GenAI4ELab/papercli-papers huggingface dataset:
Audio-Enhanced Text-to-Video Retrieval using Text-Conditioned Feature Alignment
Authors: Sarah Ibrahimi; Xiaohang Sun; Pichao Wang; Amanmeet Garg; Ashutosh Sanan; Mohamed Omar
Venue: ICCV 2023 (main)

Abstract: Text-to-video retrieval systems have recently made significant progress by utilizing pre-trained models trained on large-scale image-text pairs. However, most of the latest methods primarily focus on the video modality while disregarding the audio signal for this task. Nevertheless, a recent advancement by EclipSE has improved long-range text-to-video retrieval by developing an audiovisual video representation. Nonetheless, the objective of the text-to-video retrieval task is to capture the complementary audio and video information that is pertinent to the text query rather than simply achieving better audio and video alignment. To address this issue, we introduce TEFAL, a TExt-conditioned Feature ALignment method that produces both audio and video representations conditioned on the text query. Instead of using only an audiovisual attention block, which could suppress the audio information relevant to the text query, our approach employs two independent cross-modal attention blocks that enable the text to attend to the audio and video representations separately. Our proposed method's efficacy is demonstrated on four benchmark datasets that include audio: MSR-VTT, LSMDC, VATEX, and Charades, and achieves better than state-of-the-art performance consistently across the four datasets. This is attributed to the additional text-query-conditioned audio representation and the complementary information it adds to the text-query-conditioned video representation.
Open-vocabulary Object Segmentation with Diffusion Models
Authors: Ziyi Li; Qinye Zhou; Xiaoyun Zhang; Ya Zhang; Yanfeng Wang; Weidi Xie
Venue: ICCV 2023 (main)

Abstract: The goal of this paper is to extract the visual-language correspondence from a pre-trained text-to-image diffusion model, in the form of segmentation map, i.e., simultaneously generating images and segmentation masks for the corresponding visual entities described in the text prompt. We make the following contributions: (i) we pair the existing Stable Diffusion model with a novel grounding module, that can be trained to align the visual and textual embedding space of the diffusion model with only a small number of object categories; (ii) we establish an automatic pipeline for constructing a dataset, that consists of  image, segmentation mask, text prompt  triplets, to train the proposed grounding module; (iii) we evaluate the performance of open-vocabulary grounding on images generated from the text-to-image diffusion model and show that the module can well segment the objects of categories beyond seen ones at training time; (iv) we adopt the augmented diffusion model to build a synthetic semantic segmentation dataset, and show that, training a standard segmentation model on such dataset demonstrates competitive performance on the zero-shot segmentation (ZS3) benchmark, which opens up new opportunities for adopting the powerful diffusion model for discriminative tasks.
Human-centric Scene Understanding for 3D Large-scale Scenarios
Authors: Yiteng Xu; Peishan Cong; Yichen Yao; Runnan Chen; Yuenan Hou; Xinge Zhu; Xuming He; Jingyi Yu; Yuexin Ma
Venue: ICCV 2023 (main)

Abstract: Human-centric scene understanding is significant for real-world applications, but it is extremely challenging due to the existence of diverse human poses and actions, complex human-environment interactions, severe occlusions in crowds, etc. In this paper, we present a large-scale multi-modal dataset for human-centric scene understanding, dubbed HuCenLife, which is collected in diverse daily-life scenarios with rich and fine-grained annotations. Our HuCenLife can benefit many 3D perception tasks, such as segmentation, detection, action recognition, etc., and we also provide benchmarks for these tasks to facilitate related research. In addition, we design novel modules for LiDAR-based segmentation and action recognition, which are more applicable for large-scale human-centric scenarios and achieve state-of-the-art performance. The dataset and code can be found at https://github.com/4DVLab/HuCenLife.git.
With a Little Help from Your Own Past: Prototypical Memory Networks for Image Captioning
Authors: Manuele Barraco; Sara Sarto; Marcella Cornia; Lorenzo Baraldi; Rita Cucchiara
Venue: ICCV 2023 (main)

Abstract: Image captioning, like many tasks involving vision and language, currently relies on Transformer-based architectures for extracting the semantics in an image and translating it into linguistically coherent descriptions. Although successful, the attention operator only considers a weighted summation of projections of the current input sample, therefore ignoring the relevant semantic information which can come from the joint observation of other samples. In this paper, we devise a network which can perform attention over activations obtained while processing other training samples, through a prototypical memory model. Our memory models the distribution of past keys and values through the definition of prototype vectors which are both discriminative and compact. Experimentally, we assess the performance of the proposed model on the COCO dataset, in comparison with carefully designed baselines and state-of-the-art approaches, and by investigating the role of each of the proposed components. We demonstrate that our proposal can increase the performance of an encoder-decoder Transformer by 3.7 CIDEr points both when training in cross-entropy only and when fine-tuning with self-critical sequence training. Source code and trained models are available at: https://github.com/aimagelab/PMA-Net.
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Authors: Mingkai Zheng; Shan You; Lang Huang; Chen Luo; Fei Wang; Chen Qian; Chang Xu
Venue: ICCV 2023 (main)

