GenAI4ELab/papercli-papers huggingface dataset:
Face Clustering via Graph Convolutional Networks with Confidence Edges
Authors: Yang Wu; Zhiwei Ge; Yuhao Luo; Lin Liu; Sulong Xu
Venue: ICCV 2023 (main)

Abstract: Face clustering is a method for unlabeled image annotation and has attracted increasing attention. Existing methods have made significant breakthroughs by introducing Graph Convolutional Networks (GCNs) on the affinity graph. However, such graphs will contain many vertex pairs with inconsistent similarities and labels, thus degrading the model's performance. There are already relevant efforts for this problem, but the information about features needs to be mined further. In this paper, we define a new concept called confidence edge and guide the construction of graphs. Furthermore, a novel confidence-GCN is proposed to cluster face images by deriving more confidence edges. Firstly, Local Information Fusion is advanced to obtain a more accurate similarity metric by considering the neighbors of vertices. Then Unsupervised Neighbor Determination is used to discard low-quality edges based on similarity differences. Moreover, we elaborate that the remaining edges retain the most beneficial information to demonstrate the validity. At last, the confidence-GCN takes the graph as the input and fully uses the confidence edges to complete the clustering. Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art. At the same time, comparable results are obtained on the fashion dataset.
Learning Spatial-context-aware Global Visual Feature Representation for Instance Image Retrieval
Authors: Zhongyan Zhang; Lei Wang; Luping Zhou; Piotr Koniusz
Venue: ICCV 2023 (main)

Abstract: In instance image retrieval, considering local spatial information within an image has proven effective to boost retrieval performance, as demonstrated by local visual descriptor based geometric verification. Nevertheless, it will be highly valuable to make ordinary global image representations spatial-context-aware because global representation based image retrieval is appealing thanks to its algorithmic simplicity, low memory cost, and being friendly to sophisticated data structures. To this end, we propose a novel feature learning framework for instance image retrieval, which embeds local spatial context information into the learned global feature representations. Specifically, in parallel to the visual feature branch in a CNN backbone, we design a spatial context branch that consists of two modules called online token learning and distance encoding. For each local descriptor learned in CNN, the former module is used to indicate the types of its surrounding descriptors, while their spatial distribution information is captured by the latter module. After that, the visual feature branch and the spatial context branch are fused to produce a single global feature representation per image. As experimentally demonstrated, with the spatial-context-aware characteristic, we can well improve the performance of global representation based image retrieval while maintaining all of its appealing properties. Our code is available at https://github.com/Zy-Zhang/SpCa
Cross-modal Latent Space Alignment for Image to Avatar Translation
Authors: Manuel Ladron de Guevara; Jose Echevarria; Yijun Li; Yannick Hold-Geoffroy; Cameron Smith; Daichi Ito
Venue: ICCV 2023 (main)

Abstract: We present a novel method for automatic vectorized avatar generation from a single portrait image. Most existing approaches that create avatars rely on image-to-image translation methods, which present some limitations when applied to 3D rendering, animation, or video. Instead, we leverage modality-specific autoencoders trained on large-scale unpaired portraits and parametric avatars, and then learn a mapping between both modalities via an alignment module trained on a significantly smaller amount of data. The resulting cross-modal latent space preserves facial identity, producing more visually appealing and higher fidelity avatars than previous methods, as supported by our quantitative and qualitative evaluations. Moreover, our method's virtue of being resolution-independent makes it highly versatile and applicable in a wide range of settings.
Inspecting the Geographical Representativeness of Images from Text-to-Image Models
Authors: Abhipsa Basu; R. Venkatesh Babu; Danish Pruthi
Venue: ICCV 2023 (main)

