CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem we contribute a new benchmark named UFineBench for text-based person retrieval with ultra-fine granularity. Firstly we construct a new dataset named UFine6926. We collect a large number of person images and manually annotate each image with two detailed textual descriptions averaging 80.8 words each. The average word count is three to four times that of the previous datasets. In addition of standard in-domain evaluation we also propose a special evaluation paradigm more representative of real scenarios. It contains a new evaluation set with cross domains cross textual granularity and cross textual styles named UFine3C and a new evaluation metric for accurately measuring retrieval ability named mean Similarity Distribution (mSD). Moreover we propose CFAM a more efficient algorithm especially designed for text-based person retrieval with ultra fine-grained texts. It achieves fine granularity mining by adopting a shared cross-modal granularity decoder and hard negative match mechanism. With standard in-domain evaluation CFAM establishes competitive performance across various datasets especially on our ultra fine-grained UFine6926.
It achieves fine granularity mining by adopting a shared cross-modal granularity decoder and hard negative match mechanism. With standard in-domain evaluation CFAM establishes competitive performance across various datasets especially on our ultra fine-grained UFine6926. Furthermore by evaluating on UFine3C we demonstrate that training on our UFine6926 significantly improves generalization to real scenarios compared with other coarse-grained datasets. The dataset and code will be made publicly available at https://github.com/Zplusdragon/UFineBench.
Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method. Extending BO into the multi-fidelity setting has been an emerging research topic in this field but faces the challenge of determining an appropriate fidelity for each hyperparameter configuration to fit the surrogate model. To tackle the challenge we propose a multi-fidelity BO method named FastBO which excels in adaptively deciding the fidelity for each configuration and providing strong performance while ensuring efficient resource usage. These advantages are achieved through our proposed techniques based on the concepts of efficient point and saturation point for each configuration which can be obtained from the empirical learning curve of the configuration estimated from early observations. Extensive experiments demonstrate FastBO's superior anytime performance and efficiency in identifying high-quality configurations and architectures. We also show that our method provides a way to extend any single-fidelity method to the multi-fidelity setting highlighting the wide applicability of our approach.
Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars real-time performance has mostly been demonstrated for static scenes only. To address this we propose ASH an animatable Gaussian splatting approach for photorealistic rendering of dynamic humans in real time. We parameterize the clothed human as animatable 3D Gaussians which can be efficiently splatted into image space to generate the final rendering. However naively learning the Gaussian parameters in 3D space poses a severe challenge in terms of compute. Instead we attach the Gaussians onto a deformable character model and learn their parameters in 2D texture space which allows leveraging efficient 2D convolutional architectures that easily scale with the required number of Gaussians. We benchmark ASH with competing methods on pose-controllable avatars demonstrating that our method outperforms existing real-time methods by a large margin and shows comparable or even better results than offline methods.
Adversarial training is often formulated as a min-max problem however concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model i.e. previously defended or correctly classified samples are not defensible or accurately classifiable in subsequent adversarial training. We characterize such non-ignorable samples as "hiders" which reveal the hidden high-risk regions within the secure area obtained through adversarial training and prevent the model from finding the real worst cases. We demand the model to prevent hiders when defending against adversarial examples for improving accuracy and robustness simultaneously. By rethinking and redefining the min-max optimization problem for adversarial training we propose a generalized adversarial training algorithm called Hider-Focused Adversarial Training (HFAT). HFAT introduces the iterative evolution optimization strategy to simplify the optimization problem and employs an auxiliary model to reveal hiders effectively combining the optimization directions of standard adversarial training and prevention hiders. Furthermore we introduce an adaptive weighting mechanism that facilitates the model in adaptively adjusting its focus between adversarial examples and hiders during different training periods. We demonstrate the effectiveness of our method based on extensive experiments and ensure that HFAT can provide higher robustness and accuracy. We will release the source code upon publication.
