Being able to perceive the semantics and the spatial structure of the environment is essential for visual navigation of a household robot. However, most existing works only employ visual backbones pre-trained either with independent images for classification or with self-supervised learning methods to adapt to the indoor navigation domain, both neglecting the spatial relationships that are essential to the learning of navigation. Inspired by the behavior that human naturally build semantically and spatially meaningful cognitive maps in their brain during navigation, in this paper, we propose a novel navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps (Ego^2-Map). We apply the visual transformer as the backbone encoder and train the model with data collected from the large-scale Habitat-Matterport3D environments. Ego^2-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation. Experiments show that agents using our learned representations on object-goal navigation outperforms recent visual pre-training methods. Moreover, our representations lead to a significant improvement in vision-and-language navigation in continuous environments for both high-level and low-level action spaces, achieving new state-of-the-art results of 47% SR and 41% SPL on the test server.
论文检索
输入标题、作者或关键词，从 801 篇学术成果中精准定位
Domain adaptation (DA) aims to transfer knowledge from a label-rich source domain to a related but label-scarce target domain. Recently, increasing research has focused on exploring data structure of the target domain. In light of the recent success of Instance Discrimination Contrastive (IDCo) loss in self-supervised learning, we try directly applying it to domain adaptation tasks. However, the improvement is very limited, which motivates us to rethink its underlying limitations for domain adaptation tasks. An intuitive limitation is that a pair of samples belonging to the same class could be treated as negatives. Here we argue that using low-confidence samples to construct positive and negative pairs can alleviate this issue and is more suitable for IDCo loss. Another limitation is that IDCo loss cannot capture enough semantic information. We address this by introducing domain-invariant and accurate semantic information from classifier weights and input data. Specifically, we propose a class relationship enhanced features. It uses probability weighted class prototpyes as the input features of IDCo loss, which can implicitly transfer the domain-invariant class relationship. We further propose a target-dominated cross-domain mixup that can incorporate accurate semantic information from the source domain. We evaluate the proposed method in unsupervised DA and other DA settings, and extensive experimental results reveal that our method can make IDCo loss more effective and achieve state-of-the-art performance.
Recent vision transformers, large-kernel CNNs and MLPs have attained remarkable successes in broad vision tasks thanks to their effective information fusion in the global scope. However, their efficient deployments, especially on mobile devices, still suffer from noteworthy challenges due to the heavy computational costs of self-attention mechanisms, large kernels, or fully connected layers. In this work, we apply conventional convolution theorem to deep learning for addressing this and reveal that adaptive frequency filters can serve as efficient global token mixer. With this insight, we propose Adaptive Frequency Filtering (AFF) token mixer. This neural operator transfers a latent representation to the frequency domain via a Fourier transform and performs semantic-adaptive frequency filtering via an elementwise multiplication, which mathematically equals to a token mixing operation in the original latent space with a dynamic convolution kernel as large as the spatial resolution of this latent representation. We take AFF token mixers as primary neural operators to build a lightweight neural network, dubbed AFFNet. Extensive experiments demonstrate the effectiveness of our proposed AFF token mixer and show that AFFNet achieve superior accuracy and efficiency trade-offs compared to other lightweight network designs on broad visual tasks, including visual recognition and dense prediction tasks.
Several recent works have directly extended the image masked autoencoder (MAE) with random masking into video domain, achieving promising results. However, unlike images, both spatial and temporal information are important for video understanding. This suggests that the random masking strategy that is inherited from the image MAE is less effective for video MAE. This motivates the design of a novel masking algorithm that can more efficiently make use of video saliency. Specifically, we propose a motion-guided masking algorithm (MGM) which leverages motion vectors to guide the position of each mask over time. Crucially, these motion-based correspondences can be directly obtained from information stored in the compressed format of the video, which makes our method efficient and scalable. On two challenging large-scale video benchmarks (Kinetics-400 and Something-Something V2), we equip video MAE with our MGM and achieve up to +1.3% improvement compared to previous state-of-the-art methods. Additionally, our MGM achieves equivalent performance to previous video MAE using up to 66% fewer training epochs. Lastly, we show that MGM generalizes better to downstream transfer learning and domain adaptation tasks on the UCF101, HMDB51, and Diving48 datasets, achieving up to +4.9% improvement compared to baseline methods.
