Vision-Language Models (VLMs) such as Flamingo and GPT-4V have shown immense potential by integrating large language models with vision systems. Nevertheless these models face challenges in the fundamental computer vision task of object localisation due to their training on multimodal data containing mostly captions without explicit spatial grounding. While it is possible to construct custom supervised training pipelines with bounding box annotations that integrate with VLMs these result in specialized and hard-to-scale models. In this paper we aim to explore the limits of caption-based VLMs and instead propose to tackle the challenge in a simpler manner by i) keeping the weights of a caption-based VLM frozen and ii) not using any supervised detection data. To this end we introduce an input-agnostic Positional Insert (PIN) a learnable spatial prompt containing a minimal set of parameters that are slid inside the frozen VLM unlocking object localisation capabilities. Our PIN module is trained with a simple next-token prediction task on synthetic data without requiring the introduction of new output heads. Our experiments demonstrate strong zero-shot localisation performances on a variety of images including Pascal VOC COCO LVIS and diverse images like paintings or cartoons.
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This paper presents a new approach for the detection of fake videos based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos. We discovered that the generated facial videos suffer from the temporal distinctiveness in the temporal changes of style latent vectors which are inevitable during the generation of temporally stable videos with various facial expressions and geometric transformations. Our framework utilizes the StyleGRU module trained by contrastive learning to represent the dynamic properties of style latent vectors. Additionally we introduce a style attention module that integrates StyleGRU-generated features with content-based features enabling the detection of visual and temporal artifacts. We demonstrate our approach across various benchmark scenarios in deepfake detection showing its superiority in cross-dataset and cross-manipulation scenarios. Through further analysis we also validate the importance of using temporal changes of style latent vectors to improve the generality of deepfake video detection.
Deep neural networks are vulnerable to adversarial attacks leading to erroneous outputs. Adversarial training has been recognized as one of the most effective methods to counter such attacks. However existing adversarial training techniques have predominantly been evaluated on balanced datasets whereas real-world data often exhibit a long-tailed distribution casting doubt on the efficacy of these methods in practical scenarios. In this paper we delve into the performance of adversarial training under long-tailed distributions. Through an analysis of the prior method "RoBal" (Wu et al. CVPR'21) we discover that utilizing Balanced Softmax Loss (BSL) alone can obtain comparable performance to the complete RoBal approach while significantly reducing the training overhead. Then we reveal that adversarial training under long-tailed distributions also suffers from robust overfitting similar to uniform distributions. We explore utilizing data augmentation to mitigate this issue and unexpectedly discover that unlike results obtained with balanced data data augmentation not only effectively alleviates robust overfitting but also significantly improves robustness. We further identify that the improvement is attributed to the increased diversity of training data. Extensive experiments further corroborate that data augmentation alone can significantly improve robustness. Finally building on these findings we demonstrate that compared to RoBal the combination of BSL and data augmentation leads to a +6.66% improvement in model robustness under AutoAttack on CIFAR-10-LT. Our code is available at: https://github.com/NISPLab/AT-BSL.
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition
PDF ↗Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context. Class representations need to be adapted to more accurately reflect an object concept under these shifts. In the absence of training data from target geographies we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness. For this purpose we explore the feasibility of probing a large language model for geography-based object knowledge and we examine the effects of integrating knowledge into zero-shot and learnable soft prompting with CLIP. Within this exploration we propose geography knowledge regularization to ensure that soft prompts trained on a source set of geographies generalize to an unseen target set. Accuracy gains over prompting baselines on DollarStreet while training only on Europe data are up to +2.8/1.2/1.6 on target data from Africa/Asia/Americas and +4.6 overall on the hardest classes. Competitive performance is shown vs. few-shot target training and analysis is provided to direct future study of geographical robustness.
In this paper we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end we implement two optimization strategies. The first prompt-free conditional learning utilizes a prompt-free encoder derived from a pre-trained Stable Diffusion model. This strategy is designed to adapt new conditions to the diffusion process by minimizing the textual-visual correlation thereby ensuring a more precise alignment between the generated content and the specified conditions. The second strategy entails condition-specific negative rectification which addresses the inconsistencies typically brought about by Classifier-free guidance in few-shot training contexts. Our extensive experiments across a variety of condition modalities demonstrate the effectiveness and efficiency of our framework yielding results comparable to those obtained with datasets a thousand times larger. Our codes are available at https://github.com/Yuyan9Yu/BeyondTextConstraint.