Abstract: Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2, which formulates various consistency regularizations between labeled and unlabeled data from the graph perspective. In SimMatchV2, we regard the augmented view of a sample as a node, which consists of a label and its corresponding representation. Different nodes are connected with the edges, which are measured by the similarity of the node representations. Inspired by the message passing and node classification in graph theory, we propose four types of consistencies, namely 1) node-node consistency, 2) node-edge consistency, 3) edge-edge consistency, and 4) edge-node consistency. We also uncover that a simple feature normalization can reduce the gaps of the feature norm between different augmented views, significantly improving the performance of SimMatchV2. Our SimMatchV2 has been validated on multiple semi-supervised learning benchmarks. Notably, with ResNet-50 as our backbone and 300 epochs of training, SimMatchV2 achieves 71.9% and 76.2% Top-1 Accuracy with 1% and 10% labeled examples on ImageNet, which significantly outperforms the previous methods and achieves state-of-the-art performance.
Reinforced Disentanglement for Face Swapping without Skip Connection
Authors: Xiaohang Ren; Xingyu Chen; Pengfei Yao; Heung-Yeung Shum; Baoyuan Wang
Venue: ICCV 2023 (main)

Abstract: The SOTA face swap models still suffer the problem of either target identity (i.e., shape) being leaked or the target non-identity attributes (i.e., background, hair) failing to be fully preserved in the final results. We show that
 this insufficient disentanglement is caused by two flawed designs that were commonly adopted in prior models: (1) counting on only one compressed encoder to represent both the semantic-level non-identity facial attributes(i.e., pose) and the pixel-level non-facial region details, which is contradictory to satisfy at the same time; (2) highly relying on long skip-connections between the encoder and the final generator, leaking a certain amount of target face identity into the result. To fix them, we introduce a new face swap framework called "WSC-swap" that gets rid of skip connections and uses two target encoders to respectively
 capture the pixel-level non-facial region attributes and the semantic non-identity attributes in the face region. To further reinforce the disentanglement learning for the target encoder, we employ both identity removal loss via adversarial training (i.e., GAN) and the non-identity preservation loss via prior 3DMM models like. Extensive experiments on both FaceForensics++ and CelebA-HQ show that our results significantly outperform previous works on a rich set of metrics, including one novel metric for measuring identity consistency that was completely neglected before.
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Authors: Robert van der Klis; Stephan Alaniz; Massimiliano Mancini; Cassio F. Dantas; Dino Ienco; Zeynep Akata; Diego Marcos
Venue: ICCV 2023 (main)

Abstract: Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds. Encouraging a fine-grained classification model to first detect such parts and then using them to infer the class could help us gauge whether the model is indeed looking at the right details better than with interpretability methods that provide a single attribution map.
 We propose PDiscoNet to discover object parts by using only image-level class labels along with priors encouraging the parts to be: discriminative, compact, distinct from each other, equivariant to rigid transforms, and active in at least some of the images. In addition to using the appropriate losses to encode these priors, we propose to use part-dropout, where full part feature vectors are dropped at once to prevent a single part from dominating in the classification, and part feature vector modulation, which makes the information coming from each part distinct from the perspective of the classifier.
 Our results on CUB, CelebA, and PartImageNet show that the proposed method provides substantially better part discovery performance than previous methods while not requiring any additional hyper-parameter tuning and without penalizing the classification performance.
Privacy-Preserving Face Recognition Using Random Frequency Components
Authors: Yuxi Mi; Yuge Huang; Jiazhen Ji; Minyi Zhao; Jiaxiang Wu; Xingkun Xu; Shouhong Ding; Shuigeng Zhou
Venue: ICCV 2023 (main)

Abstract: The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.
Vision Transformer Adapters for Generalizable Multitask Learning
Authors: Deblina Bhattacharjee; Sabine Süsstrunk; Mathieu Salzmann
Venue: ICCV 2023 (main)

Abstract: We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains. Integrated into an off-the-shelf vision transformer backbone, our adapters can simultaneously solve multiple dense vision tasks in a parameter-efficient manner, unlike existing multitasking transformers that are parametrically expensive. In contrast to concurrent methods, we do not require retraining or fine-tuning whenever a new task or domain is added. We introduce a task-adapted attention mechanism within our adapter framework that combines gradient-based task similarities with attention-based ones. The learned task affinities generalize to the following settings: zero-shot task transfer, unsupervised domain adaptation, and generalization without fine-tuning to novel domains. We demonstrate that our approach outperforms not only the existing convolutional neural network-based multitasking methods but also the vision transformer-based ones. Our project page is at https://ivrl.github.io/VTAGML.
How to Choose your Best Allies for a Transferable Attack?
Authors: Thibault Maho; Seyed-Mohsen Moosavi-Dezfooli; Teddy Furon
Venue: ICCV 2023 (main)

