GenAI4ELab/papercli-papers huggingface dataset:
iVS-Net: Learning Human View Synthesis from Internet Videos
Authors: Junting Dong; Qi Fang; Tianshuo Yang; Qing Shuai; Chengyu Qiao; Sida Peng
Venue: ICCV 2023 (main)

Abstract: Recent advances in implicit neural representations make it possible to generate free-viewpoint videos of the human from sparse view images. To avoid the expensive training for each person, previous methods adopt the generalizable human model and demonstrate impressive results. However, these methods usually rely on limited multi-view images typically collected in the studio or commercial high-quality 3D scans for training, which heavily prohibits their generalization capability for in-the-wild images. To solve this problem, we propose a new approach to learn a generalizable human model from a new source of data, i.e., Internet videos. These videos capture various human appearances and poses and record the performers from abundant viewpoints. To exploit these videos, we present a temporal self-supervised pipeline to enforce the local appearance consistency of each body part over different frames of the same video. Once learned, the human model enables creating photorealistic free-viewpoint videos from a single input image. Experiments show that our method can generate high-quality view synthesis on in-the-wild images while only training on monocular videos.
Story Visualization by Online Text Augmentation with Context Memory
Authors: Daechul Ahn; Daneul Kim; Gwangmo Song; Seung Hwan Kim; Honglak Lee; Dongyeop Kang; Jonghyun Choi
Venue: ICCV 2023 (main)

Abstract: Story visualization (SV) is a challenging text-to-image generation task for the difficulty of not only rendering visual details from the text descriptions but also encoding a longterm context across multiple sentences. While prior efforts mostly focus on generating a semantically relevant image for each sentence, encoding a context spread across the given paragraph to generate contextually convincing images (e.g., with a correct character or with a proper background of the scene) remains a challenge. To this end, we propose a novel memory architecture for the Bi-directional Transformer framework with an online text augmentation that generates multiple pseudo-descriptions as supplementary supervision during training for better generalization to the language variation at inference. In extensive experiments on the two popular SV benchmarks, i.e., the Pororo-SV and Flintstones-SV, the proposed method significantly outperforms the state of the arts in various metrics including FID, character F1, frame accuracy, BLEU-2/3, and R-precision with similar or less computational complexity.
Attention Discriminant Sampling for Point Clouds
Authors: Cheng-Yao Hong; Yu-Ying Chou; Tyng-Luh Liu
Venue: ICCV 2023 (main)

Abstract: This paper describes an attention-driven approach to 3-D point cloud sampling. We establish our method based on a structure-aware attention discriminant analysis that explores geometric and semantic relations embodied among points and their clusters. The proposed attention discriminant sampling (ADS) starts by efficiently decomposing a given point cloud into clusters to implicitly encode its structural and geometric relatedness among points. By treating each cluster as a structural component, ADS then draws on evaluating two levels of self-attention: within-cluster and between-cluster. The former reflects the semantic complexity entailed by the learned features of points within each cluster, while the latter reveals the semantic similarity between clusters. Driven by structurally preserving the point distribution, these two aspects of self-attention help avoid sampling redundancy and decide the number of sampled points in each cluster. Extensive experiments demonstrate that ADS significantly improves classification performance to 95.1% on ModelNet40 and 87.5% on ScanObjectNN and achieves 86.9% mIoU on ShapeNet Part Segmentation. For scene segmentation, ADS yields 91.1% accuracy on S3DIS with higher mIoU to the state-of-the-art and 75.6% mIoU on ScanNetV2. Furthermore, ADS surpasses the state-of-the-art with 55.0% mAP50 on ScanNetV2 object detection.
Global Balanced Experts for Federated Long-Tailed Learning
Authors: Yaopei Zeng; Lei Liu; Li Liu; Li Shen; Shaoguo Liu; Baoyuan Wu
Venue: ICCV 2023 (main)

Abstract: Federated learning (FL) is a prevalent distributed machine learning approach that enables collaborative training of a global model across multiple devices without sharing local data. However, the presence of long-tailed data can negatively deteriorate the model's performance in real-world FL applications. Moreover, existing re-balance strategies are less effective for the federated long-tailed issue when directly utilizing local label distribution as the class prior at the clients' side. To this end, we propose a novel Global Balanced Multi-Expert (GBME) framework to optimize a balanced global objective, which does not require additional information beyond the standard FL pipeline. In particular, a proxy is derived from the accumulated gradients uploaded by the clients after local training, and is shared by all clients as the class prior for re-balance training. Such a proxy can also guide the client grouping to train a multi-expert model, where the knowledge from different clients can be aggregated via the ensemble of different experts corresponding to different client groups. To further strengthen the privacy-preserving ability, we present a GBME-p algorithm with a theoretical guarantee to prevent privacy leakage from the proxy. Extensive experiments on long-tailed decentralized datasets demonstrate the effectiveness of GBME and GBME-p, both of which show superior performance to state-of-the-art methods.
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction
Authors: Imanol G. Estepa; Ignacio Sarasua; Bhalaji Nagarajan; Petia Radeva
Venue: ICCV 2023 (main)