Although existing few-shot learning works yield promising results for in-domain queries, they still suffer from weak cross-domain generalization. Limited support data requires effective knowledge transfer, but domain-shift makes this harder. Towards this emerging challenge, researchers improved adaptation by introducing task-specific parameters, which are directly optimized and estimated for each task. However, adding a fixed number of additional parameters fails to consider the diverse domain shifts between target tasks and the source domain, limiting efficacy. In this paper, we first observe the dependence of task-specific parameter configuration on the target task. Abundant task-specific parameters may over-fit, and insufficient task-specific parameters may result in under-adaptation -- but the optimal task-specific configuration varies for different test tasks. Based on these findings, we propose the Task-aware Adaptive Network (TA2-Net), which is trained by reinforcement learning to adaptively estimate the optimal task-specific parameter configuration for each test task. It learns, for example, that tasks with significant domain shift usually have a larger need for task-specific parameters for adaptation. We evaluate our model on Meta-dataset. Empirical results show that our model outperforms existing state-of-the-art methods.
Dec-Adapter: Exploring Efficient Decoder-Side Adapter for Bridging Screen Content and Natural Image Compression
PDF ↗Natural image compression has been greatly improved in the deep learning era. However, the compression performance will be heavily degraded if the pretrained encoder is directly applied on screen content image compression. Meanwhile, we observe that parameter-efficient trans-fer learning (PETL) methods have shown great adaptation ability in high-level vision tasks. Therefore, we propose a Dec-Adapter, a pioneering entropy-efficient transfer learning module for the decoder to bridge natural image and screen content compression. The adapter's parameters are learned during encoding and transmitted to the decoder for image-adaptive decoding. Our Dec-Adapter is lightweight, domain-transferable, and architecture-agnostic with generalized performance in bridging the two domains. Experiments demonstrate that our method outperforms all existing methods by a large margin in terms of BD-rate performance on screen content image compression. Specifically, our method achieves over 2 dB gain compared with the baseline when transferred to screen content image com-pression.
The task of few-shot GAN adaptation aims to adapt a pre-trained GAN model to a small dataset with very few training images. While existing methods perform well when the dataset for pre-training is structurally similar to the target dataset, the approaches suffer from training instabilities or memorization issues when the objects in the two domains have a very different structure. To mitigate this limitation, we propose a new smoothness similarity regularization that transfers the inherently learned smoothness of the pre-trained GAN to the few-shot target domain even if the two domains are very different. We evaluate our approach by adapting an unconditional and a class-conditional GAN to diverse few-shot target domains. Our proposed method significantly outperforms prior few-shot GAN adaptation methods in the challenging case of structurally dissimilar source-target domains, while performing on par with the state of the art for similar source-target domains.
The vision community is undergoing the unprecedented progress with the emergence of Vision-Language Pretraining Models (VLMs). Prompt learning plays as the holy grail of accessing VLMs since it enables their fast adaptation to downstream tasks with limited resources. Whereas existing research milling around single-prompt paradigms, rarely investigate the technical potential behind their multi-prompt learning counterparts. This paper aims to provide a principled retrospect for vision-language multi-prompt learning. We extend the recent constant modality gap phenomenon to learnable prompts and then, justify the superiority of vision-language transfer with multi-prompt augmentation, empirically and theoretically. In terms of this observation, we propose an Energy-based Multi-prompt Learning (EMPL) to generate multiple prompt embeddings by drawing instances from an energy-based distribution, which is implicitly defined by VLMs. So our EMPL is not only parameter-efficient but also rigorously lead to the balance between in-domain and out-of-domain open-vocabulary generalization. Comprehensive experiments have been conducted to justify our claims and the excellence of EMPL.
