GenAI4ELab/papercli-papers huggingface dataset:
UniVTG: Towards Unified Video-Language Temporal Grounding
Authors: Kevin Qinghong Lin; Pengchuan Zhang; Joya Chen; Shraman Pramanick; Difei Gao; Alex Jinpeng Wang; Rui Yan; Mike Zheng Shou
Venue: ICCV 2023 (main)

Abstract: Video Temporal Grounding (VTG), which aims to ground target clips from videos (such as consecutive intervals or disjoint shots) according to custom language queries (e.g., sentences or words), is key for video browsing on social media. Most methods in this direction develop task-specific models that are trained with type-specific labels, such as moment retrieval (time interval) and highlight detection (worthiness curve), which limits their abilities to generalize to various VTG tasks and labels. In this paper, we propose to Unify the diverse VTG labels and tasks, dubbed UniVTG, along three directions: Firstly, we revisit a wide range of VTG labels and tasks and define a unified formulation. Based on this, we develop data annotation schemes to create scalable pseudo supervision. Secondly, we develop an effective and flexible grounding model capable of addressing each task and making full use of each label. Lastly, thanks to the unified framework, we are able to unlock temporal grounding pretraining from large-scale diverse labels and develop stronger grounding abilities e.g., zero-shot grounding. Extensive experiments on three tasks (moment retrieval, highlight detection and video summarization) across seven datasets (QVHighlights, Charades-STA, TACoS, Ego4D, YouTube Highlights, TV-Sum, and QFVS) demonstrate the effectiveness and flexibility of our proposed framework. The codes are available at https://github.com/showlab/UniVTG.
Disposable Transfer Learning for Selective Source Task Unlearning
Authors: Seunghee Koh; Hyounguk Shon; Janghyeon Lee; Hyeong Gwon Hong; Junmo Kim
Venue: ICCV 2023 (main)

Abstract: Transfer learning is widely used for training deep neural networks (DNN) for building a powerful representation. Even after the pre-trained model is adapted for the target task, the representation performance of the feature extractor is retained to some extent. As the performance of the pre-trained model can be considered the private property of the owner, it is natural to seek the exclusive right of the generalized performance of the pre-trained weight. To address this issue, we suggest a new paradigm of transfer learning called disposable transfer learning (DTL), which disposes of only the source task without degrading the performance of the target task. To achieve knowledge disposal, we propose a novel loss named Gradient Collision loss (GC loss). GC loss selectively unlearns the source knowledge by leading the gradient vectors of mini-batches in different directions. Whether the model successfully unlearns the source task is measured by piggyback learning accuracy (PL accuracy). PL accuracy estimates the vulnerability of knowledge leakage by retraining the scrubbed model on a subset of source data or new downstream data. We demonstrate that GC loss is an effective approach to the DTL problem by showing that the model trained with GC loss retains the performance on the target task with a significantly reduced PL accuracy.
Grounding 3D Object Affordance from 2D Interactions in Images
Authors: Yuhang Yang; Wei Zhai; Hongchen Luo; Yang Cao; Jiebo Luo; Zheng-Jun Zha
Venue: ICCV 2023 (main)

Abstract: Grounding 3D object affordance seeks to locate objects' "action possibilities" regions in the 3D space, which serves as a link between perception and operation for embodied agents. Existing studies primarily focus on connecting visual affordances with geometry structures, e.g., relying on annotations to declare interactive regions of interest on the object and establishing a mapping between the regions and affordances. However, the essence of learning object affordance is to understand how to use it, and the manner that detaches interactions is limited in generalization. Normally, humans possess the ability to perceive object affordances in the physical world through demonstration images or videos. Motivated by this, we introduce a novel task setting: grounding 3D object affordance from 2D interactions in images, which faces the challenge of anticipating affordance through interactions of different sources. To address this problem, we devise a novel Interaction-driven 3D Affordance Grounding Network (IAG), which aligns the region feature of objects from different sources and models the interactive contexts for 3D object affordance grounding. Besides, we collect a Point-Image Affordance Dataset (PIAD) to support the proposed task. Comprehensive experiments on PIAD demonstrate the reliability of the proposed task and the superiority of our method. The project is available at https://github.com/yyvhang/IAGNet.
Fast Globally Optimal Surface Normal Estimation from an Affine Correspondence
Authors: Levente Hajder; Lajos Lóczi; Daniel Barath
Venue: ICCV 2023 (main)