This work introduces ArtAdapter a transformative text-to-image (T2I) style transfer framework that transcends traditional limitations of color brushstrokes and object shape capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with our proposed explicit adaptation mechanism enables ArtAdapter to achieve unprecedented fidelity in style transfer ensuring close alignment with textual descriptions. Additionally the incorporation of an Auxiliary Content Adapter (ACA) effectively separates content from style alleviating the borrowing of content from style references. Moreover our novel fast finetuning approach could further enhance zero-shot style representation while mitigating the risk of overfitting. Comprehensive evaluations confirm that ArtAdapter surpasses current state-of-the-art methods.
This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmentation capacity -- to learn a compact panoramic semantic segmentation model i.e. student without requiring any labeled data. This poses considerable challenges due to SAM's inability to provide semantic labels and the large capacity gap between SAM and the student. To this end we propose a novel framework called GoodSAM that introduces a teacher assistant (TA) to provide semantic information integrated with SAM to generate ensemble logits to achieve knowledge transfer. Specifically we propose a Distortion-Aware Rectification (DAR) module that first addresses the distortion problem of panoramic images by imposing prediction-level consistency and boundary enhancement. This subtly enhances TA's prediction capacity on panoramic images. DAR then incorporates a cross-task complementary fusion block to adaptively merge the predictions of SAM and TA to obtain more reliable ensemble logits. Moreover we introduce a Multi-level Knowledge Adaptation (MKA) module to efficiently transfer the multi-level feature knowledge from TA and ensemble logits to learn a compact student model.
DAR then incorporates a cross-task complementary fusion block to adaptively merge the predictions of SAM and TA to obtain more reliable ensemble logits. Moreover we introduce a Multi-level Knowledge Adaptation (MKA) module to efficiently transfer the multi-level feature knowledge from TA and ensemble logits to learn a compact student model. Extensive experiments on two benchmarks show that our GoodSAM achieves a remarkable +3.75% mIoU improvement over the state-of-the-art (SOTA) domain adaptation methods e.g. [41]. Also our most lightweight model achieves comparable performance to the SOTA methods with only 3.7M parameters.
In this paper we focus on a challenging Online Task-Free Class Incremental Learning (OTFCIL) problem. Different from the existing methods that continuously learn the feature space from data streams we propose a novel compute-and-align paradigm for the OTFCIL. It first computes an optimal geometry i.e. the class prototype distribution for classifying existing classes and updates it when new classes emerge and then trains a DNN model by aligning its feature space to the optimal geometry. To this end we develop a novel Dynamic Neural Collapse (DNC) algorithm to compute and update the optimal geometry. The DNC expands the geometry when new classes emerge without loss of the geometry optimality and guarantees the drift distance of old class prototypes with an explicit upper bound. Then we propose a novel Dynamic feature space Self-Organization (DYSON) method containing three major components including 1) a feature extractor 2) a Dynamic Feature-Geometry Alignment (DFGA) module aligning the feature space to the optimal geometry computed by DNC and 3) a training-free class-incremental classifier derived from the DNC geometry.
Experimental comparison results on four benchmark datasets including CIFAR10 CIFAR100 CUB200 and CoRe50 demonstrate the efficiency and superiority of the DYSON method. The source code is provided in the supplementary material.
An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos predict rich detailed textual descriptions and be able to produce outputs before processing the entire video. Current state-of-the-art models however process a fixed number of downsampled frames and make a single full prediction after seeing the whole video. We propose a streaming dense video captioning model that consists of two novel components: First we propose a new memory module based on clustering incoming tokens which can handle arbitrarily long videos as the memory is of a fixed size. Second we develop a streaming decoding algorithm that enables our model to make predictions before the entire video has been processed. Our model achieves this streaming ability and significantly improves the state-of-the-art on three dense video captioning benchmarks: ActivityNet YouCook2 and ViTT. Our code is released at https://github.com/google-research/scenic.