Face anti-spoofing (FAS) or presentation attack detection is an essential component of face recognition systems deployed in security-critical applications. Existing FAS methods have poor generalizability to unseen spoof types, camera sensors, and environmental conditions. Recently, vision transformer (ViT) models have been shown to be effective for the FAS task due to their ability to capture long-range dependencies among image patches. However, adaptive modules or auxiliary loss functions are often required to adapt pre-trained ViT weights learned on large-scale datasets such as ImageNet. In this work, we first show that initializing ViTs with multimodal (e.g., CLIP) pre-trained weights improves generalizability for the FAS task, which is in line with the zero-shot transfer capabilities of vision-language pre-trained (VLP) models. We then propose a novel approach for robust cross-domain FAS by grounding visual representations with the help of natural language. Specifically, we show that aligning the image representation with an ensemble of class descriptions (based on natural language semantics) improves FAS generalizability in low-data regimes. Finally, we propose a multimodal contrastive learning strategy to boost feature generalization further and bridge the gap between source and target domains. Extensive experiments on three standard protocols demonstrate that our method significantly outperforms the state-of-the-art methods, achieving better zero-shot transfer performance than five-shot transfer of "adaptive ViTs". Code: https://github.com/koushiksrivats/FLIP
Domain adaptation (DA) aims to transfer knowledge from a fully labeled source to a scarcely labeled or totally unlabeled target under domain shift. Recently, semi-supervised learning-based (SSL) techniques that leverage pseudo labeling have been increasingly used in DA. Despite the competitive performance, these pseudo labeling methods rely heavily on the source domain to generate pseudo labels for the target domain and therefore still suffer considerably from source data bias. Moreover, class distribution bias in the target domain is also often ignored in the pseudo label generation and thus leading to further deterioration of performance. In this paper, we propose GeT that learns a non-bias target embedding distribution with high quality pseudo labels. Specifically, we formulate an online target generative classifier to induce the target distribution into distinctive Gaussian components weighted by their class priors to mitigate source data bias and enhance target class discriminability. We further propose a structure similarity regularization framework to alleviate target class distribution bias and further improve target class discriminability. Experimental results show that our proposed GeT is effective and achieves consistent improvements under various DA settings with and without class distribution bias. Our code is available at: https://lulusindazc.github.io/getproject/.
Continual learning (CL) can help pre-trained vision-language models efficiently adapt to new or under-trained data distributions without re-training. Nevertheless, during the continual training of the Contrastive Language-Image Pre-training (CLIP) model, we observe that the model's zero-shot transfer ability significantly degrades due to catastrophic forgetting. Existing CL methods can mitigate forgetting by replaying previous data. However, since the CLIP dataset is private, replay methods cannot access the pre-training dataset. In addition, replaying data of previously learned downstream tasks can enhance their performance but comes at the cost of sacrificing zero-shot performance. To address this challenge, we propose a novel method ZSCL to prevent zero-shot transfer degradation in the continual learning of vision-language models in both feature and parameter space. In the feature space, a reference dataset is introduced for distillation between the current and initial models. The reference dataset should have semantic diversity but no need to be labeled, seen in pre-training, or matched image-text pairs. In parameter space, we prevent a large parameter shift by averaging weights during the training. We propose a more challenging Multi-domain Task Incremental Learning (MTIL) benchmark to evaluate different methods, where tasks are from various domains instead of class-separated in a single dataset. Our method outperforms other methods in the traditional class-incremental learning setting and the MTIL by 9.7% average score. Our code locates at https://github.com/Thunderbeee/ZSCL.
Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment
PDF ↗Face Image Quality Assessment (FIQA) lays the foundation for ensuring the stability and accuracy of face recognition systems. However, existing FIQA methods mainly formulate quality relationships within the training set to yield quality scores, ignoring the generalization problem caused by ethnic quality bias between the training and test sets. Domain adaptation presents a potential solution to mitigate the bias, but if FIQA is treated essentially as a regression task, it will be limited by the challenge of feature scaling in transfer learning. Additionally, how to guarantee source risk is also an issue due to the lack of ground-truth labels of the source domain for FIQA. This paper presents the first attempt in the field of FIQA to address these challenges with a novel Ethnic-Quality-Bias Mitigating (EQBM) framework. Specifically, to eliminate the restriction of scalar regression, we first compute the Likert-scale quality probability distributions as source domain annotations. Furthermore, we design an easy-to-hard training scheduler based on the inter-domain uncertainty and intra-domain quality margin as well as the ranking-based domain adversarial network to enhance the effectiveness of transfer learning and further reduce the source risk in domain adaptation. Extensive experiments demonstrate that the EQBM significantly mitigates the quality bias and improves the generalization capability of FIQA across races on different datasets.