CLIP showcases exceptional cross-modal matching capabilities due to its training on image-text contrastive learning tasks. However without specific optimization for unimodal scenarios its performance in single-modality feature extraction might be suboptimal. Despite this some studies have directly used CLIP's image encoder for tasks like few-shot classification introducing a misalignment between its pre-training objectives and feature extraction methods. This inconsistency can diminish the quality of the image's feature representation adversely affecting CLIP's effectiveness in target tasks. In this paper we view text features as precise neighbors of image features in CLIP's space and present a novel CrOss-moDal nEighbor Representation (CODER) based on the distance structure between images and their neighbor texts. This feature extraction method aligns better with CLIP's pre-training objectives thereby fully leveraging CLIP's robust cross-modal capabilities. The key to construct a high-quality CODER lies in how to create a vast amount of high-quality and diverse texts to match with images. We introduce the Auto Text Generator (ATG) to automatically produce the required text in a data-free and training-free manner. We apply CODER to CLIP's zero-shot and few-shot image classification tasks. Experiment results across various datasets and models confirm CODER's effectiveness. Code is available at: https://github.com/YCaigogogo/CVPR24-CODER.
Diffusion models (DMs) have achieved remarkable generative performance particularly with the introduction of stochastic differential equations (SDEs). Nevertheless a gap emerges in the model sampling trajectory constructed by reverse-SDE due to the accumulation of score estimation and discretization errors. This gap results in a residual in the generated images adversely impacting the image quality. To remedy this we propose a novel residual learning framework built upon a correction function. The optimized function enables to improve image quality via rectifying the sampling trajectory effectively. Importantly our framework exhibits transferable residual correction ability i.e. a correction function optimized for one pre-trained DM can also enhance the sampling trajectory constructed by other different DMs on the same dataset. Experimental results on four widely-used datasets demonstrate the effectiveness and transferable capability of our framework.
Training a linear classifier or lightweight model on top of pretrained vision model outputs so-called 'frozen features' leads to impressive performance on a number of downstream few-shot tasks. Currently frozen features are not modified during training. On the other hand when networks are trained directly on images data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space dubbed 'frozen feature augmentation (FroFA)' covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA such as brightness can improve few-shot performance consistently across three network architectures three large pretraining datasets and eight transfer datasets.
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning
PDF ↗Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To address this problem a unified framework including the multi-pyramid transformer (MPT) and extended frequency contrastive regularization (EFCR) is proposed to tackle two outstanding challenges in microscopy deblur: longer attention span and data deficiency. The MPT employs an explicit pyramid structure at each network stage that integrates the cross-scale window attention (CSWA) the intra-scale channel attention (ISCA) and the feature-enhancing feed-forward network (FEFN) to capture long-range cross-scale spatial interaction and global channel context. The EFCR addresses the data deficiency problem by exploring latent deblur signals from different frequency bands. It also enables deblur knowledge transfer to learn cross-domain information from extra data improving deblur performance for labeled and unlabeled data. Extensive experiments and downstream task validation show the framework achieves state-of-the-art performance across multiple datasets. Project page: https://github.com/PieceZhang/MPT-CataBlur.
Learning from a limited amount of data namely Few-Shot Learning stands out as a challenging computer vision task. Several works exploit semantics and design complicated semantic fusion mechanisms to compensate for rare representative features within restricted data. However relying on naive semantics such as class names introduces biases due to their brevity while acquiring extensive semantics from external knowledge takes a huge time and effort. This limitation severely constrains the potential of semantics in Few-Shot Learning. In this paper we design an automatic way called Semantic Evolution to generate high-quality semantics. The incorporation of high-quality semantics alleviates the need for complex network structures and learning algorithms used in previous works. Hence we employ a simple two-layer network termed Semantic Alignment Network to transform semantics and visual features into robust class prototypes with rich discriminative features for few-shot classification. The experimental results show our framework outperforms all previous methods on six benchmarks demonstrating a simple network with high-quality semantics can beat intricate multi-modal modules on few-shot classification tasks. Code is available at https://github.com/zhangdoudou123/SemFew.
