GenAI4ELab/papercli-papers huggingface dataset:
On the Versatile Uses of Partial Distance Correlation in Deep Learning
Authors: Xingjian Zhen; Zihang Meng; Rudrasis Chakraborty; Vikas Singh
Venue: ECCV 2022 (main)

Abstract: "Comparing the functional behavior of neural network models, whether it is a single network over time or two (or more networks) during or post-training, is an essential step in understanding what they are learning (and what they are not), and for identifying strategies for regularization or efficiency improvements. Despite recent progress, e.g., comparing vision transformers to CNNs, systematic comparison of function, especially across different networks, remains difficult and is often carried out layer by layer. Approaches such as canonical correlation analysis (CCA) are applicable in principle, but have been sparingly used so far. In this paper, we revisit a (less widely known) from statistics, called distance correlation (and its partial variant), designed to evaluate correlation between feature spaces of different dimensions. We describe the steps necessary to carry out its deployment for large scale models -- this opens the door to a surprising array of applications ranging from conditioning one deep model w.r.t. another, learning disentangled representations as well as optimizing diverse models that would directly be more robust to adversarial attacks. Our experiments suggest a versatile regularizer (or constraint) with many advantages, which avoids some of the common difficulties one faces in such analyses."
Self-Regulated Feature Learning via Teacher-Free Feature Distillation
Authors: Lujun Li
Venue: ECCV 2022 (main)

Abstract: "Knowledge distillation conditioned on intermediate feature representations always leads to significant performance improvements. Conventional feature distillation framework demands extra selecting/training budgets of teachers and complex transformations to align the features between teacher-student models. To address the problem, we analyze teacher roles in feature distillation and have an intriguing observation: additional teacher architectures are not always necessary. Then we propose Tf-FD, a simple yet effective Teacher-free Feature Distillation framework, reusing channel-wise and layer-wise meaningful features within the student to provide teacher-like knowledge without an additional model. In particular, our framework is subdivided into intra-layer and inter-layer distillation. The intra-layer Tf-FD performs feature salience ranking and transfers the knowledge from salient feature to redundant feature within the same layer. For inter-layer Tf-FD, we deal with distilling high-level semantic knowledge embedded in the deeper layer representations to guide the training of shallow layers. Benefiting from the small gap between these self-features, Tf-FD simply needs to optimize extra feature mimicking losses without complex transformations. Furthermore, we provide insightful discussions to shed light on Tf-FD from feature regularization perspectives. Our experiments conducted on classification and object detection tasks demonstrate that our technique achieves state-of-the-art results on different models with fast training speeds. Code is available at https://lilujunai.github.io/Teacher-free-Distillation/."
Balancing between Forgetting and Acquisition in Incremental Subpopulation Learning
Authors: Mingfu Liang; Jiahuan Zhou; Wei Wei; Ying Wu
Venue: ECCV 2022 (main)

Abstract: "The subpopulation shifting challenge, known as some subpopulations of a category that are not seen during training, severely limits the classification performance of the state-of-the-art convolutional neural networks. Thus, to mitigate this practical issue, we explore incremental subpopulation learning (ISL) to adapt the original model via incrementally learning the unseen subpopulations without retaining the seen population data. However, striking a great balance between subpopulation learning and seen population forgetting is the main challenge in ISL but is not well studied by existing approaches. These incremental learners simply use a pre-defined and fixed hyperparameter to balance the learning objective and forgetting regularization, but their learning is usually biased towards either side in the long run. In this paper, we propose a novel two-stage learning scheme to explicitly disentangle the acquisition and forgetting for achieving a better balance between subpopulation learning and seen population forgetting: in the first “gain-acquisition” stage, we progressively learn a new classifier based on the margin-enforce loss, which enforces the hard samples and population to have a larger weight for classifier updating and avoid uniformly updating all the population; in the second “counter-forgetting” stage, we search for the proper combination of the new and old classifiers by optimizing a novel objective based on proxies of forgetting and acquisition. We benchmark the representative and state-of-the-art non-exemplar-based incremental learning methods on a large-scale subpopulation shifting dataset for the first time. Under almost all the challenging ISL protocols, we significantly outperform other methods by a large margin, demonstrating our superiority to alleviate the subpopulation shifting problem."
Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identification
Authors: Xulin Li; Yan Lu; Bin Liu; Yating Liu; Guojun Yin; Qi Chu; Jinyang Huang; Feng Zhu; Rui Zhao; Nenghai Yu
Venue: ECCV 2022 (main)