Despite the growing demand for accurate surface normal estimation models existing methods use general-purpose dense prediction models adopting the same inductive biases as other tasks. In this paper we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray direction and (2) encode the relationship between neighboring surface normals by learning their relative rotation. The proposed method can generate crisp - yet piecewise smooth - predictions for challenging in-the-wild images of arbitrary resolution and aspect ratio. Compared to a recent ViT-based state-of-the-art model our method shows a stronger generalization ability despite being trained on an orders of magnitude smaller dataset. The code is available at https://github.com/baegwangbin/DSINE.
Event sensors offer high temporal resolution visual sensing which makes them ideal for perceiving fast visual phenomena without suffering from motion blur. Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera such as recovering the angular velocity and shape of the object. The setting is equivalent to observing a static object with an orbiting camera. In this paper we propose event-based structure-from-orbit (eSfO) where the aim is to simultaneously reconstruct the 3D structure of a fast spinning object observed from a static event camera and recover the equivalent orbital motion of the camera. Our contributions are threefold: since state-of-the-art event feature trackers cannot handle periodic self-occlusion due to the spinning motion we develop a novel event feature tracker based on spatio-temporal clustering and data association that can better track the helical trajectories of valid features in the event data. The feature tracks are then fed to our novel factor graph-based structure-from-orbit back-end that calculates the orbital motion parameters (e.g. spin rate relative rotational axis) that minimize the reprojection error. For evaluation we produce a new event dataset of objects under spinning motion. Comparisons against ground truth indicate the efficacy of eSfO.
Event camera has significant advantages in capturingdynamic scene information while being prone to noise interferenceparticularly in challenging conditions like lowthreshold and low illumination. However most existing researchfocuses on gentle situations hindering event cameraapplications in realistic complex scenarios. To tackle thislimitation and advance the field we construct a new pairedreal-world event denoising dataset (LED) including 3K sequenceswith 18K seconds of high-resolution (1200*680)event streams and showing three notable distinctions comparedto others: diverse noise levels and scenes largerscalewith high-resolution and high-quality GT. Specificallyit contains stepped parameters and varying illuminationwith diverse scenarios. Moreover based on theproperty of noise events inconsistency and signal eventsconsistency we propose a novel effective denoising framework(DED) using homogeneous dual events to generate theGT with better separating noise from the raw. Furthermorewe design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholdsto realize accurate denoising. The experimental resultsdemonstrate that the remarkable performance of the proposedapproach on different datasets.The dataset and codeare at https://github.com/Yee-Sing/led.
Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions. These two disparities cause important parameters to potentially be overwhelmed by unimportant ones of dominant updates. It consequently results in significant performance decreases for lower-performing clients. 2) Model Aggregation Bias: existing FL approaches introduce unfair weight allocation and neglect domain diversity. It leads to biased model convergence objective and distinct performance among domains. We discover a pronounced directional update consistency in Federated Learning and propose a novel framework to tackle above issues. First leveraging the discovered characteristic we selectively discard unimportant parameter updates to prevent updates from clients with lower performance overwhelmed by unimportant parameters resulting in fairer generalization performance. Second we propose a fair aggregation objective to prevent global model bias towards some domains ensuring that the global model continuously aligns with an unbiased model. The proposed method is generic and can be combined with other existing FL methods to enhance fairness. Comprehensive experiments on Digits and Office-Caltech demonstrate the high fairness and performance of our method.