Abstract: Nearest neighbour based methods have proved to be one of the most successful self-supervised learning (SSL) approaches due to their high generalization capabilities. However, their computational efficiency decreases when more than one neighbour is used. In this paper, we propose a novel contrastive SSL approach, which we call All4One, that reduces the distance between neighbour representations using "centroids" created through a self-attention mechanism. We use a Centroid Contrasting objective along with single Neighbour Contrasting and Feature Contrasting objectives. Centroids help in learning contextual information from multiple neighbours whereas the neighbour contrast enables learning representations directly from the neighbours and the feature contrast allows learning representations unique to the features.This combination enables All4One to outperform popular instance discrimination approaches by more than 1% on linear classification evaluation for popular benchmark datasets and obtains state-of-the-art (SoTA) results. Finally, we show that All4One is robust towards embedding dimensionalities and augmentations, surpassing NNCLR and Barlow Twins by more than 5% on low dimensionality and weak augmentation settings.
Contrastive Pseudo Learning for Open-World DeepFake Attribution
Authors: Zhimin Sun; Shen Chen; Taiping Yao; Bangjie Yin; Ran Yi; Shouhong Ding; Lizhuang Ma
Venue: ICCV 2023 (main)

Abstract: The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks related to identity swapping or expression transferring are still overlooked. And the forgery traces hidden in unknown attacks from the open-world unlabeled faces still remain under-explored. To push the related frontier research, we introduce a new benchmark called Open-World DeepFake Attribution (OW-DFA), which aims to evaluate attribution performance against various types of fake faces under open-world scenarios. Meanwhile, we propose a novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task through 1) introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, 2) designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set. In addition, we extend the CPL framework with a multi-stage paradigm that leverages pre-train technique and iterative learning to further enhance traceability performance. Extensive experiments verify the superiority of our proposed method on the OW-DFA and also demonstrate the interpretability of deepfake attribution task and its impact on improving the security of deepfake detection area.
ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction
Authors: Jiabang He; Lei Wang; Yi Hu; Ning Liu; Hui Liu; Xing Xu; Heng Tao Shen
Venue: ICCV 2023 (main)

Abstract: Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been conducted to assess the ability of LLMs to perform document information extraction (DIE) using in-context learning. Applying LLMs to DIE poses two challenges: the modality and task gap. To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations for benefiting all test instances. We design demonstrations describing relationships that enable LLMs to understand positional relationships. We introduce formatting demonstrations for easy answer extraction. Additionally, the framework improves diverse demonstrations by updating them iteratively. Our experiments on three widely used benchmark datasets demonstrate that the ICL-D3IE framework enables GPT-3/ChatGPT to achieve superior performance when compared to previous pre-trained methods fine-tuned with full training in both the in-distribution (ID) setting and in the out-of-distribution (OOD) setting. Code is available at https://anonymous.4open.science/r/ICL-D3IE-B1EE.
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow Estimation
Authors: Yun Wang; Cheng Chi; Min Lin; Xin Yang
Venue: ICCV 2023 (main)

Abstract: Scene flow estimation, which predicts the 3D displacements of point clouds, is a fundamental task in autonomous driving. Most methods have adopted a coarse-to-fine structure to balance computational efficiency with accuracy, particularly when handling large displacements. However, inaccuracies in the initial coarse layer's scene flow estimates may accumulate, leading to incorrect final estimates. To alleviate this, we introduce a novel Iterative Hierarchical Network----IHNet. This approach circulates high-resolution estimated information (scene flow and feature) from the preceding iteration back to the low-resolution layer of the current iteration. Serving as a guide, the high-resolution estimated scene flow, instead of initializing the scene flow from zero, provides a more precise center for low-resolution layer to identify matches. Meanwhile, the decoder's feature at the high-resolution layer can contribute essential movement information. Furthermore, based on the recurrent structure, we design a resampling scheme to enhance the correspondence between points across two consecutive frames. By employing the previous estimated scene flow to fine-tune the target frame's coordinates, we can significantly reduce the correspondence discrepancy between two frame points, a problem often caused by point sparsity. Following this adjustment, we continue to estimate the scene flow using the newly updated coordinates, along with the reencoded feature. Our approach outperforms the recent state-of-the-art method WSAFlowNet by 20.1% on FlyingThings3D and 56.0% on KITTI scene flow datasets according to EPE3D metric. The code is available at https://github.com/wangyunlhr/IHNet.
SimNP: Learning Self-Similarity Priors Between Neural Points
Authors: Christopher Wewer; Eddy Ilg; Bernt Schiele; Jan Eric Lenssen
Venue: ICCV 2023 (main)

