mtybilly/Instruct2DS huggingface dataset:
PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation
Authors: Noranart Vesdapunt, Ning Zhou, Jun Wu, Cong Phuoc Huynh, Xuelu Li, Kah Kuen Fu, Chen-Nee Chuah Zhengfeng Lai
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source domain to tackle the learning tasks on the unlabeled target domain. It can be more challenging when a large domain gap exists between the source and the target domain. A more practical setting is to utilize a large-scale pre-trained model to fill the domain gap. For example, CLIP shows promising zero-shot generalizability to bridge the gap. However, after applying traditional fine-tuning to specifically adjust CLIP on a target domain, CLIP suffers from catastrophic forgetting issues where the new domain knowledge can quickly override CLIP's pre-trained knowledge and decreases the accuracy by half. We propose Catastrophic Forgetting Measurement (CFM) to adjust the learning rate to avoid excessive training (thus mitigating the catastrophic forgetting issue). We then utilize CLIP's zero-shot prediction to formulate a Pseudo-labeling setting with Adaptive Debiasing in CLIP (PADCLIP) by adjusting causal inference with our momentum and CFM. Our PADCLIP allows end-to-end training on source and target domains without extra overhead, and we achieved the best results on four public datasets, with a significant improvement (+18.5% accuracy) on DomainNet.
Causal-DFQ: Causality Guided Data-Free Network Quantization
Authors: Bingxin Xu, Gaowen Liu, Ramana Rao Kompella, Yan Yan Yuzhang Shang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devices. There is a common assumption in quantization methods from prior works that training data is available. In practice, however, this assumption cannot always be fulfilled due to reasons of privacy and security, rendering these methods inapplicable in real-life situations. 
 Thus, data-free network quantization has recently received significant attention in neural network compression. Causal reasoning provides an intuitive way to model casual relationships to eliminate data-driven correlations, making causality an essential component of analyzing data-free problems. However, causal formulations of data-free quantization are inadequate in the literature. To bridge this gap, we construct a causal graph to model the data generation and discrepancy reduction between the pre-trained and quantized models. Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions. Specifically, we design a content-style-decoupled generator, synthesizing images conditioned on the relevant and irrelevant factors; then we propose a discrepancy reduction loss to align the intervened distributions of the pre-trained and quantized models. It is worth noting that our work is the first attempt towards introducing causality to data-free quantization problem. Extensive experiments demonstrate the efficacy of Causal-DFQ.
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
Authors: Yaoyao Zhong, Yuhang Zhang, Lixiong Qin, Weihong Deng Xuannan Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples. Compared to instance-specific adversarial examples, UAP is more challenging as it needs to generalize across various samples and models. In this paper, we examine the serious dilemma of UAP generation methods from a generalization perspective -- the gradient vanishing problem using small-batch stochastic gradient optimization and the local optima problem using large-batch optimization. To address these problems, we propose a simple and effective method called Stochastic Gradient Aggregation (SGA), which alleviates the gradient vanishing and escapes from poor local optima at the same time. Specifically, SGA employs the small-batch training to perform multiple iterations of inner pre-search. Then, all the inner gradients are aggregated as a one-step gradient estimation to enhance the gradient stability and reduce quantization errors. Extensive experiments on the standard ImageNet dataset demonstrate that our method significantly enhances the generalization ability of UAP and outperforms other state-of-the-art methods. The code is available at https://github.com/liuxuannan/Stochastic-Gradient-Aggregation.