This work aims to transfer a Generative Adversarial Network (GAN) pre-trained on one image domain to another domain referred to as few as just one reference image. The challenge is that, under limited supervision, it is extremely difficult to synthesize photo realistic and highly diverse images while retaining the representative characters of the target domain. Different from existing approaches that adopt the vanilla fine-tuning strategy, we design two lightweight modules in the generator and the discriminator respectively. We first introduce an attribute adaptor in the generator and freeze the generator's original parameters, which can reuse the prior knowledge to the most extent and maintain the synthesis quality and diversity. We then equip the well-learned discriminator with an attribute classifier to ensure that the generator with the attribute adaptor captures the appropriate characters of the reference image. Furthermore, considering the very limited diversity of the training data (i.e., as few as only one image), we propose to constrain the diversity of the latent space through truncation in the training process, alleviating the optimization difficulty. Our approach brings appealing results under various settings, substantially surpassing state-of-the-art alternatives, especially in terms of synthesis diversity. Noticeably, our method works well even with large domain gaps and robustly converges within a few minutes for each experiment. Code and models are available at https://genforce.github.io/genda/.
Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language (V-L) pre-training methods to 3D vision. However, the domain gap between 3D and images is unsolved, so that V-L pre-trained models are restricted in 3D downstream tasks. To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification. We introduce a new depth rendering setting that forms a better visual effect, and then render 52,460 pairs of images and depth maps from ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines cross-modality learning to enforce the depth features for capturing expressive visual and textual features and intra-modality learning to enhance the invariance of depth aggregation. Additionally, we propose a novel Gated Dual-Path Adapter (GDPA), i.e., a dual-path structure with global-view aggregators and gated fusion for downstream representative learning. It allows the ensemble of CLIP and CLIP2Point, tuning pre-training knowledge to downstream tasks in an efficient adaptation. Experimental results show that CLIP2Point is effective in transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms other 3D transfer learning and pre-training networks, achieving state-of-the-art results on zero-shot, few-shot, and fully-supervised classification.
Gait recognition has emerged as a promising technique for the long-range retrieval of pedestrians, providing numerous advantages such as accurate identification in challenging conditions and non-intrusiveness, making it highly desirable for improving public safety and security. However, the high cost of labeling datasets, which is a prerequisite for most existing fully supervised approaches, poses a significant obstacle to the development of gait recognition. Recently, some unsupervised methods for gait recognition have shown promising results. However, these methods mainly rely on a fine-tuning approach that does not sufficiently consider the relationship between source and target domains, leading to the catastrophic forgetting of source domain knowledge. This paper presents a novel perspective that adjacent-view sequences exhibit overlapping views, which can be leveraged by the network to gradually attain cross-view and cross-dressing capabilities without pre-training on the labeled source domain. Specifically, we propose a fine-grained Unsupervised Domain Adaptation (UDA) framework that iteratively alternates between two stages. The initial stage involves offline clustering, which transfers knowledge from the labeled source domain to the unlabeled target domain and adaptively generates pseudo-labels according to the expressiveness of each part. Subsequently, the second stage encompasses online training, which further achieves cross-dressing capabilities by continuously learning to distinguish numerous features of source and target domains. The effectiveness of the proposed method is demonstrated through extensive experiments conducted on widely-used public gait datasets.
The prolific performances of Vision Transformers (ViTs) in image tasks have prompted research into adapting the image ViTs for video tasks. However, the substantial gap between image and video impedes the spatiotemporal learning of these image-pretrained models. Though video-specialized models like UniFormer can transfer to the video domain more seamlessly, their unique architectures require prolonged image pretraining, limiting the scalability. Given the emergence of powerful open-source image ViTs, we propose unlocking their potential for video understanding with efficient UniFormer designs. We call the resulting model UniFormerV2, since it inherits the concise style of the UniFormer block, while redesigning local and global relation aggregators that seamlessly integrate advantages from both ViTs and UniFormer. Our UniFormerV2 achieves state-of-the-art performances on 8 popular video benchmarks, including scene-related Kinetics-400/600/700, heterogeneous Moments in Time, temporal-related Something-Something V1/V2, and untrimmed ActivityNet and HACS. It is noteworthy that to the best of our knowledge, UniFormerV2 is the first to elicit 90% top-1 accuracy on Kinetics-400.