Abstract: "Graph-based models have achieved great success in person re-identification tasks recently, which compute the graph topology structure (affinities) among different people first and then pass the information across them to achieve stronger features. But we find existing graph-based methods in the visible-infrared person re-identification task (VI-ReID) suffer from bad generalization because of two issues: 1) train-test modality balance gap, which is a property of VI-ReID task. The number of two modalities data are balanced in the training stage, but extremely unbalanced in inference, causing the low generalization of graph-based VI-ReID methods. 2) sub-optimal topology structure caused by the end-to-end learning manner to the graph module. We analyze that the well-trained input features weaken the learning of graph topology, making it not generalized enough during the inference process. In this paper, we propose a Counterfactual Intervention Feature Transfer (CIFT) method to tackle these problems. Specifically, a Homogeneous and Heterogeneous Feature Transfer (H2FT) is designed to reduce the train-test modality balance gap by two independent types of well-designed graph modules and an unbalanced scenario simulation. Besides, a Counterfactual Relation Intervention (CRI) is proposed to utilize the counterfactual intervention and causal effect tools to highlight the role of topology structure in the whole training process, which makes the graph topology structure more reliable. Extensive experiments on standard VI-ReID benchmarks demonstrate that CIFT outperforms the state-of-the-art methods under various settings."
DAS: Densely-Anchored Sampling for Deep Metric Learning
Authors: Lizhao Liu; Shangxin Huang; Zhuangwei Zhuang; Ran Yang; Mingkui Tan; Yaowei Wang
Venue: ECCV 2022 (main)

Abstract: "Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such as image retrieval and face recognition. However, the performance of DML methods often highly depends on sampling methods to choose effective data from the embedding space in the training. In practice, the embeddings in the embedding space are obtained by some deep models, where the embedding space is often with barren area due to the absence of training points, resulting in so called “missing embedding” issue. This issue may impair the sample quality, which leads to degenerated DML performance. In this work, we investigate how to alleviate the “missing embedding” issue to improve the sampling quality and achieve effective DML. To this end, we propose a Densely-Anchored Sampling (DAS) scheme that considers the embedding with corresponding data point as “anchor” and exploits the anchor’s nearby embedding space to densely produce embeddings without data points. Specifically, we propose to exploit the embedding space around single anchor with Discriminative Feature Scaling (DFS) and multiple anchors with Memorized Transformation Shifting (MTS). In this way, by combing the embeddings with and without data points, we are able to provide more embeddings to facilitate the sampling process thus boosting the performance of DML. Our method is effortlessly integrated into existing DML frameworks and improves them without bells and whistles. Extensive experiments on three benchmark datasets demonstrate the superiority of our method."
Learn from All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
Authors: Yuhang Zhang; Chengrui Wang; Xu Ling; Weihong Deng
Venue: ECCV 2022 (main)