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Authors: Yingda Xia, Jiawen Yao, Ke Yan, Jianpeng Zhang, Le Lu, Fakai Wang, Bo Zhou, Mingyan Qiu, Qihang Yu, Mingze Yuan, Wei Fang, Yuxing Tang, Minfeng Xu, Jian Zhou, Yuqian Zhao, Qifeng Wang, Xianghua Ye, Xiaoli Yin, Yu Shi, Xin Chen, Jingren Zhou, Alan Yuille, Zaiyi Liu, Ling Zhang Jieneng Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan
Authors: Fan Wang, Jun Shu, Haifeng Wang, Li Wang, Deyu Meng, Chunfeng Lian Yongheng Sun
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Brain tissue segmentation is essential for neuroscience and clinical studies. However, segmentation on longitudinal data is challenging due to dynamic brain changes across the lifespan. Previous researches mainly focus on self-supervision with regularizations and will lose longitudinal generalization when fine-tuning on a specific age group. In this paper, we propose a dual meta-learning paradigm to learn longitudinally consistent representations and persist when fine-tuning. Specifically, we learn a plug-and-play feature extractor to extract longitudinal-consistent anatomical representations by meta-feature learning and a well-initialized task head for fine-tuning by meta-initialization learning. Besides, two class-aware regularizations are proposed to encourage longitudinal consistency. Experimental results on the iSeg2019 and ADNI datasets demonstrate the effectiveness of our method.
DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image
Authors: Xiang Yu, Meng Ye, Qilong Zhangli, Zhuowei Li, Zhixing Zhang, Dimitris N. Metaxas Di Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Explicit 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics. By leveraging a set of primitives to represent the target shape, recent methods have achieved promising results. However, these methods either use a relatively larger number of primitives or lack geometric flexibility due to the low-dimensional expressibility of the primitives. In this paper, we propose a novel bi-channel Transformer architecture, integrated with parameterized deformable models, termed DeFormer, to simultaneously estimate global and local deformations. In this way, DeFormer can abstract complex object shapes while using a small number of primitives which offer a broader geometry coverage and finer details. Then, we introduce a force-driven dynamic fitting and a cycle-consistent re-projection loss to optimize the primitive parameters. Extensive experiments on ShapeNet across various settings show that DeFormer achieves better reconstruction accuracy over the state-of-the-art, and visualizes with consistent semantic correspondences for improved interpretability.
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition
Authors: Sanping Zhou, Le Wang, Gang Hua Xingyu Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Learning discriminative features from very few labeled samples to identify novel classes has received increasing attention in skeleton-based action recognition. Existing works aim to learn action-specific embeddings by exploiting either intra-skeleton or inter-skeleton spatial associations, which may lead to less discriminative representations. To address these issues, we propose a novel Parallel Attention Interaction Network (PAINet) that incorporates two complementary branches to strengthen the match by inter-skeleton and intra-skeleton correlation. Specifically, a topology encoding module utilizing topology and physical information is proposed to enhance the modeling of interactive parts and joint pairs in both branches. In the Cross Spatial Alignment branch, we employ a spatial cross-attention module to establish joint associations across sequences, and a directional Average Symmetric Surface Metric is introduced to locate the closest temporal similarity. In parallel, the Cross Temporal Alignment branch incorporates a spatial self-attention module to aggregate spatial context within sequences as well as applies the temporal cross-attention network to correct misalignment temporally and calculate similarity. Extensive experiments on three skeleton benchmarks, namely NTU-T, NTU-S, and Kinetics, demonstrate the superiority of our framework and consistently outperform state-of-the-art methods.
Cross-view Semantic Alignment for Livestreaming Product Recognition
Authors: Yiyi Chen, Yan Li, Yanhua Cheng, Xudong Liu, Quan Chen, Han Li Wenjie Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Live commerce is the act of selling products online through livestreaming. The customer's diverse demands for online products introduces more challenges to Livestreaming Product Recognition. Previous works are either focus on fashion clothing data or subject to single-modal input, thus inconsistent with the real-world scenario where multimodal data from various categories are present. In this paper, we contribute LPR4M, a large-scale multimodal dataset that covers 34 categories, comprises 3 modalities (image, video, and text), and is 50 times larger than the largest publicly available dataset. In addition, LPR4M contains diverse videos and noise modality pair while also having a long-tailed distribution, resembling real-world problems. Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the two views (image and video) of products via instance-level contrastive learning as well as cross-view patch-level feature propagation. A novel Patch Feature Reconstruction loss is proposed to penalize the semantic misalignment between the cross-view patches. Extensive ablation studies demonstrate the effectiveness of RICE and provide insights into the importance of dataset diversity and expressivity.
