Class-incremental learning (CIL) aims to enable models to continuously learn new classes while overcoming catastrophic forgetting. The introduction of pre-trained models has brought new tuning paradigms to CIL. In this paper we revisit different parameter-efficient tuning (PET) methods within the context of continual learning. We observe that adapter tuning demonstrates superiority over prompt-based methods even without parameter expansion in each learning session. Motivated by this we propose incrementally tuning the shared adapter without imposing parameter update constraints enhancing the learning capacity of the backbone. Additionally we employ feature sampling from stored prototypes to retrain a unified classifier further improving its performance. We estimate the semantic shift of old prototypes without access to past samples and update stored prototypes session by session. Our proposed method eliminates model expansion and avoids retaining any image samples. It surpasses previous pre-trained model-based CIL methods and demonstrates remarkable continual learning capabilities. Experimental results on five CIL benchmarks validate the effectiveness of our approach achieving state-of-the-art (SOTA) performance.
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SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting
PDF ↗We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework. Drawing on the Cook-Torrance reflectance model we have meticulously configured the architecture design to precisely simulate light-surface interactions. Furthermore to overcome the limitation of scarce high-quality lightstage data we have developed a self-supervised pre-training strategy. This novel combination of accurate physical modeling and expanded training dataset establishes a new benchmark in relighting realism.
Text-to-image diffusion models have remarkably excelled in producing diverse high-quality and photo-realistic images. This advancement has spurred a growing interest in incorporating specific identities into generated content. Most current methods employ an inversion approach to embed a target visual concept into the text embedding space using a single reference image. However the newly synthesized faces either closely resemble the reference image in terms of facial attributes such as expression or exhibit a reduced capacity for identity preservation. Text descriptions intended to guide the facial attributes of the synthesized face may fall short owing to the intricate entanglement of identity information with identity-irrelevant facial attributes derived from the reference image. To address these issues we present the novel use of the extended StyleGAN embedding space \mathcal W _+ to achieve enhanced identity preservation and disentanglement for diffusion models. By aligning this semantically meaningful human face latent space with text-to-image diffusion models we succeed in maintaining high fidelity in identity preservation coupled with the capacity for semantic editing. Additionally we propose new training objectives to balance the influences of both prompt and identity conditions ensuring that the identity-irrelevant background remains \lxm negligibly affected during facial attribute modifications. Extensive experiments reveal that our method adeptly generates personalized text-to-image outputs that are not only compatible with prompt descriptions but also amenable to common StyleGAN editing directions in diverse settings. Our code and model are available at https://github.com/csxmli2016/w-plus-adapter.
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
PDF ↗Current instruction-based image editing methods such as InstructPix2Pix often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text encoder in diffusion models. To rectify this this paper introduces SmartEdit a novel approach of instruction-based image editing that leverages Multimodal Large Language Models (MLLMs) to enhance its understanding and reasoning capabilities. However direct integration of these elements still faces challenges in situations requiring complex reasoning. To mitigate this we propose a Bidirectional Interaction Module (BIM) that enables comprehensive bidirectional information interactions between the input image and the MLLM output. During training we initially incorporate perception data to boost the perception and understanding capabilities of diffusion models. Subsequently we demonstrate that a small amount of complex instruction editing data can effectively stimulate SmartEdit's editing capabilities for more complex instructions. We further construct a new evaluation dataset Reason-Edit specifically tailored for complex instruction-based image editing. Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods paving the way for the practical application of complex instruction-based image editing.
Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
PDF ↗Diffusion models (DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high-quality and realistic data samples. However their widespread use has also brought forth new challenges in model security which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We propose CAAT a simple but generic and efficient approach that does not require costly training to effectively fool latent diffusion models (LDMs). The approach is based on the observation that cross-attention layers exhibits higher sensitivity to gradient change allowing for leveraging subtle perturbations on published images to significantly corrupt the generated images. We show that a subtle perturbation on an image can significantly impact the cross-attention layers thus changing the mapping between text and image during the fine-tuning of customized diffusion models. Extensive experiments demonstrate that CAAT is compatible with diverse diffusion models and outperforms baseline attack methods in a more effective (more noise) and efficient (twice as fast as Anti-DreamBooth and Mist) manner.
