pwc-archive/papers-with-abstracts huggingface dataset:
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
Authors: Xin Qiao, Chenyang Ge, Youmin Zhang, Yanhui Zhou, Fabio Tosi, Matteo Poggi, Stefano Mattoccia
Date: 2023-03-16 00:00:00
Tasks: Super-Resolution, Vocal Bursts Intensity Prediction

We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The former are extracted by an efficient transformer processing both local and global contexts, while the latter are obtained by projecting color images into the frequency domain. Both are combined together with depth features by means of a fusion strategy within a multi-stage and multi-scale framework. Experiments on the main benchmarks, such as NYUv2, Middlebury, DIML and RGBDD, show that our approach outperforms existing methods by a large margin (~20% on NYUv2 and DIML against the contemporary work DADA, with 16x upsampling), establishing a new state-of-the-art in the guided depth super-resolution task.
DIRE for Diffusion-Generated Image Detection
Authors: Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, Houqiang Li
Date: 2023-03-16 00:00:00
Proceeding: ICCV 2023 1

Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images. We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data. To address this issue, we propose a novel image representation called DIffusion Reconstruction Error (DIRE), which measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. We observe that diffusion-generated images can be approximately reconstructed by a diffusion model while real images cannot. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images. DIRE provides an effective way to detect images generated by most diffusion models, and it is general for detecting generated images from unseen diffusion models and robust to various perturbations. Furthermore, we establish a comprehensive diffusion-generated benchmark including images generated by eight diffusion models to evaluate the performance of diffusion-generated image detectors. Extensive experiments on our collected benchmark demonstrate that DIRE exhibits superiority over previous generated-image detectors. The code and dataset are available at https://github.com/ZhendongWang6/DIRE.
A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild
Authors: Dang-Khanh Nguyen, Ngoc-Huynh Ho, Sudarshan Pant, Hyung-Jeong Yang
Date: 2023-03-16 00:00:00
Tasks: Emotion Classification

In recent years, transformer architecture has been a dominating paradigm in many applications, including affective computing. In this report, we propose our transformer-based model to handle Emotion Classification Task in the 5th Affective Behavior Analysis In-the-wild Competition. By leveraging the attentive model and the synthetic dataset, we attain a score of 0.4775 on the validation set of Aff-Wild2, the dataset provided by the organizer.
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations
Authors: Lukas Struppek, Dominik Hintersdorf, Felix Friedrich, Manuel Brack, Patrick Schramowski, Kristian Kersting
Date: 2023-03-16 00:00:00
Tasks: Attribute, Face Recognition, Image Generation, Inference Attack

Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy. To investigate this privacy leakage, we introduce the first Class Attribute Inference Attack (CAIA), which leverages recent advances in text-to-image synthesis to infer sensitive attributes of individual classes in a black-box setting, while remaining competitive with related white-box attacks. Our extensive experiments in the face recognition domain show that CAIA can accurately infer undisclosed sensitive attributes, such as an individual's hair color, gender, and racial appearance, which are not part of the training labels. Interestingly, we demonstrate that adversarial robust models are even more vulnerable to such privacy leakage than standard models, indicating that a trade-off between robustness and privacy exists.
SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective
Authors: Zipeng Xu, Songlong Xing, Enver Sangineto, Nicu Sebe
Date: 2023-03-16 00:00:00
Tasks: Image Generation, Style Transfer

Owing to the power of vision-language foundation models, e.g., CLIP, the area of image synthesis has seen recent important advances. Particularly, for style transfer, CLIP enables transferring more general and abstract styles without collecting the style images in advance, as the style can be efficiently described with natural language, and the result is optimized by minimizing the CLIP similarity between the text description and the stylized image. However, directly using CLIP to guide style transfer leads to undesirable artifacts (mainly written words and unrelated visual entities) spread over the image. In this paper, we propose SpectralCLIP, which is based on a spectral representation of the CLIP embedding sequence, where most of the common artifacts occupy specific frequencies. By masking the band including these frequencies, we can condition the generation process to adhere to the target style properties (e.g., color, texture, paint stroke, etc.) while excluding the generation of larger-scale structures corresponding to the artifacts. Experimental results show that SpectralCLIP prevents the generation of artifacts effectively in quantitative and qualitative terms, without impairing the stylisation quality. We also apply SpectralCLIP to text-conditioned image generation and show that it prevents written words in the generated images. Our code is available at https://github.com/zipengxuc/SpectralCLIP.
ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets
Authors: Pablo Villacorta, Jesús M. Rodríguez-de-Vera, Marc Bolaños, Ignacio Sarasúa, Bhalaji Nagarajan, Petia Radeva
Date: 2023-03-16 00:00:00
Tasks: Fine-Grained Image Recognition, Fine-Grained Visual Recognition