Abstract: Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate millions of images everyday, and hold the potential to drastically impact areas such as generative art, digital marketing and data augmentation. Given their outsized impact, it is important to ensure that the generated content reflects the artifacts and surroundings across the globe, rather than over-representing certain parts of the world. In this paper, we measure the geographical representativeness of common nouns (e.g., a house) generated through DALL.E 2 and Stable Diffusion models using a crowdsourced study comprising 540 participants across 27 countries. For deliberately underspecified inputs without country names, the generated images most reflect the surroundings of the United States followed by India, and the top generations rarely reflect surroundings from all other countries (average score less than 3 out of 5). Specifying the country names in the input increases the representativeness by 1.44 points on average on a 5-point Likert scale for DALL.E 2 and 0.75 for Stable Diffusion, however, the overall scores for many countries still remain low, highlighting the need for future models to be more geographically inclusive. Lastly, we examine the feasibility of quantifying the geographical representativeness of generated images without conducting user studies.
Space-time Prompting for Video Class-incremental Learning
Authors: Yixuan Pei; Zhiwu Qing; Shiwei Zhang; Xiang Wang; Yingya Zhang; Deli Zhao; Xueming Qian
Venue: ICCV 2023 (main)

Abstract: Recently, prompt-based learning has made impressive progress on image class-incremental learning, but it still lacks sufficient exploration in the video domain. In this paper, we will fill this gap by learning multiple prompts based on a powerful image-language pre-trained model, i.e., CLIP, making it fit for video class-incremental learning (VCIL). For this purpose, we present a space-time prompting approach (ST-Prompt) which contains two kinds of prompts, i.e., task-specific prompts and task-agnostic prompts. The task-specific prompts are to address the catastrophic forgetting problem by learning multi-grained prompts, i.e., spatial prompts, temporal prompts and comprehensive prompts, for accurate task identification. The task-agnostic prompts maintain a globally-shared prompt pool, which can empower the pre-trained image models with temporal perception abilities by exchanging contexts between frames. By this means, ST-Prompt can transfer the plentiful knowledge in the image-language pre-trained models to the VCIL task with only a tiny set of prompts to be optimized. To evaluate ST-Prompt, we conduct extensive experiments on three standard benchmarks. The results show that ST-Prompt can significantly surpass the state-of-the-art VCIL methods, especially it gains 9.06% on HMDB51 dataset under the 1*25 stage setting.
Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing
Authors: Alberto Baldrati; Davide Morelli; Giuseppe Cartella; Marcella Cornia; Marco Bertini; Rita Cucchiara
Venue: ICCV 2023 (main)

Abstract: Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the human body. In this context, computer vision can thus be used to improve the fashion design process. Differently from previous works that mainly focused on the virtual try-on of garments, we propose the task of multimodal-conditioned fashion image editing, guiding the generation of human-centric fashion images by following multimodal prompts, such as text, human body poses, and garment sketches. We tackle this problem by proposing a new architecture based on latent diffusion models, an approach that has not been used before in the fashion domain. Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner. Experimental results on these new datasets demonstrate the effectiveness of our proposal, both in terms of realism and coherence with the given multimodal inputs. Source code and collected multimodal annotations are publicly available at: https://github.com/aimagelab/multimodal-garment-designer.
Time-to-Contact Map by Joint Estimation of Up-to-Scale Inverse Depth and Global Motion using a Single Event Camera
Authors: Urbano Miguel Nunes; Laurent Udo Perrinet; Sio-Hoi Ieng
Venue: ICCV 2023 (main)

Abstract: Event cameras asynchronously report brightness changes with a temporal resolution in the order of microseconds, which makes them inherently suitable to address problems that involve rapid motion perception. In this paper, we address the problem of time-to-contact (TTC) estimation using a single event camera. This problem is typically addressed by estimating a single global TTC measure, which explicitly assumes that the surface/obstacle is planar and fronto-parallel. We relax this assumption by proposing an incremental event-based method to estimate the TTC that jointly estimates the (up-to scale) inverse depth and global motion using a single event camera. The proposed method is reliable and fast while asynchronously maintaining a TTC map (TTCM), which provides per-pixel TTC estimates. As a side product, the proposed method can also estimate per-event optical flow. We achieve state-of-the-art performances on TTC estimation in terms of accuracy and runtime per event while achieving competitive performance on optical flow estimation.
Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks
Authors: Sixiang Chen; Tian Ye; Jinbin Bai; Erkang Chen; Jun Shi; Lei Zhu
Venue: ICCV 2023 (main)