We introduce the Fixed Point Diffusion Model (FPDM) a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed point solving layer into the denoising network of a diffusion model transforming the diffusion process into a sequence of closely-related fixed point problems. Combined with a new stochastic training method this approach significantly reduces model size reduces memory usage and accelerates training. Moreover it enables the development of two new techniques to improve sampling efficiency: reallocating computation across timesteps and reusing fixed point solutions between timesteps. We conduct extensive experiments with state-of-the-art models on ImageNet FFHQ CelebA-HQ and LSUN-Church demonstrating substantial improvements in performance and efficiency. Compared to the state-of-the-art DiT model FPDM contains 87% fewer parameters consumes 60% less memory during training and improves image generation quality in situations where sampling computation or time is limited.
Many problems in computer vision can be formulated as geometric estimation problems i.e. given a collection of measurements (e.g. point correspondences) we wish to fit a model (e.g. an essential matrix) that agrees with our observations. This necessitates some measure of how much an observation "agrees" with a given model. A natural choice is to consider the smallest perturbation that makes the observation exactly satisfy the constraints. However for many problems this metric is expensive or otherwise intractable to compute. The so-called Sampson error approximates this geometric error through a linearization scheme. For epipolar geometry the Sampson error is a popular choice and in practice known to yield very tight approximations of the corresponding geometric residual (the reprojection error). In this paper we revisit the Sampson approximation and provide new theoretical insights as to why and when this approximation works as well as provide explicit bounds on the tightness under some mild assumptions. Our theoretical results are validated in several experiments on real data and in the context of different geometric estimation tasks.
Towards holistic understanding of 3D scenes a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories while also reflecting the inherent hierarchical structure. To achieve this we propose OmniSeg3D an omniversal segmentation method aims for segmenting anything in 3D all at once. The key insight is to lift multi-view inconsistent 2D segmentations into a consistent 3D feature field through a hierarchical contrastive learning framework which is accomplished by two steps. Firstly we design a novel hierarchical representation based on category-agnostic 2D segmentations to model the multi-level relationship among pixels. Secondly image features rendered from the 3D feature field are clustered at different levels which can be further drawn closer or pushed apart according to the hierarchical relationship between different levels. In tackling the challenges posed by inconsistent 2D segmentations this framework yields a global consistent 3D feature field which further enables hierarchical segmentation multi-object selection and global discretization. Extensive experiments demonstrate the effectiveness of our method on high-quality 3D segmentation and accurate hierarchical structure understanding. A graphical user interface further facilitates flexible interaction for omniversal 3D segmentation.
Predicting 3D point trajectory is a fundamental learning task which commonly should be equivariant under Euclidean transformation e.g. SE(3). The existing equivariant models are commonly based on the group equivariant convolution equivariant message passing vector neuron frame averaging etc. In this paper we propose a novel pose-transformed equivariant network in which the points are firstly uniquely normalized and then transformed by the learned pose transformations upon which the points after motion are predicted and aggregated. Under each transformed pose we design the point position predictor consisting of multiple Pose-Transformed Points Prediction blocks in which the global and local motions are estimated and aggregated. This framework can be proven to be equivariant to SE(3) transformation over 3D points. We evaluate the pose-transformed equivariant network on extensive datasets including human motion capture molecular dynamics modeling and dynamics simulation. Extensive experimental comparisons demonstrated our SOTA performance compared with the existing equivariant networks for 3D point trajectory prediction.
Unpaired image dehazing (UID) holds significant research importance due to the challenges in acquiring haze/clear image pairs with identical backgrounds. This paper proposes a novel method for UID named Orthogonal Decoupling Contrastive Regularization (ODCR). Our method is grounded in the assumption that an image consists of both haze-related features which influence the degree of haze and haze-unrelated features such as texture and semantic information. ODCR aims to ensure that the haze-related features of the dehazing result closely resemble those of the clear image while the haze-unrelated features align with the input hazy image. To accomplish the motivation Orthogonal MLPs optimized geometrically on the Stiefel manifold are proposed which can project image features into an orthogonal space thereby reducing the relevance between different features. Furthermore a task-driven Depth-wise Feature Classifier (DWFC) is proposed which assigns weights to the orthogonal features based on the contribution of each channel's feature in predicting whether the feature source is hazy or clear in a self-supervised fashion. Finally a Weighted PatchNCE (WPNCE) loss is introduced to achieve the pulling of haze-related features in the output image toward those of clear images while bringing haze-unrelated features close to those of the hazy input. Extensive experiments demonstrate the superior performance of our ODCR method on UID.