Real-world systems often encounter new data over time which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches to handle this. Here we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data. Our model uses a two-stream architecture to maintain knowledge about the source domain as well as complementary target-domain-specific information. The backpropagation-free property of our model helps address the well-known forgetting problem and mitigates the error accumulation issue. The proposed method also eliminates the need for the usually noisy process of pseudo-labeling and reliance on costly self-supervised training. Moreover our method leverages subspace learning effectively reducing the distribution variance between the two domains. Furthermore the source-domain-specific and the target-domain-specific streams are aligned using a novel entropy-based adaptive fusion strategy. Extensive experiments on popular benchmarks demonstrate the effectiveness of our method. The code will be available at https://github.com/abie-e/BFTT3D.
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline
PDF ↗nsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity lately due to its practical real-world applications. Due to the extremely challenging nature of this task where learning is carried out without any annotations privacy-critical collaborative learning of US-VAD systems has not been studied yet. As surveillance videos are privacy sensitive and the availability of large-scale video data may enable better US-VAD systems collaborative learning can be highly rewarding in this setting. In this paper we propose a new baseline for anomaly detection capable of localizing anomalous events in complex surveillance scenarios in a fully unsupervised fashion without any labels on a privacy-retaining participant-based distributed training configuration. Additionally we propose three new evaluation protocols to extensively evaluate anomaly detection approaches on various scenarios of collaborations and data availability. Moreover based on these protocols we modify existing VAD datasets to extensively evaluate our approach as well as existing US SOTA methods on two large-scale datasets including UCF-Crime and XD-Violence. All proposed evaluation protocols dataset splits and codes are available here: \href https://github.com/AnasEmad11/CLAP https://github.com/AnasEmad11/CLAP .
Diffusion models have made significant advances in generating high-quality images but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new ones. Nevertheless existing methods struggle to maintain strict temporal consistency and efficient memory consumption. In this work we propose a novel approach to enhance temporal consistency in generated videos by merging self-attention tokens across frames. By aligning and compressing temporally redundant tokens across frames our method improves temporal coherence and reduces memory consumption in self-attention computations. The merging strategy matches and aligns tokens according to the temporal correspondence between frames facilitating natural temporal consistency in generated video frames. To manage the complexity of video processing we divide videos into chunks and develop intra-chunk local token merging and inter-chunk global token merging ensuring both short-term video continuity and long-term content consistency. Our video editing approach seamlessly extends the advancements in image editing to video editing rendering favorable results in temporal consistency over state-of-the-art methods.
Multimodal learning has advanced the performance for many vision-language tasks. However most existing works in embodied dialog research focus on navigation and leave the localization task understudied. The few existing dialog-based localization approaches assume the availability of entire dialog prior to localizaiton which is impractical for deployed dialog-based localization. In this paper we propose DiaLoc a new dialog-based localization framework which aligns with a real human operator behavior. Specifically we produce an iterative refinement of location predictions which can visualize current pose believes after each dialog turn. DiaLoc effectively utilizes the multimodal data for multi-shot localization where a fusion encoder fuses vision and dialog information iteratively. We achieve state-of-the-art results on embodied dialog-based localization task in single-shot (+7.08% in Acc5@valUnseen) and multi-shot settings (+10.85% in Acc5@valUnseen). DiaLoc narrows the gap between simulation and real-world applications opening doors for future research on collaborative localization and navigation.
Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue involving the dynamic addition of new classes in the context of federated learning. In this field Data-Free Knowledge Transfer (DFKT) plays a crucial role in addressing catastrophic forgetting and data privacy problems. However prior approaches lack the crucial synergy between DFKT and the model training phases causing DFKT to encounter difficulties in generating high-quality data from a non-anchored latent space of the old task model. In this paper we introduce LANDER (Label Text Centered Data-Free Knowledge Transfer) to address this issue by utilizing label text embeddings (LTE) produced by pretrained language models. Specifically during the model training phase our approach treats LTE as anchor points and constrains the feature embeddings of corresponding training samples around them enriching the surrounding area with more meaningful information. In the DFKT phase by using these LTE anchors LANDER can synthesize more meaningful samples thereby effectively addressing the forgetting problem. Additionally instead of tightly constraining embeddings toward the anchor the Bounding Loss is introduced to encourage sample embeddings to remain flexible within a defined radius. This approach preserves the natural differences in sample embeddings and mitigates the embedding overlap caused by heterogeneous federated settings. Extensive experiments conducted on CIFAR100 Tiny-ImageNet and ImageNet demonstrate that LANDER significantly outperforms previous methods and achieves state-of-the-art performance in FCIL. The code is available at https://github.com/tmtuan1307/lander.