Abstract: In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image. Note that the rain streaks and raindrops have diverse shapes, sizes, and locations in the captured image, and thus modeling the correlation relationship between irregular degradations caused by rain artifacts is a necessary prerequisite for image deraining. This paper aims to present an efficient and flexible mechanism to learn and model degradation relationships in a global view, thereby achieving a unified removal of intricate rain scenes. To do so, we propose a Sparse Sampling Transformer based on Uncertainty-Driven Ranking, dubbed UDR-S2Former. Compared to previous methods, our UDR-S2Former has three merits. First, it can adaptively sample relevant image degradation information to model underlying degradation relationships. Second, explicit application of the uncertainty-driven ranking strategy can facilitate the network to attend to degradation features and understand the reconstruction process. Finally, experimental results show that our UDR-S2Former clearly outperforms state-of-the-art methods for all benchmarks.
A Benchmark for Chinese-English Scene Text Image Super-Resolution
Authors: Jianqi Ma; Zhetong Liang; Wangmeng Xiang; Xi Yang; Lei Zhang
Venue: ICCV 2023 (main)

Abstract: Scene Text Image Super-resolution (STISR) aims to recover high-resolution (HR) scene text images with visually pleasant and readable text content from the given low-resolution (LR) input. Most existing works focus on recovering English texts, which have simple structures in the characters, while little work has been done on the more challenging Chinese texts with diverse and complex character structures. In this paper, we propose a real-world Chinese-English benchmark dataset, namely Real-CE, for the task of STISR with the emphasis on restoring structurally complex Chinese characters. The benchmark provides 1,935/783 real-world LR-HR text image pairs (contains 33,789 text lines in total) for training/testing in 2x and 4x zooming modes, complemented by detailed annotations, including detection boxes and text transcripts. Moreover, we design an edge-aware learning method, which provides structural supervision in image and feature domain, to effectively reconstruct the dense structures of Chinese characters. We conduct experiments on the proposed Real-CE benchmark and evaluate the existing STISR models with and without our edge-aware loss. The benchmark, including data and source code, will be made publicly available.
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models
Authors: Chanyue Wu; Dong Wang; Yunpeng Bai; Hanyu Mao; Ying Li; Qiang Shen
Venue: ICCV 2023 (main)

Abstract: Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is initialized with pure Gaussian noise and iteratively refined. At each iteration, the noise is removed with a Conditional Denoising Transformer (CDFormer) that is trained on denoising at different noise levels, conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images. Systematic experiments have been conducted on four public datasets, demonstrating that HSR-Diff outperforms state-of-the-art methods.
Replay: Multi-modal Multi-view Acted Videos for Casual Holography
Authors: Roman Shapovalov; Yanir Kleiman; Ignacio Rocco; David Novotny; Andrea Vedaldi; Changan Chen; Filippos Kokkinos; Ben Graham; Natalia Neverova
Venue: ICCV 2023 (main)

Abstract: We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially. Each scene is filmed in high production quality, from different viewpoints with several static cameras, as well as wearable action cameras, and recorded with a large array of microphones at different positions in the room. Overall, the dataset contains over 3000 minutes of footage and over 5 million timestamped high-resolution frames annotated with camera poses and partially with foreground masks. The Replay dataset has many potential applications, such as novel-view synthesis, 3D reconstruction, novel-view acoustic synthesis, human body and face analysis, and training generative models. We provide a benchmark for training and evaluating novel-view synthesis, with two scenarios of different difficulty. Finally, we evaluate several baseline state-of-the-art methods on the new benchmark.
Advancing Example Exploitation Can Alleviate Critical Challenges in Adversarial Training
Authors: Yao Ge; Yun Li; Keji Han; Junyi Zhu; Xianzhong Long
Venue: ICCV 2023 (main)