Fine-Grained Visual Recognition (FGVR) tackles the problem of distinguishing highly similar categories. One of the main approaches to FGVR, namely subset learning, tries to leverage information from existing class taxonomies to improve the performance of deep neural networks. However, these methods rely on the existence of handcrafted hierarchies that are not necessarily optimal for the models. In this paper, we propose ELFIS, an expert learning framework for FGVR that clusters categories of the dataset into meta-categories using both dataset-inherent lexical and model-specific information. A set of neural networks-based experts are trained focusing on the meta-categories and are integrated into a multi-task framework. Extensive experimentation shows improvements in the SoTA FGVR benchmarks of up to +1.3% of accuracy using both CNNs and transformer-based networks. Overall, the obtained results evidence that ELFIS can be applied on top of any classification model, enabling the obtention of SoTA results. The source code will be made public soon.
StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model
Authors: Zipeng Xu, Enver Sangineto, Nicu Sebe
Date: 2023-03-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: Style Transfer

Despite the progress made in the style transfer task, most previous work focus on transferring only relatively simple features like color or texture, while missing more abstract concepts such as overall art expression or painter-specific traits. However, these abstract semantics can be captured by models like DALL-E or CLIP, which have been trained using huge datasets of images and textual documents. In this paper, we propose StylerDALLE, a style transfer method that exploits both of these models and uses natural language to describe abstract art styles. Specifically, we formulate the language-guided style transfer task as a non-autoregressive token sequence translation, i.e., from input content image to output stylized image, in the discrete latent space of a large-scale pretrained vector-quantized tokenizer, e.g., the discrete variational auto-encoder (dVAE) of DALL-E. To incorporate style information, we propose a Reinforcement Learning strategy with CLIP-based language supervision that ensures stylization and content preservation simultaneously. Experimental results demonstrate the superiority of our method, which can effectively transfer art styles using language instructions at different granularities. Code is available at https://github.com/zipengxuc/StylerDALLE.
A Survey of Deep Visual Cross-Domain Few-Shot Learning
Authors: Wenjian Wang, Lijuan Duan, Yuxi Wang, Junsong Fan, Zhi Gong, Zhaoxiang Zhang
Date: 2023-03-16 00:00:00
Tasks: Cross-Domain Few-Shot, cross-domain few-shot learning, Few-Shot Learning, Survey, Transfer Learning

Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. While it is assumed that train and test data have the same data distribution, this is often not the case in real-world applications. This leads to decreased model transfer effects when the new class distribution differs significantly from the learned classes. Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting. In this survey, we provide a detailed taxonomy of CDFS from the problem setting and corresponding solutions view. We summarise the existing CDFS network architectures and discuss the solution ideas for each direction the taxonomy indicates. Furthermore, we introduce various CDFS downstream applications and outline classification, detection, and segmentation benchmarks and corresponding standards for evaluation. We also discuss the challenges of CDFS research and explore potential directions for future investigation. Through this review, we aim to provide comprehensive guidance on CDFS research, enabling researchers to gain insight into the state-of-the-art while allowing them to build upon existing solutions to develop their own CDFS models.
GridCLIP: One-Stage Object Detection by Grid-Level CLIP Representation Learning
Authors: Jiayi Lin, Shaogang Gong
Date: 2023-03-16 00:00:00
Tasks: object-detection, Object Detection, Representation Learning

A vision-language foundation model pretrained on very large-scale image-text paired data has the potential to provide generalizable knowledge representation for downstream visual recognition and detection tasks, especially on supplementing the undersampled categories in downstream model training. Recent studies utilizing CLIP for object detection have shown that a two-stage detector design typically outperforms a one-stage detector, while requiring more expensive training resources and longer inference time. In this work, we propose a one-stage detector GridCLIP that narrows its performance gap to those of two-stage detectors, with approximately 43 and 5 times faster than its two-stage counterpart (ViLD) in the training and test process respectively. GridCLIP learns grid-level representations to adapt to the intrinsic principle of one-stage detection learning by expanding the conventional CLIP image-text holistic mapping to a more fine-grained, grid-text alignment. This differs from the region-text mapping in two-stage detectors that apply CLIP directly by treating regions as images. Specifically, GridCLIP performs Grid-level Alignment to adapt the CLIP image-level representations to grid-level representations by aligning to CLIP category representations to learn the annotated (especially frequent) categories. To learn generalizable visual representations of broader categories, especially undersampled ones, we perform Image-level Alignment during training to propagate broad pre-learned categories in the CLIP image encoder from the image-level to the grid-level representations. Experiments show that the learned CLIP-based grid-level representations boost the performance of undersampled (infrequent and novel) categories, reaching comparable detection performance on the LVIS benchmark.
Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video
Authors: Ziyang Hong, C. Patrick Yue
Date: 2023-03-16 00:00:00
Tasks: 3D Scene Reconstruction, Depth Estimation, Depth Prediction, Semantic Segmentation