In this paper we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM known for its zero-shot generalizability exhibits a performance degradation when faced with datasets with varying image sizes. Previous approaches tend to resize the image to a fixed size or adopt structure modifications hindering the preservation of SAM's rich prior knowledge. Besides such task-specific tuning necessitates a complete retraining of the model which is cost-expensive and unacceptable for deployment in the downstream tasks. In this paper we reformulate this challenge as a length extrapolation problem where token sequence length varies while maintaining a consistent patch size for images with different sizes. To this end we propose a Scalable Bias-Mode Attention Mask (BA-SAM) to enhance SAM's adaptability to varying image resolutions while eliminating the need for structure modifications. Firstly we introduce a new scaling factor to ensure consistent magnitude in the attention layer's dot product values when the token sequence length changes. Secondly we present a bias-mode attention mask that allows each token to prioritize neighboring information mitigating the impact of untrained distant information. Our BA-SAM demonstrates efficacy in two scenarios: zero-shot and fine-tuning. Extensive evaluation of diverse datasets including DIS5K DUTS ISIC COD10K and COCO reveals its ability to significantly mitigate performance degradation in the zero-shot setting and achieve state-of-the-art performance with minimal fine-tuning. Furthermore we propose a generalized model and benchmark showcasing BA-SAM's generalizability across all four datasets simultaneously.
Despite many attempts to leverage pre-trained text-to-image models (T2I) like Stable Diffusion (SD) for controllable image editing producing good predictable results remains a challenge. Previous approaches have focused on either fine-tuning pre-trained T2I models on specific datasets to generate certain kinds of images (e.g. with a specific object or person) or on optimizing the weights text prompts and/or learning features for each input image in an attempt to coax the image generator to produce the desired result. However these approaches all have shortcomings and fail to produce good results in a predictable and controllable manner. To address this problem we present TiNO-Edit an SD-based method that focuses on optimizing the noise patterns and diffusion timesteps during editing something previously unexplored in the literature. With this simple change we are able to generate results that both better align with the original images and reflect the desired result. Furthermore we propose a set of new loss functions that operate in the latent domain of SD greatly speeding up the optimization when compared to prior losses which operate in the pixel domain. Our method can be easily applied to variations of SD including Textual Inversion and DreamBooth that encode new concepts and incorporate them into the edited results. We present a host of image-editing capabilities enabled by our approach. Our code is publicly available at https://github.com/SherryXTChen/TiNO-Edit.
ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting
PDF ↗Abstract In recent years text-image joint pre-training techniques have shown promising results in various tasks. However in Optical Character Recognition (OCR) tasks aligning text instances with their corresponding text regions in images poses a challenge as it requires effective alignment between text and OCR-Text (referring to the text in images as OCR-Text to distinguish from the text in natural language) rather than a holistic understanding of the overall image content. In this paper we propose a new pre-training method called OCR-Text Destylization Modeling (ODM) that transfers diverse styles of text found in images to a uniform style based on the text prompt. With ODM we achieve better alignment between text and OCR-Text and enable pre-trained models to adapt to the complex and diverse styles of scene text detection and spotting tasks. Additionally we have designed a new labeling generation method specifically for ODM and combined it with our proposed Text-Controller module to address the challenge of annotation costs in OCR tasks allowing a larger amount of unlabeled data to participate in pre-training. Extensive experiments on multiple public datasets demonstrate that our method significantly improves performance and outperforms current pre-training methods in scene text detection and spotting tasks. Code is available at https://github.com/PriNing/ODM.
