Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performance posing serious threats to privacy. Meanwhile all existing MI defense methods rely on regularization that is in direct conflict with the training objective resulting in noticeable degradation in model utility. In this work we take a different perspective and propose a novel and simple Transfer Learning-based Defense against Model Inversion (TL-DMI) to render MI-robust models. Particularly by leveraging TL we limit the number of layers encoding sensitive information from private training dataset thereby degrading the performance of MI attack. We conduct an analysis using Fisher Information to justify our method. Our defense is remarkably simple to implement. Without bells and whistles we show in extensive experiments that TL-DMI achieves state-of-the-art (SOTA) MI robustness. Our code pre-trained models demo and inverted data are available at: https://hosytuyen.github.io/projects/TL-DMI
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Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance they perform a dense perception of images which incorporates redundant visual regions unrelated to linguistic queries leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks with limited exploration in vision-language fields. To address this we propose a coarse-to-fine iterative perception framework called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration irrelevant patches are discarded by our designed informativeness prediction. Furthermore we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets namely RefCOCO RefCOCO+ RefCOCOg and ReferItGame verify the effectiveness of our method which can strike a balance between accuracy and efficiency.
Estimating the 3D structure of the human body from nat- ural scenes is a fundamental aspect of visual perception. 3D human pose estimation is a vital step in advancing fields like AIGC and human-robot interaction serving as a crucial tech- nique for understanding and interacting with human actions in real-world settings. However the current datasets often collected under single laboratory conditions using complex motion capture equipment and unvarying backgrounds are insufficient. The absence of datasets on variable conditions is stalling the progress of this crucial task. To facilitate the development of 3D pose estimation we present FreeMan the first large-scale multi-view dataset collected under the real- world conditions. FreeMan was captured by synchronizing 8 smartphones across diverse scenarios. It comprises 11M frames from 8000 sequences viewed from different perspec- tives. These sequences cover 40 subjects across 10 different scenarios each with varying lighting conditions. We have also established an semi-automated pipeline containing er- ror detection to reduce the workload of manual check and ensure precise annotation. We provide comprehensive eval- uation baselines for a range of tasks underlining the sig- nificant challenges posed by FreeMan. Further evaluations of standard indoor/outdoor human sensing datasets reveal that FreeMan offers robust representation transferability in real and complex scenes. FreeMan is publicly available at https://wangjiongw.github.io/freeman.
Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g. faces on the back view) and inaccurate shapes (e.g. animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover to ensure accurate appearances of different views we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity.
Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Previous infrared physical attacks mainly focused on 2D infrared pedestrian detection which may not fully manifest its destructiveness to AI systems. In this work we propose a physical attack method against infrared detectors based on 3D modeling which is applied to a real car. The goal is to design a set of infrared adversarial stickers to make cars invisible to infrared detectors at various viewing angles distances and scenes. We build a 3D infrared car model with real infrared characteristics and propose an infrared adversarial pattern generation method based on 3D mesh shadow. We propose a 3D control points-based mesh smoothing algorithm and use a set of smoothness loss functions to enhance the smoothness of adversarial meshes and facilitate the sticker implementation. Besides We designed the aluminum stickers and conducted physical experiments on two real Mercedes-Benz A200L cars. Our adversarial stickers hid the cars from Faster RCNN an object detector at various viewing angles distances and scenes. The attack success rate (ASR) was 91.49% for real cars. In comparison the ASRs of random stickers and no sticker were only 6.21% and 0.66% respectively. In addition the ASRs of the designed stickers against six unseen object detectors such as YOLOv3 and Deformable DETR were between 73.35%-95.80% showing good transferability of the attack performance across detectors.
This paper presents DriveTrack a new benchmark and data generation framework for long-range keypoint tracking in real-world videos. DriveTrack is motivated by the observation that the accuracy of state-of-the-art trackers depends strongly on visual attributes around the selected keypoints such as texture and lighting. The problem is that these artifacts are especially pronounced in real-world videos but these trackers are unable to train on such scenes due to a dearth of annotations. DriveTrack bridges this gap by building a framework to automatically annotate point tracks on autonomous driving datasets. We release a dataset consisting of 1 billion point tracks across 24 hours of video which is seven orders of magnitude greater than prior real-world benchmarks and on par with the scale of synthetic benchmarks. DriveTrack unlocks new use cases for point tracking in real-world videos. First we show that fine-tuning keypoint trackers on DriveTrack improves accuracy on real-world scenes by up to 7%. Second we analyze the sensitivity of trackers to visual artifacts in real scenes and motivate the idea of running assistive keypoint selectors alongside trackers.