We present a novel real-time capable learning method that jointly perceives a 3D scene's geometry structure and semantic labels. Recent approaches to real-time 3D scene reconstruction mostly adopt a volumetric scheme, where a Truncated Signed Distance Function (TSDF) is directly regressed. However, these volumetric approaches tend to focus on the global coherence of their reconstructions, which leads to a lack of local geometric detail. To overcome this issue, we propose to leverage the latent geometric prior knowledge in 2D image features by explicit depth prediction and anchored feature generation, to refine the occupancy learning in TSDF volume. Besides, we find that this cross-dimensional feature refinement methodology can also be adopted for the semantic segmentation task by utilizing semantic priors. Hence, we proposed an end-to-end cross-dimensional refinement neural network (CDRNet) to extract both 3D mesh and 3D semantic labeling in real time. The experiment results show that this method achieves a state-of-the-art 3D perception efficiency on multiple datasets, which indicates the great potential of our method for industrial applications.
Cross-head Supervision for Crowd Counting with Noisy Annotations
Authors: Mingliang Dai, Zhizhong Huang, Jiaqi Gao, Hongming Shan, Junping Zhang
Date: 2023-03-16 00:00:00
Tasks: Crowd Counting

Noisy annotations such as missing annotations and location shifts often exist in crowd counting datasets due to multi-scale head sizes, high occlusion, etc. These noisy annotations severely affect the model training, especially for density map-based methods. To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision. The resultant model, CHS-Net, can synergize different types of inductive biases for better counting. In addition, we develop a progressive cross-head supervision learning strategy to stabilize the training process and provide more reliable supervision. Extensive experimental results on ShanghaiTech and QNRF datasets demonstrate superior performance over state-of-the-art methods. Code is available at https://github.com/RaccoonDML/CHSNet.
Human Reaction Intensity Estimation with Ensemble of Multi-task Networks
Authors: JiYeon Oh, Daun Kim, Jae-Yeop Jeong, Yeong-Gi Hong, Jin-Woo Jeong
Date: 2023-03-16 00:00:00
Tasks: Facial Expression Recognition

Facial expression in-the-wild is essential for various interactive computing domains. Especially, "Emotional Reaction Intensity" (ERI) is an important topic in the facial expression recognition task. In this paper, we propose a multi-emotional task learning-based approach and present preliminary results for the ERI challenge introduced in the 5th affective behavior analysis in-the-wild (ABAW) competition. Our method achieved the mean PCC score of 0.3254.
SwinVFTR: A Novel Volumetric Feature-learning Transformer for 3D OCT Fluid Segmentation
Authors: Khondker Fariha Hossain, Sharif Amit Kamran, Alireza Tavakkoli, George Bebis, Sal Baker
Date: 2023-03-16 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, SSIM

Accurately segmenting fluid in 3D optical coherence tomography (OCT) images is critical for detecting eye diseases but remains challenging. Traditional autoencoder-based methods struggle with resolution loss and information recovery. While transformer-based models improve segmentation, they arent optimized for 3D OCT volumes, which vary by vendor and extraction technique. To address this, we propose SwinVFTR, a transformer architecture for precise fluid segmentation in 3D OCT images. SwinVFTR employs channel-wise volumetric sampling and a shifted window transformer block to improve fluid localization. Moreover, a novel volumetric attention block enhances spatial and depth-wise attention. Trained using multi-class dice loss, SwinVFTR outperforms existing models on Spectralis, Cirrus, and Topcon OCT datasets, achieving mean dice scores of 0.72, 0.59, and 0.68, respectively, along with superior performance in mean intersection-over-union (IOU) and structural similarity (SSIM) metrics.
Generative Adversarial Network for Personalized Art Therapy in Melanoma Disease Management
Authors: Lennart Jütte, Ning Wang, Bernhard Roth
Date: 2023-03-16 00:00:00
Tasks: Generative Adversarial Network, Management, Style Transfer