We introduce FaceTalk a novel generative approach designed for synthesizing high-fidelity 3D motion sequences of talking human heads from input audio signal. To capture the expressive detailed nature of human heads including hair ears and finer-scale eye movements we propose to couple speech signal with the latent space of neural parametric head models to create high-fidelity temporally coherent motion sequences. We propose a new latent diffusion model for this task operating in the expression space of neural parametric head models to synthesize audio-driven realistic head sequences. In the absence of a dataset with corresponding NPHM expressions to audio we optimize for these correspondences to produce a dataset of temporally-optimized NPHM expressions fit to audio-video recordings of people talking. To the best of our knowledge this is the first work to propose a generative approach for realistic and high-quality motion synthesis of volumetric human heads representing a significant advancement in the field of audio-driven 3D animation. Notably our approach stands out in its ability to generate plausible motion sequences that can produce high-fidelity head animation coupled with the NPHM shape space. Our experimental results substantiate the effectiveness of FaceTalk consistently achieving superior and visually natural motion encompassing diverse facial expressions and styles outperforming existing methods by 75% in perceptual user study evaluation
Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications offering a unified space for sensor data fusion and supporting various downstream tasks. However conventional models use grids with fixed resolution and range and face computational inefficiencies due to the uniform allocation of resources across all cells. To address this we propose PointBeV a novel sparse BeV segmentation model operating on sparse BeV cells instead of dense grids. This approach offers precise control over memory usage enabling the use of long temporal contexts and accommodating memory-constrained platforms. PointBeV employs an efficient two-pass strategy for training enabling focused computation on regions of interest. At inference time it can be used with various memory/performance trade-offs and flexibly adjusts to new specific use cases. PointBeV achieves state-of-the-art results on the nuScenes dataset for vehicle pedestrian and lane segmentation showcasing superior performance in static and temporal settings despite being trained solely with sparse signals. We release our code with two new efficient modules used in the architecture: Sparse Feature Pulling designed for the effective extraction of features from images to BeV and Submanifold Attention which enables efficient temporal modeling. The code is available at https://github.com/valeoai/PointBeV.
In this paper we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively restoration is much easier on text modality than image one. For example it can be easily conducted by removing degradation-related words while keeping the content-aware words. Hence we combine the advantages of images in detail description and ones of text in degradation removal to perform restoration. To address the cross-modal assistance we propose to map the degraded images into textual representations for removing the degradations and then convert the restored textual representations into a guidance image for assisting image restoration. In particular We ingeniously embed an image-to-text mapper and text restoration module into CLIP-equipped text-to-image models to generate the guidance. Then we adopt a simple coarse-to-fine approach to dynamically inject multi-scale information from guidance to image restoration networks. Extensive experiments are conducted on various image restoration tasks including deblurring dehazing deraining and denoising and all-in-one image restoration. The results showcase that our method outperforms state-of-the-art ones across all these tasks. The codes and models are available at https://github.com/mrluin/TextualDegRemoval.
Semantic image synthesis i.e. generating images from user-provided semantic label maps is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation but the image quality tends to suffer when modeling large and diverse datasets. In this work we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbones pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables leading to more diverse generated images. Our model which we dub DP-SIMS achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K COCO-Stuff and Cityscapes surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.
Anomaly Score: Evaluating Generative Models and Individual Generated Images based on Complexity and Vulnerability
PDF ↗With the advancement of generative models the assessment of generated images becomes increasingly more important. Previous methods measure distances between features of reference and generated images from trained vision models. In this paper we conduct an extensive investigation into the relationship between the representation space and input space around generated images. We first propose two measures related to the presence of unnatural elements within images: complexity which indicates how non-linear the representation space is and vulnerability which is related to how easily the extracted feature changes by adversarial input changes. Based on these we introduce a new metric to evaluating image-generative models called anomaly score (AS). Moreover we propose AS-i (anomaly score for individual images) that can effectively evaluate generated images individually. Experimental results demonstrate the validity of the proposed approach.
Understanding how attention varies across individuals has significant scientific and societal impacts. However existing visual scanpath models treat attention uniformly neglecting individual differences. To bridge this gap this paper focuses on individualized scanpath prediction (ISP) a new attention modeling task that aims to accurately predict how different individuals shift their attention in diverse visual tasks. It proposes an ISP method featuring three novel technical components: (1) an observer encoder to characterize and integrate an observer's unique attention traits (2) an observer-centric feature integration approach that holistically combines visual features task guidance and observer-specific characteristics and (3) an adaptive fixation prioritization mechanism that refines scanpath predictions by dynamically prioritizing semantic feature maps based on individual observers' attention traits. These novel components allow scanpath models to effectively address the attention variations across different observers. Our method is generally applicable to different datasets model architectures and visual tasks offering a comprehensive tool for transforming general scanpath models into individualized ones. Comprehensive evaluations using value-based and ranking-based metrics verify the method's effectiveness and generalizability.
Recent progress in human shape learning shows that neural implicit models are effective in generating 3D human surfaces from limited number of views and even from a single RGB image. However existing monocular approaches still struggle to recover fine geometric details such as face hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy which improves the accuracy of the signed distance field estimation of points that lie on the reconstructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB surface normals point cloud or RGB-D data as input. In addition we introduce ANIM-Real a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera and our protocol to fine-tune ANIM enabling high-quality reconstruction from real-world human capture.