Audio-visual segmentation (AVS) aims to segment the sounding objects in video frames. Although great progress has been witnessed we experimentally reveal that current methods reach marginal performance gain within the use of the unlabeled frames leading to the underutilization issue. To fully explore the potential of the unlabeled frames for AVS we explicitly divide them into two categories based on their temporal characteristics i.e. neighboring frame (NF) and distant frame (DF). NFs temporally adjacent to the labeled frame often contain rich motion information that assists in the accurate localization of sounding objects. Contrary to NFs DFs have long temporal distances from the labeled frame which share semantic-similar objects with appearance variations. Considering their unique characteristics we propose a versatile framework that effectively leverages them to tackle AVS. Specifically for NFs we exploit the motion cues as the dynamic guidance to improve the objectness localization. Besides we exploit the semantic cues in DFs by treating them as valid augmentations to the labeled frames which are then used to enrich data diversity in a self-training manner. Extensive experimental results demonstrate the versatility and superiority of our method unleashing the power of the abundant unlabeled frames.
Generative object compositing emerges as a promising new avenue for compositional image editing. However the requirement of object identity preservation poses a significant challenge limiting practical usage of most existing methods. In response this paper introduces IMPRINT a novel diffusion-based generative model trained with a two-stage learning framework that decouples learning of identity preservation from that of compositing. The first stage is targeted for context-agnostic identity-preserving pretraining of the object encoder enabling the encoder to learn an embedding that is both view-invariant and conducive to enhanced detail preservation. The subsequent stage leverages this representation to learn seamless harmonization of the object composited to the background. In addition IMPRINT incorporates a shape-guidance mechanism offering user-directed control over the compositing process. Extensive experiments demonstrate that IMPRINT significantly outperforms existing methods and various baselines on identity preservation and composition quality.
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
PDF ↗Large Vision-Language Models (LVLMs) have advanced considerably intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned. Despite their success LVLMs still suffer from the issue of object hallucinations where models generate plausible yet incorrect outputs that include objects that do not exist in the images. To mitigate this issue we introduce Visual Contrastive Decoding (VCD) a simple and training-free method that contrasts output distributions derived from original and distorted visual inputs. The proposed VCD effectively reduces the over-reliance on statistical bias and unimodal priors two essential causes of object hallucinations. This adjustment ensures the generated content is closely grounded to visual inputs resulting in contextually accurate outputs. Our experiments show that VCD without either additional training or the usage of external tools significantly mitigates the object hallucination issue across different LVLM families. Beyond mitigating object hallucinations VCD also excels in general LVLM benchmarks highlighting its wide-ranging applicability.
Open world object detection aims to identify objects of unseen categories and incrementally recognize them once their annotations are provided. In distinction to the traditional paradigm that is limited to predefined categories this setting promises a continual and generalizable way of estimating objectness using class-agnostic information. However achieving such decorrelation between objectness and class information proves challenging. Without explicit consideration existing methods usually exhibit low recall on unknown objects and can misclassify them into known classes. To address this problem we exploit three levels of orthogonality in the detection process: First the objectness and classification heads are disentangled by operating on separate sets of features that are orthogonal to each other in a devised polar coordinate system. Secondly a prediction decorrelation loss is introduced to guide the detector towards more general and class-independent prediction. Furthermore we propose a calibration scheme that helps maintain orthogonality throughout the training process to mitigate catastrophic interference and facilitate incremental learning of previously unseen objects. Our method is comprehensively evaluated on open world and incremental object detection benchmarks demonstrating its effectiveness in detecting both known and unknown objects. Code and models are available at https://github.com/feifeiobama/OrthogonalDet.
Vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However many VLMs rely on proprietary data and are not open-source which restricts the use of white-box approaches for fine-tuning. As such we aim to develop a black-box approach to optimize VLMs through natural language prompts thereby avoiding the need to access model parameters feature embeddings or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically we adopt an automatic "hill-climbing" procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts suggesting that LLMs can utilize the implicit "gradient" direction in textual feedback for a more efficient search. In addition we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization.