Continuously Masked Transformer for Image Inpainting
Authors: Chang-Su Kim Keunsoo Ko
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: A novel continuous-mask-aware transformer for image inpainting, called CMT, is proposed in this paper, which uses a continuous mask to represent the amounts of errors in tokens. First, we initialize a mask and use it during the self-attention. To facilitate the masked self-attention, we also introduce the notion of overlapping tokens. Second, we update the mask by modeling the error propagation during the masked self-attention. Through several masked self-attention and mask update (MSAU) layers, we predict initial inpainting results. Finally, we refine the initial results to reconstruct a more faithful image. Experimental results on multiple datasets show that the proposed CMT algorithm outperforms existing algorithms significantly. The source codes are available at https://github.com/keunsoo-ko/CMT.
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
Authors: Shaohui Liu, Petr Hruby, Marc Pollefeys, Daniel Barath Rémi Pautrat
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal vanishing points, and the unknown focal length of the camera, leveraging a prior vertical direction. The direction can come from an Inertial Measurement Unit that is a standard component of recent consumer devices, e.g., smartphones. We provide an exhaustive analysis of minimal line configurations and derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. Additionally, we design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization. Combining all solvers in a hybrid robust estimator, our method achieves increased accuracy even with a rough prior. Experiments on synthetic and real-world datasets demonstrate the superior accuracy of our method compared to the state of the art, while having comparable runtimes. We further demonstrate the applicability of our solvers for relative rotation estimation. The code is available at https://github.com/cvg/VP-Estimation-with-Prior-Gravity.
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction
Authors: Changhe Chen, Amir Rasouli Mozhgan Pourkeshavarz
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Predicting diverse yet admissible trajectories that adhere to the map constraints is challenging. Graph-based scene encoders have been proven effective for preserving local structures of maps by defining lane-level connections. However, such encoders do not capture more complex patterns emerging from long-range heterogeneous connections between nonadjacent interacting lanes. To this end, we shed new light on learning common driving patterns by introducing meTA ROad paTh (TAROT) to formulate combinations of various relations between lanes on the road topology. Intuitively, this can be viewed as finding feasible routes. Furthermore, we propose MEta-road NeTwORk (MENTOR) that helps trajectory prediction by providing it with TAROT as navigation tips. More specifically, 1) we define TAROT prediction as a novel self-supervised proxy task to identify the complex heterogeneous structure of the map. 2) For typical driving actions, we establish several TAROTs that result in multiple Heterogeneous Structure Learning (HSL) tasks. These tasks are used in MENTOR, which performs meta-learning by simultaneously predicting trajectories along with proxy tasks, identifying an optimal combination of them, and automatically balancing them to improve the primary task. We show that our model achieves state-of-the-art performance on the Argoverse dataset, especially on diversity and admissibility metrics, achieving up to 20% improvements in challenging scenarios. We further investigate the contribution of proposed modules in ablation studies.
MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception
Authors: Zheng Ge, Zeming Li, Xiangyu Zhang Hongyu Zhou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: This paper proposes an efficient multi-camera to Bird's-Eye-View (BEV) view transformation method for 3D perception, dubbed MatrixVT.
 Existing view transformers either suffer from poor transformation efficiency or rely on device-specific operators, hindering the broad application of BEV models. In contrast, our method generates BEV features efficiently with only convolutions and matrix multiplications (MatMul). Specifically, we propose describing the BEV feature as the MatMul of image feature and a sparse Feature Transporting Matrix (FTM). A Prime Extraction module is then introduced to compress the dimension of image features and reduce FTM's sparsity. Moreover, we propose the Ring & Ray Decomposition to replace the FTM with two matrices and reformulate our pipeline to reduce calculation further. Compared to existing methods, MatrixVT enjoys a faster speed and less memory footprint while remaining deploy-friendly. Extensive experiments on the nuScenes benchmark demonstrate that our method is highly efficient but obtains results on par with the SOTA method in object detection and map segmentation tasks. Code will be available.