Abstract: "Noisy label Facial Expression Recognition (FER) is more challenging than traditional noisy label classification tasks due to the inter-class similarity and the annotation ambiguity. Recent works mainly tackle this problem by filtering out large-loss samples. In this paper, we explore dealing with noisy labels from a new feature-learning perspective. We find that FER models remember noisy samples by focusing on a part of the features that can be considered related to the noisy labels instead of learning from the whole features that lead to the latent truth. Inspired by that, we propose a novel Erasing Attention Consistency (EAC) method to suppress the noisy samples during the training process automatically. Specifically, we first utilize the flip semantic consistency of facial images to design an imbalanced framework. We then randomly erase input images and use flip attention consistency to prevent the model from focusing on a part of the features. EAC significantly outperforms state-of-the-art noisy label FER methods and generalizes well to other tasks with a large number of classes like CIFAR100 and Tiny-ImageNet. The code is available at https://github.com/zyh-uaiaaaa/Erasing-Attention-Consistency."
A Non-Isotropic Probabilistic Take On Proxy-Based Deep Metric Learning
Authors: Michael Kirchhof; Karsten Roth; Zeynep Akata; Enkelejda Kasneci
Venue: ECCV 2022 (main)

Abstract: "Proxy-based Deep Metric Learning (DML) learns deep metric spaces by embedding images and class representatives (proxies) close to one another during training, as commonly measured by the angle between them. However, this disregards the embedding norm, which can carry additional beneficial context such as class- or image-intrinsic uncertainty. In addition, proxy-based DML struggles to learn class-internal structures. To address both issues at once, we introduce non-isotropic probabilistic proxy-based DML. We model images as directional von Mises-Fisher (vMF) distributions on the hypersphere and motivate a non-isotropic von Mises-Fisher (nivMF) model for class proxies. This allows proxies to better represent complex class-specific variances in the embedding space. To cast these probabilistic models into proxy-to-image metrics, we further develop and investigate multiple distribution-to-point and distribution-to-distribution metrics. Each framework choice is motivated through a set of ablational studies, which showcase beneficial properties of our probabilistic approach to proxy-based DML. This comprises uncertainty-awareness, better behaved gradients during training and overall improved generalization performance. The latter is especially reflected in the competitive performance on the standard DML benchmarks. We find our proposed approach to compare favourably, suggesting that existing proxy-based DML can significantly benefit from a more probabilistic treatment."
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers
Authors: Jihao Liu; Boxiao Liu; Hang Zhou; Hongsheng Li; Yu Liu
Venue: ECCV 2022 (main)

Abstract: "CutMix is a popular augmentation technique commonly used for training modern convolutional and transformer vision networks. It was originally designed to encourage Convolution Neural Networks (CNNs) to focus more on an image’s global context instead of local information, which greatly improves the performance of CNNs. However, we found it to have limited benefits for transformer-based architectures that naturally have a global receptive field. In this paper, we propose a novel data augmentation technique TokenMix to improve the performance of vision transformers. TokenMix mixes two images at token level via partitioning the mixing region into multiple separated parts. Besides, we show that the mixed learning target in CutMix, a linear combination of a pair of the ground truth labels, might be inaccurate and sometimes counter-intuitive. To obtain a more suitable target, we propose to assign the target score according to the content-based neural activation maps of the two images from a pre-trained teacher model, which does not need to have high performance. With plenty of experiments on various vision transformer architectures, we show that our proposed TokenMix helps vision transformers focus on the foreground area to infer the classes and enhances their robustness to occlusion, with consistent performance gains. Notably, we improve DeiT-T/S/B with +1% ImageNet top-1 accuracy. Besides, TokenMix enjoys longer training, which achieves 81.2% top-1 accuracy on ImageNet with DeiT-S trained for 400 epochs."
UFO: Unified Feature Optimization
Authors: Teng Xi; Yifan Sun; Deli Yu; Bi Li; Nan Peng; Gang Zhang; Xinyu Zhang; Zhigang Wang; Jinwen Chen; Jian Wang; Lufei Liu; Haocheng Feng; Junyu Han; Jingtuo Liu; Errui Ding; Jingdong Wang
Venue: ECCV 2022 (main)