Abstract: Deep neural networks have achieved remarkable results across various tasks. However, they are susceptible to adversarial examples, which are generated by adding adversarial perturbations to original data. Adversarial training (AT) is the most effective defense mechanism against adversarial examples and has received significant attention. Recent studies highlight the importance of example exploitation, where the model's learning intensity is altered for specific examples to extend classic AT approaches. However, the analysis methodologies employed by these studies are varied and contradictory, which may lead to confusion in future research. To address this issue, we provide a comprehensive summary of representative strategies focusing on exploiting examples within a unified framework. Furthermore, we investigate the role of examples in AT and find that examples which contribute primarily to accuracy or robustness are distinct. Based on this finding, we propose a novel example-exploitation idea that can further improve the performance of advanced AT methods. This new idea suggests that critical challenges in AT, such as the accuracy-robustness trade-off, robust overfitting, and catastrophic overfitting, can be alleviated simultaneously from an example-exploitation perspective. The code can be found in https://github.com/geyao1995/advancing-example-exploitation-in-adversarial-training.
Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection
Authors: Junjia Huang; Haofeng Li; Xiang Wan; Guanbin Li
Venue: ICCV 2023 (main)

Abstract: Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of histopathology. It is critical to efficiently locate and identify cells with diverse morphology and distributions in digital pathological images. Most existing methods take complex intermediate representations as learning targets and rely on inflexible post-refinements while paying less attention to various cell density and fields of view. In this paper, we propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions and is trained collaboratively through two sub-networks, a global and a local network. The local branch learns to infer distorted input images of smaller scales while the global network outputs the large-scale predictions as extra supervision signals. We further introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. The AAT module works by learning to capture the transformed image regions that are more valuable for training the model. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
Removing Anomalies as Noises for Industrial Defect Localization
Authors: Fanbin Lu; Xufeng Yao; Chi-Wing Fu; Jiaya Jia
Venue: ICCV 2023 (main)

Abstract: Unsupervised anomaly detection aims to train models with only anomaly-free images to detect and localize unseen anomalies. Previous reconstruction-based methods have been limited by inaccurate reconstruction results. This work presents a denoising model to detect and localize the anomalies with a generative diffusion model. In particular, we introduce random noise to overwhelm the anomalous pixels and obtain pixel-wise precise anomaly scores from the intermediate denoising process. We find that the KL divergence of the diffusion model serves as a better anomaly score compared with the traditional RGB space score. Furthermore, we reconstruct the features from a pre-trained deep feature extractor as our feature level score to improve localization performance. Moreover, we propose a gradient denoising process to smoothly transform an anomalous image into a normal one. Our denoising model outperforms the state-of-the-art reconstruction-based anomaly detection methods for precise anomaly localization and high-quality normal image reconstruction on the MVTec-AD benchmark.
GPGait: Generalized Pose-based Gait Recognition
Authors: Yang Fu; Shibei Meng; Saihui Hou; Xuecai Hu; Yongzhen Huang
Venue: ICCV 2023 (main)

Abstract: Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a Generalized Pose-based Gait recognition (GPGait) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. Code is available at https://github.com/BNU-IVC/FastPoseGait.
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations
Authors: Yuewei Yang; Hai Li; Yiran Chen
Venue: ICCV 2023 (main)

Abstract: In recent years, discriminative self-supervised methods have made significant strides in advancing various visual tasks. The central idea of learning a data encoder that is robust to data distortions/augmentations is straightforward yet highly effective. Although many studies have demonstrated the empirical success of various learning methods, the resulting learned representations can exhibit instability and hinder downstream performance. In this study, we analyze discriminative self-supervised methods from a causal perspective to explain these unstable behaviors and propose solutions to overcome them. Our approach draws inspiration from prior works that empirically demonstrate the ability of discriminative self-supervised methods to demix ground truth causal sources to some extent. Unlike previous work on causality-empowered representation learning, we do not apply our solutions during the training process but rather during the inference process to improve time efficiency. Through experiments on both controlled image datasets and realistic image datasets, we show that our proposed solutions, which involve tempering a linear transformation with controlled synthetic data, are effective in addressing these issues.