Abstract: Existing neural field representations for 3D object reconstruction either (1) utilize object-level representations, but suffer from low-quality details due to conditioning on a global latent code, or (2) are able to perfectly reconstruct the observations, but fail to utilize object-level prior knowledge to infer unobserved regions. We present SimNP, a method to learn category-level self-similarities, which combines the advantages of both worlds by connecting neural point radiance fields with a category-level self-similarity representation. Our contribution is two-fold. (1) We design the first neural point representation on a category level by utilizing the concept of coherent point clouds. The resulting neural point radiance fields store a high level of detail for locally supported object regions. (2) We learn how information is shared between neural points in an unconstrained and unsupervised fashion, which allows to derive unobserved regions of an object during the reconstruction process from given observations. We show that SimNP is able to outperform previous methods in reconstructing symmetric unseen object regions, surpassing methods that build upon category-level or pixel-aligned radiance fields, while providing semantic correspondences between instances.
Beyond the Limitation of Monocular 3D Detector via Knowledge Distillation
Authors: Yiran Yang; Dongshuo Yin; Xuee Rong; Xian Sun; Wenhui Diao; Xinming Li
Venue: ICCV 2023 (main)

Abstract: Knowledge distillation (KD) is a promising approach that facilitates the compact student model to learn dark knowledge from the huge teacher model for better results. Although KD methods are well explored in the 2D detection task, existing approaches are not suitable for 3D monocular detection without considering spatial cues. Motivated by the potential of depth information, we propose a novel distillation framework that validly improves the performance of the student model without extra depth labels. Specifically, we first put forward a perspective-induced feature imitation, which utilizes the perspective principle (the farther the smaller) to facilitate the student to imitate more features of farther objects from the teacher model. Moreover, we construct a depth-guided matrix by the predicted depth gap of teacher and student to facilitate the model to learn more knowledge of farther objects in prediction level distillation. The proposed method is available for advanced monocular detectors with various backbones, which also brings no extra inference time. Extensive experiments on the KITTI and nuScenes benchmarks with diverse settings demonstrate that the proposed method outperforms the state-of-the-art KD methods.
Cascade-DETR: Delving into High-Quality Universal Object Detection
Authors: Mingqiao Ye; Lei Ke; Siyuan Li; Yu-Wing Tai; Chi-Keung Tang; Martin Danelljan; Fisher Yu
Venue: ICCV 2023 (main)

Abstract: Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not competitive in diverse domains. Moreover, these methods still struggle to very accurately estimate the object bounding boxes in complex environments. 
 
 We introduce Cascade-DETR for high-quality universal object detection. We jointly tackle the generalization to diverse domains and localization accuracy by proposing the Cascade Attention layer, which explicitly integrates object-centric information into the detection decoder by limiting the attention to the previous box prediction. To further enhance accuracy, we also revisit the scoring of queries. Instead of relying on classification scores, we predict the expected IoU of the query, leading to substantially more well-calibrated confidences. Lastly, we introduce a universal object detection benchmark, UDB10, that contains 10 datasets from diverse domains. While also advancing the state-of-the-art on COCO, Cascade-DETR substantially improves DETR-based detectors on all datasets in UDB10, even by over 10 mAP in some cases. The improvements under stringent quality requirements are even more pronounced. Our code and pretrained models are at https://github.com/SysCV/cascade-detr.
ACLS: Adaptive and Conditional Label Smoothing for Network Calibration
Authors: Hyekang Park; Jongyoun Noh; Youngmin Oh; Donghyeon Baek; Bumsub Ham
Venue: ICCV 2023 (main)