Although Domain Generalization (DG) problem has been fast-growing in the 2D image tasks, its exploration on 3D point cloud data is still insufficient and challenged by more complex and uncertain cross-domain variances with uneven inter-class modality distribution. In this paper, different from previous 2D DG works, we focus on the 3D DG problem and propose a Single-dataset Unified Generalization (SUG) framework that only leverages a single source dataset to alleviate the unforeseen domain differences faced by a well-trained source model. Specifically, we first design a Multi-grained Sub-domain Alignment (MSA) method, which can constrain the learned representations to be domain-agnostic and discriminative, by performing a multi-grained feature alignment process between the splitted sub-domains from the single source dataset. Then, a Sample-level Domain-aware Attention (SDA) strategy is presented, which can selectively enhance easy-to-adapt samples from different sub-domains according to the sample-level inter-domain distance to avoid the negative transfer. Experiments demonstrate that our SUG can boost the generalization ability for unseen target domains, even outperforming the existing unsupervised domain adaptation methods that have to access extensive target domain data.
Source-Free domain adaptive Semantic Segmentation (SFSS) aims to transfer knowledge from source domain to the target domain with only pre-trained source segmentation model and the unlabeled target dataset. Only a few works have been researched for SFSS, relying on entropy minimization, pseudo-labeling. Nevertheless, due to the domain bias, these methods tend to suffering from the confusion of classes with a similar visual appearance in different domains. To address the above issue, we propose to enhance discriminability towards target samples with masked image modeling to model spatial context relations as additional recognition clues. Specifically, we design a novel Dual-Level Masked Consistency method, which explicitly encourages the model to learn comprehensive context relations, i.e. patch-wise context and channel-wise context, on the target domain. By randomly masking target images and forcing the model to reconstruct predictions of the entire image with left unmasked part, the model has to make full use of spatially contextual information. To take a step further, we propose a novel masking strategy considering both local context and global context information by applying patch-wise masking on image patches and channel-wise masking on latent features. Notably, patch-wise context learning and channel-wise context learning can complement each other. Extensive experiments demonstrate the effectiveness of our proposed method and our method achieves state-of-the-art performance on two synthetic-to-real benchmarks: GTA5→Cityscapes and SYNTHIA→Cityscapes.
Compared to single-modality magnetic resonance (MR) image super-resolution (SR) methods, multi-modality MR image methods can utilize high-resolution reference modality (e.g., T1 modality) to provide valuable complementary information for low-resolution target modality (e.g., T2 modality) in SR reconstruction, which can further improve the quality of the SR images. Although they have achieved impressive results, these methods still suffer from the following drawbacks: (1) They can only handle fixed integer upsampling factors, such as 2X, 3X, and 4X, and require training and storing corresponding models for each upsampling factor, which is infeasible in clinical practice; (2) They only perform feature extraction and reconstruction in the image domain. However, the aliasing artifacts produced in the image domain are structural and non-local. Therefore, using only the image domain cannot effectively reconstruct high-quality aliasing-free SR images. To address these issues, we develop a brand-new Dual-Domain Implicit Network (DuDoINet) for multi-modality MR image arbitrary-scale SR. Specifically, we propose a dual-domain learning scheme for multi-modality MR image SR, which allows the network to sufficiently exploit the frequency and image domain information in MR images. In addition, we design implicit attention to achieve arbitrary-scale upsampling of MR images, which utilizes a continuously differentiable function that generates pixel values from pixel coordinates. Furthermore, we designed a deformable cross-modality attention mechanism that can adaptively transfer high-frequency details from the T1 to the T2 modality, better integrating valuable complementary information from the T1 modality. Extensive and comprehensive experiments on healthy subjects and patient datasets demonstrate that our DuDoINet outperforms SOTA methods, demonstrating its great potential for clinical practice.
Nighttime tracking is challenging due to the lack of sufficient training data and scene diversity. Unsupervised domain adaptation is a solution by transferring knowledge from day (source domain) to night (target domain). It typically involves adversarial training with a domain discriminator on the source and target data to learn domain-invariant features. However, the imbalanced source/target distribution can cause overfitting of the domain discriminator, hindering the domain adaptability. To address this issue, we propose a Progressive Domain-Style Translation (PDST) for domain adaptive nighttime tracking. PDST decomposes and recombines domain-invariant content encodings and domain-specific style encodings of different domains. Thus the rich source domain content is translated to the target domain, expanding the inter-class diversity of the target domain to alleviate overfitting. Moreover, a momentum update manner is introduced to progressively estimate the domain-style encoding from multiple features, which more accurately reflects the statistical domain attribute than an individual image-style. Finally, we incorporate two regularization terms to constrain the content and domain-style consistency in the translation process, ensuring the generated source-like target features are valid to facilitate the training of domain adaptation. Exhaustive experiments demonstrate the domain adaptability and SOTA performance of the proposed method in nighttime tracking.