Local and Global Logit Adjustments for Long-Tailed Learning
Authors: Jingna Sun, Hao Yang, Li Chen, Xu Wang, Wenming Yang, Daniel Du, Min Zheng Yingfan Tao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Multi-expert ensemble models for long-tailed learning typically either learn diverse generalists from the whole dataset or aggregate specialists on different subsets. However, the former is insufficient for tail classes due to the high imbalance factor of the entire dataset, while the latter may bring ambiguity in predicting unseen classes. To address these issues, we propose a novel Local and Global Logit Adjustments (LGLA) method that learns experts with full data covering all classes and enlarges the discrepancy among them by elaborated logit adjustments. LGLA consists of two core components: a Class-aware Logit Adjustment (CLA) strategy and an Adaptive Angular Weighted (AAW) loss. The CLA strategy trains multiple experts which excel at each subset using the Local Logit Adjustment (LLA). It also trains one expert specializing in an inversely long-tailed distribution through Global Logit Adjustment (GLA). Moreover, the AAW loss adopts adaptive hard sample mining with respect to different experts to further improve accuracy. Extensive experiments on popular long-tailed benchmarks manifest the superiority of LGLA over the SOTA methods.
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need
Authors: Leon Bottou, Yann Lecun, Randall Balestriero Vivien Cabannes
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Self-Supervised Learning (SSL) has emerged as the solution of choice to learn transferable representations from unlabeled data. However, SSL requires to build samples that are known to be semantically akin, i.e. positive views.
 Requiring such knowledge is the main limitation of SSL and is often tackled by ad-hoc strategies e.g. applying known data-augmentations to the same input.
 In this work, we generalize and formalize this principle through Positive Active Learning (PAL) where an oracle queries semantic relationships between samples.
 PAL achieves three main objectives. First, it is a theoretically grounded learning framework that encapsulates standard SSL but also supervised and semi-supervised learning depending on the employed oracle.
 Second, it provides a consistent algorithm to embed a priori knowledge, e.g. some observed labels, into any SSL losses without any change in the training pipeline.
 Third, it provides a proper active learning framework yielding low-cost solutions to annotate datasets, arguably bringing the gap between theory and practice of active learning that is based on simple-to-answer-by-non-experts queries of semantic relationships between inputs.
Wasserstein Expansible Variational Autoencoder for Discriminative and Generative Continual Learning
Authors: Adrian G. Bors Fei Ye
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Task-Free Continual Learning (TFCL) represents a challenging learning paradigm where a model is trained on the non-stationary data distributions without any knowledge of the task information, thus representing a more practical approach. Despite promising achievements by the Variational Autoencoder (VAE) mixtures in continual learning, such methods ignore the redundancy among the probabilistic representations of their components when performing model expansion, leading to mixture components learning similar tasks. This paper proposes the Wasserstein Expansible Variational Autoencoder (WEVAE), which evaluates the statistical similarity between the probabilistic representation of new data and that represented by each mixture component and then uses it for deciding when to expand the model. Such a mechanism can avoid unnecessary model expansion while ensuring the knowledge diversity among the trained components. In addition, we propose an energy-based sample selection approach that assigns high energies to novel samples and low energies to the samples which are similar to the model's knowledge. Extensive empirical studies on both supervised and unsupervised benchmark tasks demonstrate that our model outperforms all competing methods. The code is available at https://github.com/dtuzi123/WEVAE/.
Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning
Authors: Jianfei Cai, Jing Zhang, Dacheng Tao, Bohan Zhuang Haoyu He
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only tunes a small number of parameters while freezing the vast majority ones to ease storage burden and optimization difficulty. However, existing PEFT methods introduce trainable parameters to the same positions across different tasks depending solely on human heuristics and neglect the domain gaps. To this end, we study where to introduce and how to allocate trainable parameters by proposing a novel Sensitivity-aware visual Parameter-efficient fine-Tuning (SPT) scheme, which adaptively allocates trainable parameters to task-specific important positions given a desired tunable parameter budget. Specifically, our SPT first quickly identifies the sensitive parameters that require tuning for a given task in a data-dependent way. Next, our SPT further boosts the representational capability for the weight matrices whose number of sensitive parameters exceeds a pre-defined threshold by utilizing existing structured tuning methods, e.g., LoRA or Adapter, to replace directly tuning the selected sensitive parameters (unstructured tuning) under the budget. Extensive experiments on a wide range of downstream recognition tasks show that our SPT is complementary to the existing PEFT methods and largely boosts their performance, e.g., SPT improves Adapter with supervised pre-trained ViT-B/16 backbone by 4.2% and 1.4% mean Top-1 accuracy, reaching SOTA performance on FGVC and VTAB-1k benchmarks, respectively. Source code is at https://github.com/ziplab/SPT.
Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events
Authors: Hyeonseong Kim, Yujeong Chae, Kuk-Jin Yoon Hoonhee Cho
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recognizing objects from sparse and noisy events becomes extremely difficult when paired images and category labels do not exist. In this paper, we study label-free event-based object recognition where category labels and paired images are not available. To this end, we propose a joint formulation of object recognition and image reconstruction in a complementary manner. Our method first reconstructs images from events and performs object recognition through Contrastive Language-Image Pre-training (CLIP), enabling better recognition through a rich context of images. Since the category information is essential in reconstructing images, we propose category-guided attraction loss and category-agnostic repulsion loss to bridge the textual features of predicted categories and the visual features of reconstructed images using CLIP. Moreover, we introduce a reliable data sampling strategy and local-global reconstruction consistency to boost joint learning of two tasks. To enhance the accuracy of prediction and quality of reconstruction, we also propose a prototype-based approach using unpaired images. Extensive experiments demonstrate the superiority of our method and its extensibility for zero-shot object recognition. Our project code is available at https://github.com/Chohoonhee/Ev-LaFOR.
Gloss-Free Sign Language Translation: Improving from Visual-Language Pretraining
Authors: Zhigang Chen, Albert Clapés, Jun Wan, Yanyan Liang, Sergio Escalera, Zhen Lei, Du Zhang Benjia Zhou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, involving the translation of visual-gestural language to text. Many previous methods employ an intermediate representation,i.e., gloss sequences, to facilitate SLT, thus transforming it into a two-stage task of sign language recognition (SLR) followed by sign language translation (SLT). However, the scarcity of gloss-annotated sign language data, combined with the information bottleneck in the mid-level gloss representation, has hindered the further development of the SLT task. To address this challenge, we propose a novel Gloss-Free SLT base on Visual-Language Pretraining (GFSLT-VLP), which improves SLT by inheriting language-oriented prior knowledge from pre-trained models, without any gloss annotation assistance. Our approach involves two stages: (i) integrating Contrastive Language-Image Pre-training (CLIP) with masked self-supervised learning to create pre-tasks that bridge the semantic gap between visual and textual representations and restore masked sentences, and (ii) constructing an end-to-end architecture with an encoder-decoder-like structure that inherits the parameters of the pre-trained Visual Encoder and Text Decoder from the first stage. The seamless combination of these novel designs forms a robust sign language representation and significantly improves gloss-free sign language translation. In particular, we have achieved unprecedented improvements in terms of BLEU-4 score on the PHOENIX14T dataset (>=+5) and the CSL-Daily dataset (>=+3) compared to state-of-the-art gloss-free SLT methods. Furthermore, our approach also achieves competitive results on the PHOENIX14T dataset when compared with most of the gloss-based methods.
Weakly-supervised 3D Pose Transfer with Keypoints
Authors: Chen Li, Gim Hee Lee Jinnan Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The main challenges of 3D pose transfer are: 1) Lack of paired training data with different characters performing the same pose; 2) Disentangling pose and shape information from the target mesh; 3) Difficulty in applying to meshes with different topologies. We thus propose a novel weakly-supervised keypoint-based framework to overcome these difficulties. Specifically, we use a topology-agnostic keypoint detector with inverse kinematics to compute transformations between the source and target meshes. Our method only requires supervision on the keypoints, can be applied to meshes with different topologies and is shape-invariant for the target which allows extraction of pose-only information from the target meshes without transferring shape information. We further design a cycle reconstruction to perform self-supervised pose transfer without the need for ground truth deformed mesh with the same pose and shape as the target and source, respectively. We evaluate our approach on benchmark human and animal datasets, where we achieve superior performance compared to the state-of-the-art unsupervised approaches and even comparable performance with the fully supervised approaches. We test on the more challenging Mixamo dataset to verify our approach's ability in handling meshes with different topologies and complex clothes. Cross-dataset evaluation further shows the strong generalization ability of our approach. Our code will be open-sourced upon paper acceptance.