Abstract: We present a new solver for estimating a surface normal from a single affine correspondence in two calibrated views. The proposed approach provides a new globally optimal solution for this over-determined problem and proves that it reduces to a linear system that can be solved extremely efficiently. This allows for performing significantly faster than other recent methods, solving the same problem and obtaining the same globally optimal solution. We demonstrate on 15k image pairs from standard benchmarks that the proposed approach leads to the same results as other optimal algorithms while being, on average, five times faster than the fastest alternative. Besides its theoretical value, we demonstrate that such an approach has clear benefits, e.g., in image-based visual localization, due to not requiring a dense point cloud to recover the surface normal. We show on the Cambridge Landmarks dataset that leveraging the proposed surface normal estimation further improves localization accuracy. Matlab and C++ implementations are also published in the supplementary material.
Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos
Authors: Zhiqiang Shen; Xiaoxiao Sheng; Hehe Fan; Longguang Wang; Yulan Guo; Qiong Liu; Hao Wen; Xi Zhou
Venue: ICCV 2023 (main)

Abstract: Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, annotating point cloud videos is usually notoriously expensive. Moreover, training via one or only a few traditional tasks (e.g., classification) may be insufficient to learn subtle details of the spatio-temporal structure existing in point cloud videos. In this paper, we propose a Masked Spatio-Temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations. MaST-Pre is based on spatio-temporal point-tube masking and consists of two self-supervised learning tasks. First, by reconstructing masked point tubes, our method is able to capture the appearance information of point cloud videos. Second, to learn motion, we propose a temporal cardinality difference prediction task that estimates the change in the number of points within a point tube. In this way, MaST-Pre is forced to model the spatial and temporal structure in point cloud videos. Extensive experiments on MSRAction-3D, NTU-RGBD, NvGesture, and SHREC'17 demonstrate the effectiveness of the proposed method.
Frequency-aware GAN for Adversarial Manipulation Generation
Authors: Peifei Zhu; Genki Osada; Hirokatsu Kataoka; Tsubasa Takahashi
Venue: ICCV 2023 (main)

Abstract: Image manipulation techniques have drawn growing concerns as manipulated images might cause morality and security problems. Various methods have been proposed to detect manipulations and achieved promising performance. However, these methods might be vulnerable to adversarial attacks. In this work, we design an Adversarial Manipulation Generation (AMG) task to explore the vulnerability of image manipulation detectors. We first propose an optimal loss function and extend existing attacks to generate adversarial examples. We observe that existing spatial attacks cause large degradation in image quality and find the loss of high-frequency detailed components might be its major reason. Inspired by this observation, we propose a novel adversarial attack that incorporates both spatial and frequency features into the GAN architecture to generate adversarial examples. We further design an encoder-decoder architecture with skip connections of high-frequency components to preserve fine details. We evaluated our method on three image manipulation detectors (FCN, ManTra-Net and MVSS-Net) with three benchmark datasets (DEFACTO, CASIAv2 and COVER). Experiments show that our method generates adversarial examples significantly fast (0.01s per image), preserves better image quality (PSNR 30% higher than spatial attacks) and achieves a high attack success rate. We also observe that the examples generated by AMG can fool both classification and segmentation models, which indicates better transferability among different tasks.
DreamPose: Fashion Video Synthesis with Stable Diffusion
Authors: Johanna Karras; Aleksander Holynski; Ting-Chun Wang; Ira Kemelmacher-Shlizerman
Venue: ICCV 2023 (main)

