Reviewerly/exHarmony huggingface dataset:
M-FUSE: Multi-frame Fusion for Scene Flow Estimation

Recently, neural network for scene flow estimation show impressive results on automotive data such as the KITTI benchmark. However, despite of using sophisticated rigidity assumptions and parametrizations, such networks are typically limited to only two frame pairs which does not allow them to exploit temporal information. In our paper we address this shortcoming by proposing a novel multi-frame approach that considers an additional preceding stereo pair. To this end, we proceed in two steps: Firstly, building upon the recent RAFT-3D approach, we develop an improved two-frame baseline by incorporating an advanced stereo method. Secondly, and even more importantly, exploiting the specific modeling concepts of RAFT-3D, we propose a U-Net architecture that performs a fusion of forward and backward flow estimates and hence allows to integrate temporal information on demand. Experiments on the KITTI benchmark do not only show that the advantages of the improved baseline and the temporal fusion approach complement each other, they also demonstrate that the computed scene flow is highly accurate. More precisely, our approach ranks second overall and first for the even more challenging foreground objects, in total outperforming the original RAFT-3D method by more than 16%. Code is available at https://github.com/cv-stuttgart/M-FUSE.
Deep Learning for Image Segmentation: A Focus on Medical Imaging

Image segmentation is crucial for various research areas.Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging.With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation.This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation.First, segmentation models are categorized based on their model architecture and primary working principle.Then, CNN categories are described, and various models are discussed within each category.Compared with other existing surveys, several applications with multiple architectural adaptations are discussed within each category.A comparative summary is included to give the reader insights into utilized architectures in different applications and datasets.This study focuses on medical image segmentation applications, where the most widely used architectures are illustrated, and other promising models are suggested that have proven their success in different domains.Finally, the present work discusses current limitations and solutions along with future trends in the field.
SHARDS: Efficient SHAdow Removal using Dual Stage Network for High-Resolution Images

Shadow Removal is an important and widely researched topic in computer vision. Recent advances in deep learning have resulted in addressing this problem by using convolutional neural networks (CNNs) similar to other vision tasks. But these existing works are limited to low-resolution images. Furthermore, the existing methods rely on heavy network architectures which cannot be deployed on resource-constrained platforms like smartphones. In this paper, we propose SHARDS, a shadow removal method for high-resolution images. The proposed method solves shadow removal for high-resolution images in two stages using two lightweight networks: a Low-resolution Shadow Removal Network (LSRNet) followed by a Detail Refinement Network (DRNet). LSRNet operates at low-resolution and computes a low-resolution, shadow-free output. It achieves state-of-the-art results on standard datasets with 65x lesser network parameters than existing methods. This is followed by DRNet, which is tasked to refine the low-resolution output to a high-resolution output using the high-resolution input shadow image as guidance. We construct high-resolution shadow removal datasets and through our experiments, prove the effectiveness of our proposed method on them. It is then demonstrated that this method can be deployed on modern day smartphones and is the first of its kind solution that can efficiently (2.4secs) perform shadow removal for high-resolution images (12MP) in these devices. Like many existing approaches, our shadow removal network relies on a shadow region mask as input to the network. To complement the lightweight shadow removal network, we also propose a lightweight shadow detector in this paper.
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials

There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in the real-world scenarios. To simulate the wild scenes, this paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models. The suggested CDDB designs multiple evaluations on the detection over easy, hard, and long sequence of deepfake tasks, with a set of appropriate measures. In addition, we exploit multiple approaches to adapt multiclass incremental learning methods, commonly used in the continual visual recognition, to the continual deepfake detection problem. We evaluate existing methods, including their adapted ones, on the proposed CDDB. Within the proposed benchmark, we explore some commonly known essentials of standard continual learning. Our study provides new insights on these essentials in the context of continual deepfake detection. The suggested CDDB is clearly more challenging than the existing benchmarks, which thus offers a suitable evaluation avenue to the future research. Both data and code are available at https://github.com/Coral79/CDDB.
Dynamic Re-weighting for Long-tailed Semi-supervised Learning

