we discuss in this paper, such as how we should design new benchmarks and evaluate Visual Sentiment Analysis, and whether we should keep designing tailored Deep Learning models for Visual Sentiment Analysis or focus our efforts on better using the knowledge encoded in large vision-language models such as CLIP for this task. Our code is available at https://github.com/cristinabustos16/CLIP-E.
GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation

Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. However, the tight coupling between the current text encoder and image decoder in T2I models makes it challenging to replace or upgrade. Such changes often require massive fine-tuning or even training from scratch with the prohibitive expense. To address this problem, we propose GlueGen, which applies a newly proposed GlueNet model to align features from single-modal or multi-modal encoders with the latent space of an existing T2I model. The approach introduces a new training objective that leverages parallel corpora to align the representation spaces of different encoders. Empirical results show that GlueNet can be trained efficiently and enables various capabilities beyond previous state-of-the-art models: 1) multilingual language models such as XLM-Roberta can be aligned with existing T2I models, allowing for the generation of high-quality images from captions beyond English; 2) GlueNet can align multi-modal encoders such as AudioCLIP with the Stable Diffusion model, enabling sound-to-image generation; 3) it can also upgrade the current text encoder of the latent diffusion model for challenging case generation. By the alignment of various feature representations, the GlueNet allows for flexible and efficient integration of new functionality into existing T2I models and sheds light on X-to-image (X2I) generation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground

In video action recognition, shortcut static features can interfere with the learning of motion features, resulting in poor out-of-distribution (OOD) generalization. The video background is clearly a source of static bias, but the video foreground, such as the clothing of the actor, can also provide static bias. In this paper, we empirically verify the existence of foreground static bias by creating test videos with conflicting signals from the static and moving portions of the video. To tackle this issue, we propose a simple yet effective technique, StillMix, to learn robust action representations. Specifically, StillMix identifies bias-inducing video frames using a 2D reference network and mixes them with videos for training, serving as effective bias suppression even when we cannot explicitly extract the source of bias within each video frame or enumerate types of bias. Finally, to precisely evaluate static bias, we synthesize two new benchmarks, SCUBA for static cues in the background, and SCUFO for static cues in the foreground. With extensive experiments, we demonstrate that StillMix mitigates both types of static bias and improves video representations for downstream applications. Code is available at https://github.com/lihaoxin05/StillMix.
Visible-Infrared Person Re-Identification via Semantic Alignment and Affinity Inference

Visible-infrared person re-identification (VI-ReID) focuses on matching the pedestrian images of the same identity captured by different modality cameras. The part-based methods achieve great success by extracting fine-grained features from feature maps. But most existing part-based methods employ horizontal division to obtain part features suffering from misalignment caused by irregular pedestrian movements. Moreover, most current methods use Euclidean or cosine distance of the output features to measure the similarity without considering the pedestrian relationships. Misaligned part features and naive inference methods both limit the performance of existing works. We propose a Semantic Alignment and Affinity Inference framework (SAAI), which aims to align latent semantic part features with the learnable prototypes and improve inference with affinity information. Specifically, we first propose semantic-aligned feature learning that employs the similarity between pixelwise features and learnable prototypes to aggregate the latent semantic part features. Then, we devise an affinity inference module to optimize the inference with pedestrian relationships. Comprehensive experimental results conducted on the SYSU-MM01 and RegDB datasets demonstrate the favorable performance of our SAAI framework. Our code will be released at https://github.com/xiaoye-hhh/SAAI.
ATT3D: Amortized Text-to-3D Object Synthesis

Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently achieved high-quality results but requires a lengthy, per-prompt optimization to create 3D objects. To address this, we amortize optimization over text prompts by training on many prompts simultaneously with a unified model, instead of separately. With this, we share computation across a prompt set, training in less time than per-prompt optimization. Our framework – Amortized Text-to-3D (ATT3D) – enables knowledge sharing between prompts to generalize to unseen setups and smooth interpolations between text for novel assets and simple animations.
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds

Attributed to the unstructured and sparse nature of point clouds, the transformer shows greater potential in point clouds data processing. However, the recent query-based 3D detectors usually project the features acquired from a sparse backbone into the structured and compact Bird’s Eye View(BEV) plane before adopting the transformer, which destroys the sparsity of features, introducing empty tokens and additional resource consumption for the transformer. To this end, in this paper, we propose a novel query-based 3D detector called Clusterformer, our Clusterformer regards each object as a cluster of 3D space which mainly consists of the non-empty voxels belonging to the same object, and leverages the cluster to conduct the transformer decoder to generate the proposals from the sparse voxel features directly. Such cluster-based transformer structure can effectively improve the performance and convergence speed of query-based detectors by making use of the object prior information contained in the clusters. Additionally, we introduce a Query2Key strategy to enhance the key and value features with the object-level information iteratively in our cluster-based transformer structure. Experimental results show that the proposed Clusterformer outperforms the previous query-based detectors with a lower latency and memory usage, which achieves state-of-the-art performance on the Waymo Open Datasets and KITTI Datasets.
Exploring Predicate Visual Context in Detecting of Human–Object Interactions

Recently, the DETR framework has emerged as the dominant approach for human–object interaction (HOI) research. In particular, two-stage transformer-based HOI detectors are amongst the most performant and training-efficient approaches. However, these often condition HOI classification on object features that lack fine-grained contextual information, eschewing pose and orientation information in favour of visual cues about object identity and box extremities. This naturally hinders the recognition of complex or ambiguous interactions. In this work, we study these issues through visualisations and carefully designed experiments. Accordingly, we investigate how best to re-introduce image features via cross-attention. With an improved query design, extensive exploration of keys and values, and box pair positional embeddings as spatial guidance, our model with enhanced predicate visual context (PViC) outperforms state-of-the-art methods on the HICO-DET and V-COCO benchmarks, while maintaining low training cost.