Abstract: We present DreamPose, a diffusion-based method for generating animated fashion videos from still images. Given an image and a sequence of human body poses, our method synthesizes a video containing both human and fabric motion. To achieve this, we transform a pretrained text-to-image model (Stable Diffusion) into a pose-and-image guided video synthesis model, using a novel finetuning strategy, a set of architectural changes to support the added conditioning signals, and techniques to encourage temporal consistency. We fine-tune on a collection of fashion videos from the UBC Fashion dataset. We evaluate our method on a variety of clothing styles and poses, and demonstrate that our method produces state-of-the-art results on fashion video animation. Video results are available at www.grail.cs.washington.edu/projects/dreampose.
Tube-Link: A Flexible Cross Tube Framework for Universal Video Segmentation
Authors: Xiangtai Li; Haobo Yuan; Wenwei Zhang; Guangliang Cheng; Jiangmiao Pang; Chen Change Loy
Venue: ICCV 2023 (main)

Abstract: Video segmentation aims to segment and track every pixel in diverse scenarios accurately. In this paper, we present Tube-Link, a versatile framework that addresses multiple core tasks of video segmentation with a unified architecture. Our framework is a near-online approach that takes a short subclip as input and outputs the corresponding spatial-temporal tube masks. To enhance the modeling of cross-tube relationships, we propose an effective way to perform tube-level linking via attention along the queries. In addition, we introduce temporal contrastive learning to instance-wise discriminative features for tube-level association. Our approach offers flexibility and efficiency for both short and long video inputs, as the length of each subclip can be varied according to the needs of datasets or scenarios. Tube-Link outperforms existing specialized architectures by a significant margin on five video segmentation datasets. Specifically, it achieves almost 13% relative improvements on VIPSeg and 4% improvements on KITTI-STEP over the strong baseline Video K-Net. When using a ResNet50 backbone on Youtube-VIS-2019 and 2021, Tube-Link boosts IDOL by 3% and 4%, respectively. Code is available at https://github.com/lxtGH/Tube-Link.
Hybrid Spectral Denoising Transformer with Guided Attention
Authors: Zeqiang Lai; Chenggang Yan; Ying Fu
Venue: ICCV 2023 (main)

Abstract: In this paper, we present a Hybrid Spectral Denoising Transformer (HSDT) for hyperspectral image denoising. Challenges in adapting transformer for HSI arise from the capabilities to tackle existing limitations of CNN-based methods in capturing the global and local spatial-spectral correlations while maintaining efficiency and flexibility. To address these issues, we introduce a hybrid approach that combines the advantages of both models with a Spatial-Spectral Separable Convolution (S3Conv), Guided Spectral Self-Attention (GSSA), and Self-Modulated Feed-Forward Network (SM-FFN). Our S3Conv works as a lightweight alternative to 3D convolution, which extracts more spatial-spectral correlated features while keeping the flexibility to tackle HSIs with an arbitrary number of bands. These features are then adaptively processed by GSSA which performs 3D self-attention across the spectral bands, guided by a set of learnable queries that encode the spectral signatures. This not only enriches our model with powerful capabilities for identifying global spectral correlations but also maintains linear complexity. Moreover, our SM-FFN proposes the self-modulation that intensifies the activations of more informative regions, which further strengthens the aggregated features. Extensive experiments are conducted on various datasets under both simulated and real-world noise, and it shows that our HSDT significantly outperforms the existing state-of-the-art methods while maintaining low computational overhead. Code is at https://github.com/Zeqiang-Lai/HSDT.
HiVLP: Hierarchical Interactive Video-Language Pre-Training
Authors: Bin Shao; Jianzhuang Liu; Renjing Pei; Songcen Xu; Peng Dai; Juwei Lu; Weimian Li; Youliang Yan
Venue: ICCV 2023 (main)