Semi-supervised Learning (SSL) reduces significant human annotations by simply demanding a small number of labelled samples and a large number of unlabelled samples. The research community has often developed SSL regarding the nature of a balanced data set; in contrast, real data is often imbalanced or even long-tailed. The need to study SSL under imbalance is therefore critical. In this paper, we essentially extend FixMatch (a SSL method) to the imbalanced case. We find that the unlabeled data is as well highly imbalanced during the training process; in this respect we propose a re-weighting solution based on the effective number. Furthermore, since prediction uncertainty leads to temporal variations in the number of pseudo-labels, we are innovative in proposing a dynamic reweighting scheme on the unlabeled data. The simplicity and validity of our method are backed up by experimental evidence. Especially on CIFAR-10, CIFAR-100, ImageNet127 data sets, our approach provides the strongest results against previous methods across various scales of imbalance.
Effective Invertible Arbitrary Image Rescaling

Great successes have been achieved using deep learning techniques for image super-resolution (SR) with fixed scales. To increase its real world applicability, numerous models have also been proposed to restore SR images with arbitrary scale factors, including asymmetric ones where images are resized to different scales along horizontal and vertical directions. Though most models are only optimized for the unidirectional upscaling task while assuming a predefined downscaling kernel for low-resolution (LR) inputs, recent models based on Invertible Neural Networks (INN) are able to increase upscaling accuracy significantly by optimizing the downscaling and upscaling cycle jointly. However, limited by the INN architecture, it is constrained to fixed integer scale factors and requires one model for each scale. Without increasing model complexity, a simple and effective invertible arbitrary rescaling network (IARN) is proposed to achieve arbitrary image rescaling by training only one model in this work. Using innovative components like position-aware scale encoding and preemptive channel splitting, the network is optimized to convert the non-invertible rescaling cycle to an effectively invertible process. It is shown to achieve a state-of-the-art (SOTA) performance in bidirectional arbitrary rescaling without compromising perceptual quality in LR outputs. It is also demonstrated to perform well on tests with asymmetric scales using the same network architecture.
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation

Universal Domain Adaptation aims to transfer the knowledge between the datasets by handling two shifts: domain-shift and category-shift. The main challenge is correctly distinguishing the unknown target samples while adapting the distribution of known class knowledge from source to target. Most existing methods approach this problem by first training the target adapted known classifier and then relying on the single threshold to distinguish unknown target samples. However, this simple threshold-based approach prevents the model from considering the underlying complexities existing between the known and unknown samples in the high-dimensional feature space. In this paper, we propose a new approach in which we use two sets of feature points, namely dual Classifiers for Prototypes and Reciprocals (CPR). Our key idea is to associate each prototype with corresponding known class features while pushing the reciprocals apart from these prototypes to locate them in the potential unknown feature space. The target samples are then classified as unknown if they fall near any reciprocals at test time. To successfully train our framework, we collect the partial, confident target samples that are classified as known or unknown through on our proposed multi-criteria selection. We then additionally apply the entropy loss regularization to them. For further adaptation, we also apply standard consistency regularization that matches the predictions of two different views of the input to make more compact target feature space. We evaluate our proposal, CPR, on three standard benchmarks and achieve comparable or new state-of-the-art results. We also provide extensive ablation experiments to verify our main design choices in our framework.
Is Bigger Always Better? An Empirical Study on Efficient Architectures for Style Transfer and Beyond

Network architecture plays a pivotal role in style transfer. Most existing algorithms use VGG19 as the feature extractor, which incurs a high computational cost. In this work, we conduct an empirical study on the popular network architectures and find that some more efficient networks can replace VGG19 while having comparable style transfer performance. Beyond that, we show that an efficient network can be further accelerated by removing its empty channels via a simple channel pruning method tweaked for style transfer. To prevent the potential performance drop due to using a more lightweight network and obtain better style transfer results, we introduce a more accurate deep feature alignment strategy to improve existing style transfer modules. Taking GoogLeNet as an exemplary efficient network, the pruned GoogLeNet with the improved style transfer module is 2.3 ~ 107.4× faster than the state-of-the-art approaches and can achieve 68.03 FPS on 512×512 images. Extensive experiments demonstrate that VGG19 can be replaced by a more lightweight network with significantly improved efficiency and comparable style transfer quality.
Visually explaining 3D-CNN predictions for video classification with an adaptive occlusion sensitivity analysis