Abstract: We address the problem of network calibration adjusting miscalibrated confidences of deep neural networks. Many approaches to network calibration adopt a regularization-based method that exploits a regularization term to smooth the miscalibrated confidences. Although these approaches have shown the effectiveness on calibrating the networks, there is still a lack of understanding on the underlying principles of regularization in terms of network calibration. We present in this paper an in-depth analysis of existing regularization-based methods, providing a better understanding on how they affect to network calibration. Specifically, we have observed that 1) the regularization-based methods can be interpreted as variants of label smoothing, and 2) they do not always behave desirably. Based on the analysis, we introduce a novel loss function, dubbed ACLS, that unifies the merits of existing regularization methods, while avoiding the limitations. We show extensive experimental results for image classification and semantic segmentation on standard benchmarks, including CIFAR10, Tiny-ImageNet, ImageNet, and PASCAL VOC, demonstrating the effectiveness of our loss function.
EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity
Authors: Zijie Jiang; Masatoshi Okutomi
Venue: ICCV 2023 (main)

Abstract: Self-supervised monocular scene flow estimation, aiming to understand both 3D structures and 3D motions from two temporally consecutive monocular images, has received increasing attention for its simple and economical sensor setup. However, the accuracy of current methods suffers from the bottleneck of less-efficient network architecture and lack of motion rigidity for regularization. In this paper, we propose a superior model named EMR-MSF by borrowing the advantages of network architecture design under the scope of supervised learning. We further impose explicit and robust geometric constraints with an elaborately constructed ego-motion aggregation module where a rigidity soft mask is proposed to filter out dynamic regions for stable ego-motion estimation using static regions. Moreover, we propose a motion consistency loss along with a mask regularization loss to fully exploit static regions. Several efficient training strategies are integrated including a gradient detachment technique and an enhanced view synthesis process for better performance. Our proposed method outperforms the previous self-supervised works by a large margin and catches up to the performance of supervised methods. On the KITTI scene flow benchmark, our approach improves the SF-all metric of the state-of-the-art self-supervised monocular method by 44% and demonstrates superior performance across sub-tasks including depth and visual odometry, amongst other self-supervised single-task or multi-task methods.
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks
Authors: Qihan Huang; Mengqi Xue; Wenqi Huang; Haofei Zhang; Jie Song; Yongcheng Jing; Mingli Song
Venue: ICCV 2023 (main)

Abstract: Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. However, recent works find that the interpretability from prototypes is fragile, due to the semantic gap between the similarities in the feature space and that in the input space. In this work, we strive to address this challenge by making the first attempt to quantitatively and objectively evaluate the interpretability of the part-prototype networks. Specifically, we propose two evaluation metrics, termed as "consistency score" and "stability score", to evaluate the explanation consistency across images and the explanation robustness against perturbations, respectively, both of which are essential for explanations taken into practice. Furthermore, we propose an elaborated part-prototype network with a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and provide substantial discussions to uncover the interpretability of existing part-prototype networks. Experiments on three benchmarks across nine architectures demonstrate that our model achieves significantly superior performance to the state of the art, in both the accuracy and interpretability. Our code is available at https://github.com/hqhQAQ/EvalProtoPNet.
Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold: Learning with Event-Driven Backpropagation
Authors: Wenjie Wei; Malu Zhang; Hong Qu; Ammar Belatreche; Jian Zhang; Hong Chen
Venue: ICCV 2023 (main)

Abstract: Spiking Neural Networks (SNNs) offer a highly promising computing paradigm due to their biological plausibility, exceptional spatiotemporal information processing capability and low power consumption. As a temporal encoding scheme for SNNs, Time-To-First-Spike (TTFS) encodes information using the timing of a single spike, which allows spiking neurons to transmit information through sparse spike trains and results in lower power consumption and higher computational efficiency compared to traditional rate-based encoding counterparts. However, despite the advantages of the TTFS encoding scheme, the effective and efficient training of TTFS-based deep SNNs remains a significant and open research problem. In this work, we first examine the factors underlying the limitations of applying existing TTFS-based learning algorithms to deep SNNs. Specifically, we investigate issues related to over-sparsity of spikes and the complexity of finding the `causal set'. We then propose a simple yet efficient dynamic firing threshold (DFT) mechanism for spiking neurons to address these issues. Building upon the proposed DFT mechanism, we further introduce a novel direct training algorithm for TTFS-based deep SNNs, called DTA-TTFS. This method utilizes event-driven processing and spike timing to enable efficient learning of deep SNNs. The proposed training method was validated on the image classification task and experimental results clearly demonstrate that our proposed method achieves state-of-the-art accuracy in comparison to existing TTFS-based learning algorithms, while maintaining high levels of sparsity and energy efficiency on neuromorphic inference accelerator.