Abstract: Video-Language Pre-training (VLP) has become one of the most popular research topics in deep learning. However, compared to image-language pre-training, VLP has lagged far behind due to the lack of large amounts of video-text pairs. In this work, we train a VLP model with a hybrid of image-text and video-text pairs, which significantly outperforms pre-training with only the video-text pairs. Besides, existing methods usually model the cross-modal interaction using cross-attention between single-scale visual tokens and textual tokens. These visual features are either of low resolutions lacking fine-grained information, or of high resolutions without high-level semantics. To address the issue, we propose Hierarchical interactive Video-Language Pre-training (HiVLP) that efficiently uses a hierarchical visual feature group for multi-modal cross-attention during pre-training. In the hierarchical framework, low-resolution features are learned with focus on more global high-level semantic information, while high-resolution features carry fine-grained details. As a result, HiVLP has the ability to effectively learn both the global and fine-grained representations to achieve better alignment between video and text inputs. Furthermore, we design a hierarchical multi-scale vision contrastive loss for self-supervised learning to boost the interaction between them. Experimental results show that HiVLP establishes new state-of-the-art results in three downstream tasks, text-video retrieval, video-text retrieval, and video captioning.
Learning Concordant Attention via Target-aware Alignment for Visible-Infrared Person Re-identification
Authors: Jianbing Wu; Hong Liu; Yuxin Su; Wei Shi; Hao Tang
Venue: ICCV 2023 (main)

Abstract: Owing to the large distribution gap between the heterogeneous data in Visible-Infrared Person Re-identification (VI Re-ID), we point out that existing paradigms often suffer from the inter-modal semantic misalignment issue and thus fail to align and compare local details properly. In this paper, we present Concordant Attention Learning (CAL), a novel framework that learns semantic-aligned representations for VI Re-ID. Specifically, we design the Target-aware Concordant Alignment paradigm, which allows target-aware attention adaptation when aligning heterogeneous samples (i.e., adaptive attention adjustment according to the target image being aligned). This is achieved by exploiting the discriminative clues from the modality counterpart and designing effective modality-agnostic correspondence searching strategies. To ensure semantic concordance during the cross-modal retrieval stage, we further propose MatchDistill, which matches the attention patterns across modalities and learns their underlying semantic correlations by bipartite-graph-based similarity modeling and cross-modal knowledge exchange. Extensive experiments on VI Re-ID benchmark datasets demonstrate the effectiveness and superiority of the proposed CAL.
PhysDiff: Physics-Guided Human Motion Diffusion Model
Authors: Ye Yuan; Jiaming Song; Umar Iqbal; Arash Vahdat; Jan Kautz
Venue: ICCV 2023 (main)

Abstract: Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate physically-implausible motions with pronounced artifacts such as floating, foot sliding, and ground penetration. This seriously impacts the quality of generated motions and limits their real-world application. To address this issue, we present a novel physics-guided motion diffusion model (PhysDiff), which incorporates physical constraints into the diffusion process. Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion. The projected motion is further used in the next diffusion step to guide the denoising diffusion process. Intuitively, the use of physics in our model iteratively pulls the motion toward a physically-plausible space, which cannot be achieved by simple post-processing. Experiments on large-scale human motion datasets show that our approach achieves state-of-the-art motion quality and improves physical plausibility drastically (>78% for all datasets).
Masked Motion Predictors are Strong 3D Action Representation Learners
Authors: Yunyao Mao; Jiajun Deng; Wengang Zhou; Yao Fang; Wanli Ouyang; Houqiang Li
Venue: ICCV 2023 (main)