This paper proposes a method for visually explaining the decision-making process of 3D convolutional neural networks (CNN) with a temporal extension of occlusion sensitivity analysis. The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporalspatial data space and then measure the change degree in the output score. The occluded volume data that produces a larger change degree is regarded as a more critical element for classification. However, while the occlusion sensitivity analysis is commonly used to analyze single image classification, it is not so straightforward to apply this idea to video classification as a simple fixed cuboid cannot deal with the motions. To this end, we adapt the shape of a 3D occlusion mask to complicated motions of target objects. Our flexible mask adaptation is performed by considering the temporal continuity and spatial co-occurrence of the optical flows extracted from the input video data. We further propose to approximate our method by using the first-order partial derivative of the score with respect to an input image to reduce its computational cost. We demonstrate the effectiveness of our method through various and extensive comparisons with the conventional methods in terms of the deletion/insertion metric and the pointing metric on the UCF101. The code is available at: https://github.com/uchiyama33/AOSA.
LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

Robust watermarking requires finding invariant features under multiple attacks to ensure correct extraction.Deep learning has extremely powerful in extracting features, and watermarking algorithms based on deep learning have attracted widespread attention.Most existing methods use 3 × 3 small kernel convolution to extract image features and embed the watermarking.However, the effective perception fields for small kernel convolution are extremely confined, so the pixels that each watermarking can affect are restricted, thus limiting the performance of the watermarking.To address these problems, we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss functions.It uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1 × 1 convolution to achieve adaptability in the channel dimension.Subsequently, the modification of the embedded watermarking on the cover image is extended to more pixels.Because the magnitude and convergence rates of each loss function are different, an adaptive loss weight assignment strategy is proposed to make the weights participate in the network training together and adjust the weight dynamically.Further, a high-frequency wavelet loss is proposed, by which the watermarking is restricted to only the low-frequency wavelet sub-bands, thereby enhancing the robustness of watermarking against image compression.The experimental results show that the peak signal-to-noise ratio (PSNR) of the encoded image reaches 40.12, the structural similarity (SSIM) reaches 0.9721, and the watermarking has good robustness against various types of noise.
Multi-view Tracking Using Weakly Supervised Human Motion Prediction

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and infer people’s presence in individual frames from these. This enables to enforce consistency both over time and across views of a single temporal frame. We validate our approach on the PETS2009 and WILDTRACK datasets and demonstrate that it outperforms state-of-the-art methods.
Representation Disentanglement in Generative Models with Contrastive Learning

Contrastive learning has shown its effectiveness in image classification and generation. Recent works apply contrastive learning to the discriminator of the Generative Adversarial Networks. However, there is little work exploring if contrastive learning can be applied to the encoderdecoder structure to learn disentangled representations. In this work, we propose a simple yet effective method via incorporating contrastive learning into latent optimization, where we name it ContraLORD. Specifically, we first use a generator to learn discriminative and disentangled embeddings via latent optimization. Then an encoder and two momentum encoders are applied to dynamically learn disentangled information across a large number of samples with content-level and residual-level contrastive loss. In the meanwhile, we tune the encoder with the learned embeddings in an amortized manner. We evaluate our approach on ten benchmarks regarding representation disentanglement and linear classification. Extensive experiments demonstrate the effectiveness of our ContraLORD on learning both discriminative and generative representations.
Skew-Robust Human-Object Interactions in Videos

Humans are, arguably, one of the most important regions of interest in a visual analysis pipeline. Detecting how the human interacts with the surrounding environment, thus, becomes an important problem and has several potential use-cases. While this has been adequately addressed in the literature in the image setting, there exist very few methods addressing the case for in-the-wild videos. The problem is further exacerbated by the high degree of label skew. To this end, we propose SERVO-HOI, a robust end-to-end framework for recognizing human-object interactions from a video, particularly in high label-skew settings. The network contextualizes multiple image representations and is trained to explicitly handle dataset skew. We propose and analyse methods to address the long-tail distribution of the labels and show improvements on the tail-labels. SeRVo-HOI outperforms the state-of-the-art by a significant margin (21.1% vs 17.6% mAP) on the large-scale, in-the-wild VidHOI dataset while particularly demonstrating solid improvements in the tail-classes (19.9% vs 17.3% mAP).
SALAD : Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection

We present a novel method, SALAD, for the challenging vision task of adapting a pre-trained "source" domain network to a "target" domain, with a small budget for annotation in the "target" domain and a shift in the label space. Further, the task assumes that the source data is not available for adaptation, due to privacy concerns or otherwise. We postulate that such systems need to jointly optimize the dual task of (i) selecting fixed number of samples from the target domain for annotation and (ii) transfer of knowledge from the pre-trained network to the target domain. To do this, SALAD consists of a novel Guided Attention Transfer Network (GATN) and an active learning function, H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AL</inf> . The GATN enables feature distillation from pre-trained network to the target network, complemented with the target samples mined by H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AL</inf> using transfer-ability and uncertainty criteria. SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection; (ii) it can handle shifts in output label space from the pre-trained source network to the target domain; (iii) it does not require access to source data for adaptation. We conduct extensive experiments across 3 visual tasks, viz. digits classification (MNIST, SVHN, VISDA), synthetic (GTA5) to real (CityScapes) image segmentation, and document layout detection (PubLayNet to DSSE). We show that our source-free approach, SALAD, results in an improvement of 0.5%−31.3% (across datasets and tasks) over prior adaptation methods that assume access to large amounts of annotated source data for adaptation. Code is available here.
CoNMix for Source-free Single and Multi-target Domain Adaptation

This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of Consistency with Nuclear-Norm Maximization and MixUp knowledge distillation (CoNMix) as a solution to this problem. The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing. The source-free approach leverages target pseudo labels, which can be noisy, to improve the target adaptation. We introduce consistency between label preserving augmentations and utilize pseudo label refinement methods to reduce noisy pseudo labels. Further, we propose novel MixUp Knowledge Distillation (MKD) for better generalization on multiple target domains using various source-free STDA models. We also show that the Vision Transformer (VT) backbone gives better feature representation with improved domain transferability and class discriminability. Our proposed framework achieves the state-of-the-art (SOTA) results in various paradigms of source-free STDA and MTDA settings on popular domain adaptation datasets like Office-Home, Office-Caltech, and DomainNet. Project Page: https://sites.google.com/view/conmix-vcl
AFPSNet: Multi-Class Part Parsing based on Scaled Attention and Feature Fusion

Multi-class part parsing is a dense prediction task that seeks to simultaneously detect multiple objects and the semantic parts within these objects in the scene. This problem is important in providing detailed object understanding, but is challenging due to the existence of both class-level and part-level ambiguities. In this paper, we propose to integrate an attention refinement module and a feature fusion module to tackle the part-level ambiguity. The attention refinement module aims to enhance the feature representations by focusing on important features. The feature fusion module aims to improve the fusion operation for different scales of features. We also propose an object-to-part training strategy to tackle the class-level ambiguity, which improves the localization of parts by exploiting prior knowledge of objects. The experimental results demonstrated the effectiveness of the proposed modules and the training strategy, and showed that our proposed method achieved state-of-the-art performance on the benchmark datasets.
Guiding Visual Question Answering with Attention Priors

The current success of modern visual reasoning systems is arguably attributed to cross-modality attention mechanisms. However, in deliberative reasoning such as in VQA, attention is unconstrained at each step, and thus may serve as a statistical pooling mechanism rather than a semantic operation intended to select information relevant to inference. This is because at training time, attention is only guided by a very sparse signal (i.e. the answer label) at the end of the inference chain. This causes the cross-modality attention weights to deviate from the desired visual-language bindings. To rectify this deviation, we propose to guide the attention mechanism using explicit linguistic-visual grounding. This grounding is derived by connecting structured linguistic concepts in the query to their referents among the visual objects. Here we learn the grounding from the pairing of questions and images alone, without the need for answer annotation or external grounding supervision. This grounding guides the attention mechanism inside VQA models through a duality of mechanisms: pre-training attention weight calculation and directly guiding the weights at inference time on a case- by-case basis. The resultant algorithm is capable of probing attention-based reasoning models, injecting relevant associative knowledge, and regulating the core reasoning process. This scalable enhancement improves the performance of VQA models, fortifies their robustness to limited access to supervised data, and increases interpretability.