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
Authors: Renrui Zhang, Bowei He, Aojun Zhou, Dong Wang, Bin Zhao, Peng Gao Xiangyang Zhu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks. To improve its capacity on downstream tasks, few-shot learning has become a widely-adopted technique. However, existing methods either exhibit limited performance or suffer from excessive learnable parameters. In this paper, we propose APE, an Adaptive Prior rEfinement method for CLIP's pre-trained knowledge, which achieves superior accuracy with high computational efficiency. Via a prior refinement module, we analyze the inter-class disparity in the downstream data and decouple the domain-specific knowledge from the CLIP-extracted cache model. On top of that, we introduce two model variants, a training-free APE and a training-required APE-T. We explore the trilateral affinities between the test image, prior cache model, and textual representations, and only enable a lightweight category-residual module to be trained. For the average accuracy over 11 benchmarks, both APE and APE-T attain state-of-the-art and respectively outperform the second-best by +1.59% and +1.99% under 16 shots with x30 less learnable parameters.
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
Authors: Yale Song, Sayan Nag, Kevin Qinghong Lin, Hardik Shah, Mike Zheng Shou, Rama Chellappa, Pengchuan Zhang Shraman Pramanick
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks. However, existing egocentric VLP frameworks utilize separate video and language encoders and learn task-specific cross-modal information only during fine-tuning, limiting the development of a unified system. In this work, we introduce the second generation of egocentric video-language pre-training (EgoVLPv2), a significant improvement from the previous generation, by incorporating cross-modal fusion directly into the video and language backbones. EgoVLPv2 learns strong video-text representation during pre-training and reuses the cross-modal attention modules to support different downstream tasks in a flexible and efficient manner, reducing fine-tuning costs. Moreover, our proposed fusion in the backbone strategy is more lightweight and compute-efficient than stacking additional fusion-specific layers. Extensive experiments on a wide range of VL tasks demonstrate the effectiveness of EgoVLPv2 by achieving consistent state-of-the-art performance over strong baselines across all downstream.
On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement
Authors: Yunan Zhu, Shunxin Xu, Dong Liu Xin Luo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Several recent studies advocate the use of spectral discriminators, which evaluate the Fourier spectra of images for generative modeling. However, the effectiveness of the spectral discriminators is not well interpreted yet. We tackle this issue by examining the spectral discriminators in the context of perceptual image super-resolution (i.e., GAN-based SR), as SR image quality is susceptible to spectral changes. Our analyses reveal that the spectral discriminator indeed performs better than the ordinary (a.k.a. spatial) discriminator in identifying the differences in the high-frequency range; however, the spatial discriminator holds an advantage in the low-frequency range. Thus, we suggest that the spectral and spatial discriminators shall be used simultaneously. Moreover, we improve the spectral discriminators by first calculating the patch-wise Fourier spectrum and then aggregating the spectra by Transformer. We verify the effectiveness of the proposed method twofold. On the one hand, thanks to the additional spectral discriminator, our obtained SR images have their spectra better aligned to those of the real images, which leads to a better PD tradeoff. On the other hand, our ensembled discriminator predicts the perceptual quality more accurately, as evidenced in the no-reference image quality assessment task.
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
Authors: Zhen Zhao, Lei Qi, Yu Qiao, Yinghuan Shi, Hengshuang Zhao Lihe Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples. This practice ensures high-quality pseudo labels, but incurs a relatively low utilization of the whole unlabeled set. In this work, our key insight is that these uncertain samples can be turned into certain ones, as long as the confusion classes for the top-1 class are detected and removed. Invoked by this, we propose a novel method dubbed ShrinkMatch to learn uncertain samples. For each uncertain sample, it adaptively seeks a shrunk class space, which merely contains the original top-1 class, as well as remaining less likely classes. Since the confusion ones are removed in this space, the re-calculated top-1 confidence can satisfy the pre-defined threshold. We then impose a consistency regularization between a pair of strongly and weakly augmented samples in the shrunk space to strive for discriminative representations. Furthermore, considering the varied reliability among uncertain samples and the gradually improved model during training, we correspondingly design two reweighting principles for our uncertain loss. Our method exhibits impressive performance on widely adopted benchmarks. Code is available at https://github.com/LiheYoung/ShrinkMatch.