Abstract: In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, researchers have been actively investigating effective self-supervised pre-training strategies. In this work, we show that instead of following the prevalent pretext task to perform masked self-component reconstruction in human joints, explicit contextual motion modeling is key to the success of learning effective feature representation for 3D action recognition. Formally, we propose the Masked Motion Prediction (MAMP) framework. To be specific, the proposed MAMP takes as input the masked spatio-temporal skeleton sequence and predicts the corresponding temporal motion of the masked human joints. Considering the high temporal redundancy of the skeleton sequence, in our MAMP, the motion information also acts as an empirical semantic richness prior that guide the masking process, promoting better attention to semantically rich temporal regions. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed MAMP pre-training substantially improves the performance of the adopted vanilla transformer, achieving state-of-the-art results without bells and whistles. The source code of our MAMP is available at https://github.com/maoyunyao/MAMP.
Template-guided Hierarchical Feature Restoration for Anomaly Detection
Authors: Hewei Guo; Liping Ren; Jingjing Fu; Yuwang Wang; Zhizheng Zhang; Cuiling Lan; Haoqian Wang; Xinwen Hou
Venue: ICCV 2023 (main)

Abstract: Targeting for detecting anomalies of various sizes for complicated normal patterns, we propose a Template-guided Hierarchical Feature Restoration method, which introduces two key techniques, bottleneck compression and template-guided compensation, for anomaly-free feature restoration. Specially, our framework compresses hierarchical features of an image by bottleneck structure to preserve the most crucial features shared among normal samples. We design template-guided compensation to restore the distorted features towards anomaly-free features. Particularly, we choose the most similar normal sample as the template and leverage hierarchical features from the template to compensate the distorted features. The bottleneck could partially filter out anomaly features, while the compensation further converts the reminding anomaly features towards normal with template guidance. Finally, anomalies are detected in terms of the cosine distance between the pre-trained features of an inference image and the corresponding restored anomaly-free features. Experimental results demonstrate the effectiveness of our approach, which achieves the state-of-the-art performance on the MVTec LOCO AD dataset.
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Authors: Abdelrahman Shaker; Muhammad Maaz; Hanoona Rasheed; Salman Khan; Ming-Hsuan Yang; Fahad Shahbaz Khan
Venue: ICCV 2023 (main)

Abstract: Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolution limits its use in real-time applications, especially for deployment on resource-constrained mobile devices. Although hybrid approaches have been proposed to combine the advantages of convolutions and self-attention for a better speed-accuracy trade-off, the expensive matrix multiplication operations in self-attention remain a bottleneck. In this work, we introduce a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations with linear element-wise multiplications. Our design shows that the key-value interaction can be replaced with a linear layer without sacrificing any accuracy. Unlike previous state-of-the-art methods, our efficient formulation of self-attention enables its usage at all stages of the network. Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Our small variant achieves 78.5% top-1 ImageNet-1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2x faster compared to MobileViT-v2. Our code and models: https://tinyurl.com/5ft8v46w
UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes
Authors: Sunwook Hwang; Youngseok Kim; Seongwon Kim; Saewoong Bahk; Hyung-Sin Kim
Venue: ICCV 2023 (main)

Abstract: Semi-supervised Learning (SSL) has received increasing attention in autonomous driving to reduce the enormous burden of 3D annotation. In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level point cloud: learning from unlabeled de-identified intermediate features (i.e., "smashed" data) to preserve privacy. Since these intermediate features are naturally produced by the inference pipeline, no additional computation is required on autonomous vehicles. However, generating effective consistency loss for unlabeled feature-level scene turns out to be a critical challenge. The latest SSL frameworks for 3D object detection that enforce consistency regularization between different augmentations of an unlabeled raw-point scene become detrimental when applied to intermediate features. To solve the problem, we introduce a novel combination of hybrid pseudo labels and feature-level Ground Truth sampling (F-GT), which safely augments unlabeled multi-type 3D scene features and provides high-quality supervision. We implement UpCycling on two representative 3D object detection models: SECOND-IoU and PV-RCNN. Experiments on widely-used datasets (Waymo, KITTI, and Lyft) verify that UpCycling outperforms other augmentation methods applied at the feature level. In addition, while preserving privacy, UpCycling performs better or comparably to the state-of-the-art methods that utilize raw-level unlabeled data in both domain adaptation and partial-label scenarios.