Abstract: "This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collection of multiple AI functions. UFO aims to benefit each single task with a large-scale pretraining on all tasks. Compared with existing foundation models, UFO has two different points of emphasis, i.e., relatively smaller model size and NO adaptation cost: 1) UFO squeezes a wide range of tasks into a moderate-sized unified model in a multi-task learning manner and further trims the model size when transferred to down-stream tasks. 2) UFO does not emphasize transfer to novel tasks. Instead, it aims to make the trimmed model dedicated for one or more already-seen task. To this end, it directly selects partial modules in the unified model, and thus requires completely NO adaptation cost. With these two characteristics, UFO provides great convenience for flexible deployment, while maintaining the benefits of large-scale pretraining. A key merit of UFO is that the trimming process not only reduces the model size and inference consumption, but also even improves the accuracy on certain tasks. Specifically, UFO considers the multi-task training and brings two-fold impact on the unified model: some closely related tasks have mutual benefits, while some tasks have conflicts against each other. UFO manages to reduce the conflicts and to preserve the mutual benefits through a novel Network Architecture Search (NAS) method. Experiments on a wide range of deep representation learning tasks (i.e., face recognition, person re-identification, vehicle re-identification and product retrieval) show that the model trimmed from UFO achieves higher accuracy than its single-task-trained counterpart and yet has smaller model size, validating the concept of UFO. Besides, UFO also supported the release of 17 billion parameters computer vision (CV) foundation model which is the largest CV model in the industry."
Sound Localization by Self-Supervised Time Delay Estimation
Authors: Ziyang Chen; David F. Fouhey; Andrew Owens
Venue: ECCV 2022 (main)

Abstract: Sounds reach one microphone in a stereo pair sooner than the other, resulting in an interaural time delay that conveys their directions. Estimating a sound’s time delay requires finding correspondences between the signals recorded by each microphone. We propose to learn these correspondences through self-supervision, drawing on recent techniques from visual tracking. We adapt the contrastive random walk of Jabri et al. to learn a cycle-consistent representation from unlabeled stereo sounds, resulting in a model that performs on par with supervised methods on "in the wild" internet recordings. We also propose a multimodal contrastive learning model that solves a visually-guided localization task: estimating the time delay for a particular person in a multi-speaker mixture, given a visual representation of their face.
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation
Authors: Yinan He; Gengshi Huang; Siyu Chen; Jianing Teng; Kun Wang; Zhenfei Yin; Lu Sheng; Ziwei Liu; Yu Qiao; Jing Shao
Venue: ECCV 2022 (main)

Abstract: "In computer vision, pre-training models based on large-scale supervised learning have been proven effective over the past few years. However, existing works mostly focus on learning from the individual tasks with the single data source e.g., ImageNet for classification or COCO for detection). This restricted form limits their generalizability and usability due to the lack of vast semantic information from various tasks and data sources. Here, we demonstrate that jointly learning from heterogeneous tasks and multiple data sources contributes to universal visual representation, leading to better transferring results of various downstream tasks. Thus, learning how to bridge the gaps among different tasks and data sources is the key, but it still remains an open question. In this work, we propose a representation learning framework called X-Learner, which learns the universal feature of multiple vision tasks supervised by various sources, with expansion and squeeze stage:1) Expansion Stage: X-Learner learns the task-specific feature to alleviate task interference and enrich the representation by reconciliation layer. 2) Squeeze Stage: X-Learner condenses the model to a reasonable size and learns the universal and generalizable representation for various tasks transferring. Extensive experiments demonstrate that X-Learner achieves strong performance on different tasks without extra annotations, modalities and computational costs compared to existing representation learning methods. Notably, a single X-Learner model shows remarkable gains of 3.0%, 3.3% and 1.8% over current pre-trained models on 12 downstream datasets for classification, object detection and semantic segmentation."
SLIP: Self-Supervision Meets Language-Image Pre-training
Authors: Norman Mu; Alexander Kirillov; David Wagner; Saining Xie
Venue: ECCV 2022 (main)