Deep Equilibrium Object Detection
Authors: Yao Teng, Limin Wang Shuai Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Query-based object detectors directly decode image features into object instances with a set of learnable queries. These query vectors are progressively refined to stable meaningful representations through a sequence of decoder layers, and then used to directly predict object locations and categories with simple FFN heads. 
 In this paper, we present a new query-based object detector (DEQDet) by designing a deep equilibrium decoder. Our DEQ decoder models the query vector refinement as the fixed point solving of an implicit layer and is equivalent to applying infinite steps of refinement.
 To be more specific to object decoding, we use a two-step unrolled equilibrium equation to explicitly capture the query vector refinement. Accordingly, we are able to incorporate refinement awareness into the DEQ training with the inexact gradient back-propagation (RAG). In addition, to stabilize the training of our DEQDet and improve its generalization ability, we devise the deep supervision scheme on the optimization path of DEQ with refinement-aware perturbation (RAP).
 Our experiments demonstrate DEQDet converges faster, consumes less memory, and achieves better results than the baseline counterpart (AdaMixer). In particular, our DEQDet with ResNet50 backbone and 300 queries achieves the 49.5 mAP and 33.0 APs on the MS COCO benchmark under 2x training scheme (24 epochs).
Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation
Authors: Zhenhua Liu, Xinfeng Zhang, Zhao Wang, Kai Han, Shanshe Wang, Siwei Ma, Wen Gao Wenkang Shan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D human pose estimation. On the one hand, D3DP generates multiple possible 3D pose hypotheses for a single 2D observation. It gradually diffuses the ground truth 3D poses to a random distribution, and learns a denoiser conditioned on 2D keypoints to recover the uncontaminated 3D poses. The proposed D3DP is compatible with existing 3D pose estimators and supports users to balance efficiency and accuracy during inference through two customizable parameters. On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use. It reprojects 3D pose hypotheses to the 2D camera plane, selects the best hypothesis joint-by-joint based on the reprojection errors, and combines the selected joints into the final pose. The proposed JPMA conducts aggregation at the joint level and makes use of the 2D prior information, both of which have been overlooked by previous approaches. Extensive experiments on Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the state-of-the-art deterministic and probabilistic approaches by 1.5% and 8.9%, respectively. Code is available at https://github.com/paTRICK-swk/D3DP.
RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation
Authors: Yuxin Mao, Jing Zhang, Yuchao Dai Zhexiong Wan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recently, the RGB images and point clouds fusion methods have been proposed to jointly estimate 2D optical flow and 3D scene flow. However, as both conventional RGB cameras and LiDAR sensors adopt a frame-based data acquisition mechanism, their performance is limited by the fixed low sampling rates, especially in highly-dynamic scenes. By contrast, the event camera can asynchronously capture the intensity changes with a very high temporal resolution, providing complementary dynamic information of the observed scenes. In this paper, we incorporate RGB images, Point clouds and Events for joint optical flow and scene flow estimation with our proposed multi-stage multimodal fusion model, RPEFlow. First, we present an attention fusion module with a cross-attention mechanism to implicitly explore the internal cross-modal correlation for 2D and 3D branches, respectively. Second, we introduce a mutual information regularization term to explicitly model the complementary information of three modalities for effective multimodal feature learning. We also contribute a new synthetic dataset to advocate further research. Experiments on both synthetic and real datasets show that our model outperforms the existing state-of-the-art by a wide margin. Code and dataset is available at https://npucvr.github.io/RPEFlow.
SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-Training
Authors: Chen Wei, Huiyu Wang, Alan Yuille, Cihang Xie Yuanze Lin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Video-language pre-training is crucial for learning powerful multi-modal representation. However, it typically requires a massive amount of computation. In this paper, we develop SMAUG, an efficient pre-training framework for video-language models. The foundation component in SMAUG is masked autoencoders. Different from prior works which only mask textual inputs, our masking strategy considers both visual and textual modalities, providing a better cross-modal alignment and saving more pre-training costs. On top of that, we introduce a space-time token sparsification module, which leverages context information to further select only "important" spatial regions and temporal frames for pre-training. Coupling all these designs allows our method to enjoy both competitive performances on text-to-video retrieval and video question answering tasks, and much less pre-training costs by 1.9x or more. For example, our SMAUG only needs  50 NVIDIA A6000 GPU hours for pre-training to attain competitive performances on these two video-language tasks across six popular benchmarks.
eP-ALM: Efficient Perceptual Augmentation of Language Models
Authors: Corentin Dancette, Matthieu Cord Mustafa Shukor
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Large Language Models (LLMs) have so far impressed the world, with unprecedented capabilities that emerge in models at large scales. On the vision side, transformer models (i.e., ViT) are following the same trend, achieving the best performance on challenging benchmarks. With the abundance of such unimodal models, a natural question arises; do we need also to follow this trend to tackle multimodal tasks? In this work, we propose to rather direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. In particular, they still train a large number of parameters, rely on large multimodal pretraining, use encoders (e.g., CLIP) trained on huge image-text datasets, and add significant inference overhead. In addition, most of these approaches have focused on Zero-Shot and In Context Learning, with little to no effort on direct finetuning. We investigate the minimal computational effort needed to adapt unimodal models for multimodal tasks and propose a new challenging setup, alongside different approaches, that efficiently adapts unimodal pretrained models. 
 We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning across Image, Video, and Audio modalities, following the proposed setup. The code is available here: https://github.com/mshukor/eP-ALM.
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction
Authors: Hao Chen Yingxue Xu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Survival prediction is a complicated ordinal regression task that aims to predict the ranking risk of death, which generally benefits from the integration of histology and genomic data. Despite the progress in joint learning from pathology and genomics, existing methods still suffer from challenging issues: 1) Due to the large size of pathological images, it is difficult to effectively represent the gigapixel whole slide images (WSIs). 2) Interactions within tumor microenvironment (TME) in histology are essential for survival analysis. Although current approaches attempt to model these interactions via co-attention between histology and genomic data, they focus on only dense local similarity across modalities, which fails to capture global consistency between potential structures, i.e. TME-related interactions of histology and co-expression of genomic data. To address these challenges, we propose a Multimodal Optimal Transport-based Co-Attention Transformer framework with global structure consistency, in which optimal transport (OT) is applied to match patches of a WSI and genes embeddings for selecting informative patches to represent the gigapixel WSI. More importantly, OT-based co-attention provides a global awareness to effectively capture structural interactions within TME for survival prediction. To overcome high computational complexity of OT, we propose a robust and efficient implementation over micro-batch of WSI patches by approximating the original OT with unbalanced mini-batch OT. Extensive experiments show the superiority of our method on five benchmark datasets compared to the state-of-the-art methods. The code will be released.
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
Authors: Ziyue Xu, Dong Yang, Vishwesh Nath, Wenqi Li, Can Zhao, Daguang Xu, Yiran Chen, Holger R. Roth Jingwei Sun
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Federated learning is a popular collaborative learning approach that enables clients to train a global model without sharing their local data. Vertical federated learning (VFL) deals with scenarios in which the data on clients have different feature spaces but share some overlapping samples. Existing VFL approaches suffer from high communication costs and cannot deal efficiently with limited overlapping samples commonly seen in the real world. We propose a practical vertical federated learning (VFL) framework called one-shot VFL that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning. We also propose few-shot VFL to improve the accuracy further with just one more communication round between the server and the clients. In our proposed framework, the clients only need to communicate with the server once or only a few times. We evaluate the proposed VFL framework on both image and tabular datasets. Our methods can improve the accuracy by more than 46.5% and reduce the communication cost by more than 330xcompared with state-of-the-art VFL methods when evaluated on CIFAR-10. Our code is publicly available.