In this work we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of appearance biases such as clothing color and background. We propose ABNet a novel framework which leverages disentanglement of biometric and non-biometric features to perform effective person identification from daily activities. ABNet relies on a bias-less teacher to learn biometric features from RGB videos and explicitly disentangle non-biometric features with the help of biometric distortion. In addition ABNet also exploits activity prior for biometrics which is enabled by joint biometric and activity learning. We perform comprehensive evaluation of the proposed approach across five different datasets which are derived from existing activity recognition benchmarks. Furthermore we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets. The code and dataset can be accessed at: https://github.com/sacrcv/Activity-Biometrics/
Despite the remarkable progress in image style transfer formulating style in the context of art is inherently subjective and challenging. In contrast to existing methods this study shows that vanilla diffusion models can directly extract style information and seamlessly integrate the generative prior into the content image without retraining. Specifically we adopt dual denoising paths to represent content/style references in latent space and then guide the content image denoising process with style latent codes. We further reveal that the cross-attention mechanism in latent diffusion models tends to blend the content and style images resulting in stylized outputs that deviate from the original content image. To overcome this limitation we introduce a cross-attention reweighting strategy. Through theoretical analysis and experiments we demonstrate the effectiveness and superiority of the diffusion-based zero-shot style transfer via attention reweighting Z-STAR.
Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a simple relationship model. These methods however struggle with a diversity of appearance situation position interaction and relation in videos. This limitation hinders the ability to fully comprehend the interplay within the complex visual dynamics of subjects. In this paper we delve into interactivities understanding within visual content by deriving scene graph representations from dense interactivities among humans and objects. To achieve this goal we first present a new dataset containing Appearance-Situation-Position-Interaction-Relation predicates named ASPIRe offering an extensive collection of videos marked by a wide range of interactivities. Then we propose a new approach named Hierarchical Interlacement Graph (HIG) which leverages a unified layer and graph within a hierarchical structure to provide deep insights into scene changes across five distinct tasks. Our approach demonstrates superior performance to other methods through extensive experiments conducted in various scenarios.
Trajectory prediction is fundamental in computer vision and autonomous driving particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range physical obstructions and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns as they can result in missing essential non-visible objects. To bridge this gap we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj) setting a new benchmark for future research.
Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches such as employing correlation-aware causal graphs or proxies for mutual information these methods are less practical and not applicable in general. In this work we propose FAir DisEntanglement with Sensitive relevance (FADES) a novel approach that leverages conditional mutual information from the information theory perspective to address these challenges. We employ sensitive relevant code to direct correlated information between target labels and sensitive attributes by imposing conditional independence allowing better separation of the features of interest in the latent space. Utilizing an intuitive disentangling approach FADES consistently achieves superior performance and fairness both quantitatively and qualitatively with its straightforward structure. Specifically the proposed method outperforms existing works in downstream classification and counterfactual generations on various benchmarks.
Current controls over diffusion models (e.g. through text or ControlNet) for image generation fall short in recognizing abstract continuous attributes like illumination direction or non-rigid shape change. In this paper we present an approach for allowing users of text-to-image models to have fine-grained control of several attributes in an image. We do this by engineering special sets of input tokens that can be transformed in a continuous manner we call them Continuous 3D Words. These attributes can for example be represented as sliders and applied jointly with text prompts for fine-grained control over image generation. Given only a single mesh and a rendering engine we show that our approach can be adopted to provide continuous user control over several 3D-aware attributes including time-of-day illumination bird wing orientation dollyzoom effect and object poses. Our method is capable of conditioning image creation with multiple Continuous 3D Words and text descriptions simultaneously while adding no overhead to the generative process.
Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However this quality comes at significant computational cost: nearly all of these models are iterative and require running sampling multiple times with large models. This iterative process is needed to ensure that different regions of the image are not only aligned with the text prompt but also compatible with each other. In this work we propose a light-weight approach to achieving this compatibility between different regions of an image using a Markov Random Field (MRF) model. We demonstrate the effectiveness of this method on top of the latent token-based Muse text-to-image model. The MRF richly encodes the compatibility among image tokens at different spatial locations to improve quality and significantly reduce the required number of Muse sampling steps. Inference with the MRF is significantly cheaper and its parameters can be quickly learned through back-propagation by modeling MRF inference as a differentiable neural-network layer. Our full model MarkovGen uses this proposed MRF model to both speed up Muse by 1.5xand produce higher quality images by decreasing undesirable image artifacts.