Abstract: The transferability of adversarial examples is a key issue in the security of deep neural networks. The possibility of an adversarial example crafted for a source model fooling another targeted model makes the threat of adversarial attacks more realistic. Measuring transferability is a crucial problem, but the Attack Success Rate alone does not provide a sound evaluation. This paper proposes a new methodology for evaluating transferability by putting distortion in a central position. This new tool shows that transferable attacks may perform far worse than a black box attack if the attacker randomly picks the source model. To address this issue, we propose a new selection mechanism, called FiT, which aims at choosing the best source model with only a few preliminary queries to the target. Our experimental results show that FiT is highly effective at selecting the best source model for multiple scenarios such as single-model attacks, ensemble-model attacks and multiple attacks.
CVRecon: Rethinking 3D Geometric Feature Learning For Neural Reconstruction
Authors: Ziyue Feng; Liang Yang; Pengsheng Guo; Bing Li
Venue: ICCV 2023 (main)

Abstract: Recent advances in neural reconstruction using posed image sequences have made remarkable progress. However, due to the lack of depth information, existing volumetric-based techniques simply duplicate 2D image features of the object surface along the entire camera ray. We contend this duplication introduces noise in empty and occluded spaces, posing challenges for producing high-quality 3D geometry. Drawing inspiration from traditional multi-view stereo methods, we propose an end-to-end 3D neural reconstruction framework CVRecon, designed to exploit the rich geometric embedding in the cost volumes to facilitate 3D geometric feature learning. Furthermore, we present Ray-contextual Compensated Cost Volume (RCCV), a novel 3D geometric feature representation that encodes view-dependent information with improved integrity and robustness. Through comprehensive experiments, we demonstrate that our approach significantly improves the reconstruction quality in various metrics and recovers clear fine details of the 3D geometries. Our extensive ablation studies provide insights into the development of effective 3D geometric feature learning schemes. Project page: https://cvrecon.ziyue.cool
Self-Supervised Object Detection from Egocentric Videos
Authors: Peri Akiva; Jing Huang; Kevin J Liang; Rama Kovvuri; Xingyu Chen; Matt Feiszli; Kristin Dana; Tal Hassner
Venue: ICCV 2023 (main)

Abstract: Understanding the visual world from the perspective of humans (egocentric) has been a long-standing challenge in computer vision. Egocentric videos exhibit high scene complexity and irregular motion flows compared to typical video understanding tasks. With the egocentric domain in mind, we address the problem of self-supervised, class-agnostic object detection, which aims to locate all objects in a given view, regardless of category, without any annotations or pre-training weights. Our method, self-supervised object Detection from Egocentric VIdeos (DEVI), generalizes appearance-based methods to learn features that are category-specific and invariant to viewing angles and illumination conditions from highly ambiguous environments in an end-to-end manner. Our approach leverages typical human behavior and its egocentric perception to sample diverse views of the same objects for our multi-view and scale-regression loss functions. With our learned cluster residual module, we are able to effectively describe multi-category patches for better complex scene understanding. DEVI provides a boost in performance on recent egocentric datasets, with performance gains up to 4.11% AP50, 0.11% AR1, 1.32% AR10, and 5.03% AR100, while significantly reducing model complexity. We also demonstrate competitive performance on out-of-domain datasets without additional training or fine-tuning.
Prior-guided Source-free Domain Adaptation for Human Pose Estimation
Authors: Dripta S. Raychaudhuri; Calvin-Khang Ta; Arindam Dutta; Rohit Lal; Amit K. Roy-Chowdhury
Venue: ICCV 2023 (main)

Abstract: Domain adaptation methods for 2D human pose estimation typically require continuous access to the source data during adaptation, which can be challenging due to privacy, memory, or computational constraints. To address this limitation, we focus on the task of source-free domain adaptation for pose estimation, where a source model must adapt to a new target domain using only unlabeled target data. Although recent advances have introduced source-free methods for classification tasks, extending them to the regression task of pose estimation is non-trivial. In this paper, we present Prior-guided Self-training (POST), a pseudo-labeling approach that builds on the popular Mean Teacher framework to compensate for the distribution shift. POST leverages prediction-level and feature-level consistency between a student and teacher model against certain image transformations. In the absence of source data, POST utilizes a human pose prior that regularizes the adaptation process by directing the model to generate more accurate and anatomically plausible pose pseudo-labels. Despite being simple and intuitive, our framework can deliver significant performance gains compared to applying the source model directly to the target data, as demonstrated in our extensive experiments and ablation studies. In fact, our approach achieves comparable performance to recent state-of-the-art
 methods that use source data for adaptation
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment
Authors: Bingyang Zhou; Haoyu Zhou; Tianhai Liang; Qiaojun Yu; Siheng Zhao; Yuwei Zeng; Jun Lv; Siyuan Luo; Qiancai Wang; Xinyuan Yu; Haonan Chen; Cewu Lu; Lin Shao
Venue: ICCV 2023 (main)