Abstract: "Recent work has shown that self-supervised pre-training leads to improvements over supervised learning on challenging visual recognition tasks. CLIP, an exciting new approach to learning with language supervision, demonstrates promising performance on a wide variety of benchmarks. In this work, we explore whether self-supervised learning can aid in the use of language supervision for visual representation learning with Vision Transformers. We introduce SLIP, a multi-task learning framework for combining self-supervised learning and CLIP pre-training. After pre-training, we thoroughly evaluate representation quality and compare performance to both CLIP and self-supervised learning under three distinct settings: zero-shot transfer, linear classification, and end-to-end finetuning. Across ImageNet and a battery of additional datasets, we find that SLIP improves accuracy by a large margin. We validate our results further with experiments on different model sizes, training schedules, and pre-training datasets. Our findings show that SLIP enjoys the best of both worlds: better performance than self-supervision (+8.1% linear accuracy) and language supervision (+5.2% zero-shot accuracy). Our code is available at: github.com/facebookresearch/SLIP."
Discovering Deformable Keypoint Pyramids
Authors: Jianing Qian; Anastasios Panagopoulos; Dinesh Jayaraman
Venue: ECCV 2022 (main)

Abstract: "The locations of objects and their associated landmark keypoints can serve as versatile and semantically meaningful image representations. In natural scenes, these keypoints are often hierarchically grouped into sets corresponding to coherently moving objects and their moveable and deformable parts. Motivated by this observation, we propose Keypoint Pyramids, an approach to exploit this property for discovering keypoints without explicit supervision. Keypoint Pyramids discovers multi-level keypoint hierarchies satisfying three desiderata: comprehensiveness of the overall keypoint representation, coarse-to-fine informativeness of individual hierarchy levels, and parent-child associations of keypoints across levels. On human pose and tabletop multi-object scenes, our experimental results show that Keypoint Pyramids jointly discovers object keypoints and their natural hierarchical groupings, with finer levels adding detail to coarser levels to more comprehensively represent the visual scene. Further, we show qualitatively and quantitatively that keypoints discovered by Keypoint Pyramids using its hierarchical prior bind more consistently, and are more predictive of manually annotated semantic keypoints, compared to prior flat keypoint discovery approaches."
Neural Video Compression Using GANs for Detail Synthesis and Propagation
Authors: Fabian Mentzer; Eirikur Agustsson; Johannes Ballé; David Minnen; Nick Johnston; George Toderici
Venue: ECCV 2022 (main)

Abstract: "We present the first neural video compression method based on generative adversarial networks (GANs). Our approach significantly outperforms previous neural and non-neural video compression methods in a user study, setting a new state-of-the-art in visual quality for neural methods. We show that the GAN loss is crucial to obtain this high visual quality. Two components make the GAN loss effective: we i) synthesize detail by conditioning the generator on a latent extracted from the warped previous reconstruction to then ii) propagate this detail with high-quality flow. We find that user studies are required to compare methods, i.e., none of our quantitative metrics were able to predict all studies. We present the network design choices in detail, and ablate them with user studies."
A Contrastive Objective for Learning Disentangled Representations
Authors: Jonathan Kahana; Yedid Hoshen
Venue: ECCV 2022 (main)

Abstract: "Learning representations of images that are invariant to sensitive or unwanted attributes is important for many tasks including bias removal and cross domain retrieval. Here, our objective is to learn representations that are invariant to the domain (sensitive attribute) for which labels are provided, while being informative over all other image attributes, which are unlabeled. We present a new approach, proposing a new domain-wise contrastive objective for ensuring invariant representations. This objective crucially restricts negative image pairs to be drawn from the same domain, which enforces domain invariance whereas the standard contrastive objective does not. This domain-wise objective is insufficient on its own as it suffers from shortcut solutions resulting in feature suppression. We overcome this issue by a combination of a reconstruction constraint, image augmentations and initialization with pre-trained weights. Our analysis shows that the choice of augmentations is important, and that a misguided choice of augmentations can harm the invariance and informativeness objectives. In an extensive evaluation, our method convincingly outperforms the state-of-the-art in terms of representation invariance, representation informativeness, and training speed. Furthermore, we find that in some cases our method can achieve excellent results even without the reconstruction constraint, leading to a much faster and resource efficient training."