Diffusion models have recently achieved remarkable progress in generating realistic images. However challenges remain in accurately understanding and synthesizing the layout requirements in the textual prompts. To align the generated image with layout instructions we present a training-free layout calibration system SimM that intervenes in the generative process on the fly during inference time. Specifically following a "check-locate-rectify" pipeline the system first analyses the prompt to generate the target layout and compares it with the intermediate outputs to automatically detect errors. Then by moving the located activations and making intra- and inter-map adjustments the rectification process can be performed with negligible computational overhead. To evaluate SimM over a range of layout requirements we present a benchmark SimMBench that compensates for the lack of superlative spatial relations in existing datasets. And both quantitative and qualitative results demonstrate the effectiveness of the proposed SimM in calibrating the layout inconsistencies. Our project page is at https://simm-t2i.github.io/SimM.
We present Score-Guided Human Mesh Recovery (ScoreHMR) an approach for solving inverse problems for 3D human pose and shape reconstruction. These inverse problems involve fitting a human body model to image observations traditionally solved through optimization techniques. ScoreHMR mimics model fitting approaches but alignment with the image observation is achieved through score guidance in the latent space of a diffusion model. The diffusion model is trained to capture the conditional distribution of the human model parameters given an input image. By guiding its denoising process with a task-specific score ScoreHMR effectively solves inverse problems for various applications without the need for retraining the task-agnostic diffusion model. We evaluate our approach on three settings/applications. These are: (i) single-frame model fitting; (ii) reconstruction from multiple uncalibrated views; (iii) reconstructing humans in video sequences. ScoreHMR consistently outperforms all optimization baselines on popular benchmarks across all settings. We make our code and models available on the project website: https://statho.github.io/ScoreHMR.
Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts. However existing personalized generation methods cannot simultaneously satisfy the requirements of high efficiency promising identity (ID) fidelity and flexible text controllability. In this work we introduce PhotoMaker an efficient personalized text-to-image generation method which mainly encodes an arbitrary number of input ID images into a stack ID embedding for preserving ID information. Such an embedding also empowers our method to be applied in many interesting scenarios such as when replacing the corresponding class word and when combining the characteristics of different identities. Besides to better drive the training of our PhotoMaker we propose an ID-oriented data creation pipeline to assemble the training data. Under the nourishment of the dataset constructed through the proposed pipeline our PhotoMaker demonstrates comparable performance to test-time fine-tuning-based methods yet provides significant speed improvements strong generalization capabilities and a wide range of applications.
Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning
PDF ↗In the realm of computer vision and graphics accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking registration texture transfer and statistical shape analysis. Moving beyond traditional hand-crafted and data-driven feature learning methods we incorporate spectral methods with deep learning focusing on functional maps (FMs) and optimal transport (OT). Traditional OT-based approaches often reliant on entropy regularization OT in learning-based framework face computational challenges due to their quadratic cost. Our key contribution is to employ the sliced Wasserstein distance (SWD) for OT which is a valid fast optimal transport metric in an unsupervised shape matching framework. This unsupervised framework integrates functional map regularizers with a novel OT-based loss derived from SWD enhancing feature alignment between shapes treated as discrete probability measures. We also introduce an adaptive refinement process utilizing entropy regularized OT further refining feature alignments for accurate point-to-point correspondences. Our method demonstrates superior performance in non-rigid shape matching including near-isometric and non-isometric scenarios and excels in downstream tasks like segmentation transfer. The empirical results on diverse datasets highlight our framework's effectiveness and generalization capabilities setting new standards in non-rigid shape matching with efficient OT metrics and an adaptive refinement module.
Hand mesh reconstruction has attracted considerable attention in recent years with various approaches and techniques being proposed. Some of these methods incorporate complex components and designs which while effective may complicate the model and hinder efficiency. In this paper we decompose the mesh decoder into token generator and mesh regressor. Through extensive ablation experiments we found that the token generator should select discriminating and representative points while the mesh regressor needs to upsample sparse keypoints into dense meshes in multiple stages. Given these functionalities we can achieve high performance with minimal computational resources. Based on this observation we propose a simple yet effective baseline that outperforms state-of-the-art methods by a large margin while maintaining real-time efficiency. Our method outperforms existing solutions achieving state-of-the-art (SOTA) results across multiple datasets. On the FreiHAND dataset our approach produced a PA-MPJPE of 5.8mm and a PA-MPVPE of 6.1mm. Similarly on the DexYCB dataset we observed a PA-MPJPE of 5.5mm and a PA-MPVPE of 5.5mm. As for performance speed our method reached up to 33 frames per second (fps) when using HRNet and up to 70 fps when employing FastViT-MA36. Code will be made available.