Abstract: We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations. Our dataset consists of around 4000 models covering 11 categories annotated with clothes features, boundary lines, and keypoints. ClothesNet can be used to facilitate a variety of computer vision and robot interaction tasks. Using our dataset, we establish benchmark tasks for clothes perception, including classification, boundary line segmentation, and keypoint detection, and develop simulated clothes environments for robotic interaction tasks, including rearranging, folding, hanging, and dressing. 
  We also demonstrate the efficacy of our ClothesNet in real-world experiments.
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
Authors: Chenxin Xu; Robby T. Tan; Yuhong Tan; Siheng Chen; Xinchao Wang; Yanfeng Wang
Venue: ICCV 2023 (main)

Abstract: Exploring spatial-temporal dependencies from observed motions is one of the core challenges of human motion prediction. Previous methods mainly focus on dedicated network structures to model the spatial and temporal dependencies. This paper considers a new direction by introducing a model learning framework with auxiliary tasks. In our auxiliary tasks, partial body joints' coordinates are corrupted by either masking or adding noise and the goal is to recover corrupted coordinates depending on the rest coordinates. To work with auxiliary tasks, we propose a novel auxiliary-adapted transformer, which can handle incomplete, corrupted motion data and achieve coordinate recovery via capturing spatial-temporal dependencies. Through auxiliary tasks, the auxiliary-adapted transformer is promoted to capture more comprehensive spatial-temporal dependencies among body joints' coordinates, leading to better feature learning. Extensive experimental results have shown that our method outperforms state-of-the-art methods by remarkable margins of 7.2%, 3.7%, and 9.4% in terms of 3D mean per joint position error (MPJPE) on the Human3.6M, CMU Mocap, and 3DPW datasets, respectively. We also demonstrate that our method is more robust under data missing cases and noisy data cases. Code is available at https://github.com/MediaBrain-SJTU/AuxFormer.
Measuring Asymmetric Gradient Discrepancy in Parallel Continual Learning
Authors: Fan Lyu; Qing Sun; Fanhua Shang; Liang Wan; Wei Feng
Venue: ICCV 2023 (main)

Abstract: In Parallel Continual Learning (PCL), the parallel multiple tasks start and end training unpredictably, thus suffering from training conflict and catastrophic forgetting issues. The two issues are raised because the gradients from parallel tasks differ in directions and magnitudes. Thus, in this paper, we formulate the PCL into a minimum distance optimization problem among gradients and propose an explicit Asymmetric Gradient Distance (AGD) to evaluate the gradient discrepancy in PCL. AGD considers both gradient magnitude ratios and directions, and has a tolerance when updating with a small gradient of inverse direction, which reduces the imbalanced influence of gradients on parallel task training. Moreover, we propose a novel Maximum Discrepancy Optimization (MaxDO) strategy to minimize the maximum discrepancy among multiple gradients. Solving by MaxDO with AGD, parallel training reduces the influence of the training conflict and suppresses the catastrophic forgetting of finished tasks. Extensive experiments validate the effectiveness of our approach on three image recognition datasets.
StyleLipSync: Style-based Personalized Lip-sync Video Generation
Authors: Taekyung Ki; Dongchan Min
Venue: ICCV 2023 (main)

Abstract: In this paper, we present StyleLipSync, a style-based personalized lip-sync video generative model that can generate identity-agnostic lip-synchronizing video from arbitrary audio. To generate a video of arbitrary identities, we leverage expressive lip prior from the semantically rich latent space of a pre-trained StyleGAN, where we can also design a video consistency with a linear transformation. In contrast to the previous lip-sync methods, we introduce pose-aware masking that dynamically locates the mask to improve the naturalness over frames by utilizing a 3D parametric mesh predictor frame by frame. Moreover, we propose a few-shot lip-sync adaptation method for an arbitrary person by introducing a sync regularizer that preserves lip-sync generalization while enhancing the person-specific visual information. Extensive experiments demonstrate that our model can generate accurate lip-sync videos even with the zero-shot setting and enhance characteristics of an unseen face using a few seconds of target video through the proposed adaptation method.