Universal image restoration is a practical and potential computer vision task for real-world applications. The main challenge of this task is handling the different degradation distributions at once. Existing methods mainly utilize task-specific conditions (e.g. prompt) to guide the model to learn different distributions separately named multi-partite mapping. However it is not suitable for universal model learning as it ignores the shared information between different tasks. In this work we propose an advanced selective hourglass mapping strategy based on diffusion model termed DiffUIR. Two novel considerations make our DiffUIR non-trivial. Firstly we equip the model with strong condition guidance to obtain accurate generation direction of diffusion model (selective). More importantly DiffUIR integrates a flexible shared distribution term (SDT) into the diffusion algorithm elegantly and naturally which gradually maps different distributions into a shared one. In the reverse process combined with SDT and strong condition guidance DiffUIR iteratively guides the shared distribution to the task-specific distribution with high image quality (hourglass). Without bells and whistles by only modifying the mapping strategy we achieve state-of-the-art performance on five image restoration tasks 22 benchmarks in the universal setting and zero-shot generalization setting. Surprisingly by only using a lightweight model (only 0.89M) we could achieve outstanding performance. The source code and pre-trained models are available at https://github.com/iSEE-Laboratory/DiffUIR
The creation of new datasets often presents new challenges for video recognition and can inspire novel ideas while addressing these challenges. While existing datasets mainly comprise landscape mode videos our paper seeks to introduce portrait mode videos to the research community and highlight the unique challenges associated with this video format. With the growing popularity of smartphones and social media applications recognizing portrait mode videos is becoming increasingly important. To this end we have developed the first dataset dedicated to portrait mode video recognition namely PortraitMode-400. The taxonomy of PortraitMode-400 was constructed in a data-driven manner comprising 400 fine-grained categories and rigorous quality assurance was implemented to ensure the accuracy of human annotations. In addition to the new dataset we conducted a comprehensive analysis of the impact of video format (portrait mode versus landscape mode) on recognition accuracy and spatial bias due to the different formats. Furthermore we designed extensive experiments to explore key aspects of portrait mode video recognition including the choice of data augmentation evaluation procedure the importance of temporal information and the role of audio modality. Building on the insights from our experimental results and the introduction of PortraitMode-400 our paper aims to inspire further research efforts in this emerging research area.
FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment
PDF ↗Existing action quality assessment (AQA) methods mainly learn deep representations at the video level for scoring diverse actions. Due to the lack of a fine-grained understanding of actions in videos they harshly suffer from low credibility and interpretability thus insufficient for stringent applications such as Olympic diving events. We argue that a fine-grained understanding of actions requires the model to perceive and parse actions in both time and space which is also the key to the credibility and interpretability of the AQA technique. Based on this insight we propose a new fine-grained spatial-temporal action parser named FineParser. It learns human-centric foreground action representations by focusing on target action regions within each frame and exploiting their fine-grained alignments in time and space to minimize the impact of invalid backgrounds during the assessment. In addition we construct fine-grained annotations of human-centric foreground action masks for the FineDiving dataset called FineDiving-HM. With refined annotations on diverse target action procedures FineDiving-HM can promote the development of real-world AQA systems. Through extensive experiments we demonstrate the effectiveness of FineParser which outperforms state-of-the-art methods while supporting more tasks of fine-grained action understanding. Data and code are available at https://github.com/PKU-ICST-MIPL/FineParser_CVPR2024.
Diffusion-based text-to-image generative models e.g. Stable Diffusion have revolutionized the field of content generation enabling significant advancements in areas like image editing and video synthesis. Despite their formidable capabilities these models are not without their limitations. It is still challenging to synthesize an image that aligns well with the input text and multiple runs with carefully crafted prompts are required to achieve satisfactory results. To mitigate these limitations numerous studies have endeavored to fine-tune the pre-trained diffusion models i.e.. UNet utilizing various technologies. Yet amidst these efforts a pivotal question of text-to-image diffusion model training has remained largely unexplored: Is it possible and feasible to fine-tune the text encoder to improve the performance of text-to-image diffusion models? Our findings reveal that instead of replacing the CLIP text encoder used in Stable Diffusion with other large language models we can enhance it through our proposed fine-tuning approach TextCraftor leading to substantial improvements in quantitative benchmarks and human assessments. Interestingly our technique also empowers controllable image generation through the interpolation of different text encoders fine-tuned with various rewards. We also demonstrate that TextCraftor is orthogonal to UNet finetuning and can be combined to further improve generative quality.
Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with either video-level supervision one-class supervision or in an unsupervised setting. Training-based methods are prone to be domain-specific thus being costly for practical deployment as any domain change will involve data collection and model training. In this paper we radically depart from previous efforts and propose LAnguage-based VAD (LAVAD) a method tackling VAD in a novel training-free paradigm exploiting the capabilities of pre-trained large language models (LLMs) and existing vision-language models (VLMs). We leverage VLM-based captioning models to generate textual descriptions for each frame of any test video. With the textual scene description we then devise a prompting mechanism to unlock the capability of LLMs in terms of temporal aggregation and anomaly score estimation turning LLMs into an effective video anomaly detector. We further leverage modality-aligned VLMs and propose effective techniques based on cross-modal similarity for cleaning noisy captions and refining the LLM-based anomaly scores. We evaluate LAVAD on two large datasets featuring real-world surveillance scenarios (UCF-Crime and XD-Violence) showing that it outperforms both unsupervised and one-class methods without requiring any training or data collection.
FINER: Flexible Spectral-bias Tuning in Implicit NEural Representation by Variable-periodic Activation Functions
PDF ↗Implicit Neural Representation (INR) which utilizes a neural network to map coordinate inputs to corresponding attributes is causing a revolution in the field of signal processing. However current INR techniques suffer from a restricted capability to tune their supported frequency set resulting in imperfect performance when representing complex signals with multiple frequencies. We have identified that this frequency-related problem can be greatly alleviated by introducing variable-periodic activation functions for which we propose FINER. By initializing the bias of the neural network within different ranges sub-functions with various frequencies in the variable-periodic function are selected for activation. Consequently the supported frequency set of FINER can be flexibly tuned leading to improved performance in signal representation. We demonstrate the capabilities of FINER in the contexts of 2D image fitting 3D signed distance field representation and 5D neural radiance fields optimization and we show that it outperforms existing INRs.
Current vision-language pre-training (VLP) methodologies predominantly depend on paired image-text datasets a resource that is challenging to acquire in radiology due to privacy considerations and labelling complexities. Data augmentation provides a practical solution to overcome the issue of data scarcity however most augmentation methods exhibit a limited focus prioritising either image or text augmentation exclusively. Acknowledging this limitation our objective is to devise a framework capable of concurrently augmenting medical image and text data. We design a Pairwise Augmentation (PairAug) approach that contains an Inter-patient Augmentation (InterAug) branch and an Intra-patient Augmentation (IntraAug) branch. Specifically the InterAug branch of our approach generates radiology images using synthesised yet plausible reports derived from a Large Language Model (LLM). The generated pairs can be considered a collection of new patient cases since they are artificially created and may not exist in the original dataset. In contrast the IntraAug branch uses newly generated reports to manipulate images. This process allows us to create new paired data for each individual with diverse medical conditions. Our extensive experiments on various downstream tasks covering medical image classification zero-shot and fine-tuning analysis demonstrate that our PairAug concurrently expanding both image and text data substantially outperforms image-/text-only expansion baselines and advanced medical VLP baselines. Our code is released at https://github.com/YtongXie/PairAug.
Photorealistic simulation plays a crucial role in applications such as autonomous driving where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However reconstruction quality suffers on street scenes due to largely collinear camera motions and sparser samplings at higher speeds. On the other hand the application often demands rendering from camera views that deviate from the inputs to accurately simulate behaviors like lane changes. In this paper we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes. First our framework learns a geometric scene representation from Lidar which are fused with the implicit grid-based representation for radiance decoding thereby supplying stronger geometric information offered by explicit point cloud. Second we put forth a robust occlusion-aware depth supervision scheme which allows utilizing densified Lidar points by accumulation. Third we generate augmented training views from Lidar points for further improvement. Our insights translate to largely improved novel view synthesis under real driving scenes.
Learning generalizable visual representations from Internet data has yielded promising results for robotics. Yet prevailing approaches focus on pre-training 2D representations being sub-optimal to deal with occlusions and accurately localize objects in complex 3D scenes. Meanwhile 3D representation learning has been limited to single-object understanding. To address these limitations we introduce a novel 3D pre-training framework for robotics named SUGAR that captures semantic geometric and affordance properties of objects through 3D point clouds. We underscore the importance of cluttered scenes in 3D representation learning and automatically construct a multi-object dataset benefiting from cost-free supervision in simulation. SUGAR employs a versatile transformer-based model to jointly address five pre-training tasks namely cross-modal knowledge distillation for semantic learning masked point modeling to understand geometry structures grasping pose synthesis for object affordance 3D instance segmentation and referring expression grounding to analyze cluttered scenes. We evaluate our learned representation on three robotic-related tasks namely zero-shot 3D object recognition referring expression grounding and language-driven robotic manipulation. Experimental results show that SUGAR's 3D representation outperforms state-of-the-art 2D and 3D representations.