Melanoma is the most lethal type of skin cancer. Patients are vulnerable to mental health illnesses which can reduce the effectiveness of the cancer treatment and the patients adherence to drug plans. It is crucial to preserve the mental health of patients while they are receiving treatment. However, current art therapy approaches are not personal and unique to the patient. We aim to provide a well-trained image style transfer model that can quickly generate unique art from personal dermoscopic melanoma images as an additional tool for art therapy in disease management of melanoma. Visual art appreciation as a common form of art therapy in disease management that measurably reduces the degree of psychological distress. We developed a network based on the cycle-consistent generative adversarial network for style transfer that generates personalized and unique artworks from dermoscopic melanoma images. We developed a model that converts melanoma images into unique flower-themed artworks that relate to the shape of the lesion and are therefore personal to the patient. Further, we altered the initial framework and made comparisons and evaluations of the results. With this, we increased the options in the toolbox for art therapy in disease management of melanoma. The development of an easy-to-use user interface ensures the availability of the approach to stakeholders. The transformation of melanoma into flower-themed artworks is achieved by the proposed model and the graphical user interface. This contribution opens a new field of GANs in art therapy and could lead to more personalized disease management.
Towards a Smaller Student: Capacity Dynamic Distillation for Efficient Image Retrieval
Authors: Yi Xie, Huaidong Zhang, Xuemiao Xu, Jianqing Zhu, Shengfeng He
Date: 2023-03-16 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Retrieval, Knowledge Distillation, Retrieval

Previous Knowledge Distillation based efficient image retrieval methods employs a lightweight network as the student model for fast inference. However, the lightweight student model lacks adequate representation capacity for effective knowledge imitation during the most critical early training period, causing final performance degeneration. To tackle this issue, we propose a Capacity Dynamic Distillation framework, which constructs a student model with editable representation capacity. Specifically, the employed student model is initially a heavy model to fruitfully learn distilled knowledge in the early training epochs, and the student model is gradually compressed during the training. To dynamically adjust the model capacity, our dynamic framework inserts a learnable convolutional layer within each residual block in the student model as the channel importance indicator. The indicator is optimized simultaneously by the image retrieval loss and the compression loss, and a retrieval-guided gradient resetting mechanism is proposed to release the gradient conflict. Extensive experiments show that our method has superior inference speed and accuracy, e.g., on the VeRi-776 dataset, given the ResNet101 as a teacher, our method saves 67.13% model parameters and 65.67% FLOPs (around 24.13% and 21.94% higher than state-of-the-arts) without sacrificing accuracy (around 2.11% mAP higher than state-of-the-arts).
MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency
Authors: Qiao Wu, Jiaqi Yang, Kun Sun, Chu'ai Zhang, Yanning Zhang, Mathieu Salzmann
Date: 2023-03-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Single Object Tracking, Data Augmentation, Object Tracking

3D single object tracking (SOT) is an indispensable part of automated driving. Existing approaches rely heavily on large, densely labeled datasets. However, annotating point clouds is both costly and time-consuming. Inspired by the great success of cycle tracking in unsupervised 2D SOT, we introduce the first semi-supervised approach to 3D SOT. Specifically, we introduce two cycle-consistency strategies for supervision: 1) Self tracking cycles, which leverage labels to help the model converge better in the early stages of training; 2) forward-backward cycles, which strengthen the tracker's robustness to motion variations and the template noise caused by the template update strategy. Furthermore, we propose a data augmentation strategy named SOTMixup to improve the tracker's robustness to point cloud diversity. SOTMixup generates training samples by sampling points in two point clouds with a mixing rate and assigns a reasonable loss weight for training according to the mixing rate. The resulting MixCycle approach generalizes to appearance matching-based trackers. On the KITTI benchmark, based on the P2B tracker, MixCycle trained with $\textbf{10\%}$ labels outperforms P2B trained with $\textbf{100\%}$ labels, and achieves a $\textbf{28.4\%}$ precision improvement when using $\textbf{1\%}$ labels. Our code will be released at \url{https://github.com/Mumuqiao/MixCycle}.
GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision
Authors: Mingyue Zhao, Shang Zhao, Quan Quan, Li Fan, Xiaolan Qiu, Shiyuan Liu, S. Kevin Zhou
Date: 2023-03-16 00:00:00
Tasks: Segmentation

Airway segmentation, especially bronchioles segmentation, is an important but challenging task because distal bronchus are sparsely distributed and of a fine scale. Existing neural networks usually exploit sparse topology to learn the connectivity of bronchioles and inefficient shallow features to capture such high-frequency information, leading to the breakage or missed detection of individual thin branches. To address these problems, we contribute a new bronchial segmentation method based on Group Deep Dense Supervision (GDDS) that emphasizes fine-scale bronchioles segmentation in a simple-but-effective manner. First, Deep Dense Supervision (DDS) is proposed by constructing local dense topology skillfully and implementing dense topological learning on a specific shallow feature layer. GDDS further empowers the shallow features with better perception ability to detect bronchioles, even the ones that are not easily discernible to the naked eye. Extensive experiments on the BAS benchmark dataset have shown that our method promotes the network to have a high sensitivity in capturing fine-scale branches and outperforms state-of-the-art methods by a large margin (+12.8 % in BD and +8.8 % in TD) while only introducing a small number of extra parameters.