CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
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.
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.
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.
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.
Garment manipulation (e.g. unfolding folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks while highly challenging due to the diversity of garment configurations geometries and deformations. Although able to manipulate similar shaped garments in a certain task previous works mostly have to design different policies for different tasks could not generalize to garments with diverse geometries and often rely heavily on human-annotated data. In this paper we leverage the property that garments in a certain category have similar structures and then learn the topological dense (point-level) visual correspondence among garments in the category level with different deformations in the self-supervised manner. The topological correspondence can be easily adapted to the functional correspondence to guide the manipulation policies for various downstream tasks within only one or few-shot demonstrations. Experiments over garments in 3 different categories on 3 representative tasks in diverse scenarios using one or two arms taking one or more steps inputting flat or messy garments demonstrate the effectiveness of our proposed method. Project page: https://warshallrho.github.io/unigarmentmanip.
3D visual grounding aims to localize 3D objects described by free-form language sentences. Following the detection-then-matching paradigm existing methods mainly focus on embedding object attributes in unimodal feature extraction and multimodal feature fusion to enhance the discriminability of the proposal feature for accurate grounding. However most of them ignore the explicit interaction of multiple attributes causing a bias in unimodal representation and misalignment in multimodal fusion. In this paper we propose a multi-attribute aware Transformer for 3D visual grounding learning the multi-attribute interactions to refine the intra-modal and inter-modal grounding cues. Specifically we first develop an attribute causal analysis module to quantify the causal effect of different attributes for the final prediction which provides powerful supervision to correct the misleading attributes and adaptively capture other discriminative features. Then we design an exchanging-based multimodal fusion module which dynamically replaces tokens with low attribute attention between modalities before directly integrating low-dimensional global features. This ensures an attribute-level multimodal information fusion and helps align the language and vision details more efficiently for fine-grained multimodal features. Extensive experiments show that our method can achieve state-of-the-art performance on ScanRefer and Sr3D/Nr3D datasets.
Video-P2P is the first framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-trained image generation models there are currently no large-scale video generation models publicly available. Video-P2P addresses this limitation by adapting an image generation diffusion model to complete various video editing tasks. Specifically we propose to first tune a Text-to-Set (T2S) model to complete an approximate inversion and then optimize a shared unconditional embedding to achieve accurate video inversion with a small memory cost. We further prove that it is crucial for consistent video editing. For attention control we introduce a novel decoupled-guidance strategy which uses different guidance strategies for the source and target prompts. The optimized unconditional embedding for the source prompt improves reconstruction ability while an initialized unconditional embedding for the target prompt enhances editability. Incorporating the attention maps of these two branches enables detailed editing. These technical designs enable various text-driven editing applications including word swap prompt refinement and attention re-weighting. Video-P2P works well on real-world videos for generating new characters while optimally preserving their original poses and scenes. It significantly outperforms previous approaches.
Recent weakly supervised semantic segmentation (WSSS) methods strive to incorporate contextual knowledge to improve the completeness of class activation maps (CAM). In this work we argue that the knowledge bias between instances and contexts affects the capability of the prototype to sufficiently understand instance semantics. Inspired by prototype learning theory we propose leveraging prototype awareness to capture diverse and fine-grained feature attributes of instances. The hypothesis is that contextual prototypes might erroneously activate similar and frequently co-occurring object categories due to this knowledge bias. Therefore we propose to enhance the prototype representation ability by mitigating the bias to better capture spatial coverage in semantic object regions. With this goal we present a Context Prototype-Aware Learning (CPAL) strategy which leverages semantic context to enrich instance comprehension. The core of this method is to accurately capture intra-class variations in object features through context-aware prototypes facilitating the adaptation to the semantic attributes of various instances. We design feature distribution alignment to optimize prototype awareness aligning instance feature distributions with dense features. In addition a unified training framework is proposed to combine label-guided classification supervision and prototypes-guided self-supervision.
We design feature distribution alignment to optimize prototype awareness aligning instance feature distributions with dense features. In addition a unified training framework is proposed to combine label-guided classification supervision and prototypes-guided self-supervision. Experimental results on PASCAL VOC 2012 and MS COCO 2014 show that CPAL significantly improves off-the-shelf methods and achieves state-of-the-art performance. The project is available at \href https://github.com/Barrett-python/CPAL  https://github.com/Barrett-python/CPAL.
In this paper we explore the potential of Snapshot Com- pressive Imaging (SCI) technique for recovering the under- lying 3D scene representation from a single temporal com- pressed image. SCI is a cost-effective method that enables the recording of high-dimensional data such as hyperspec- tral or temporal information into a single image using low- cost 2D imaging sensors. To achieve this a series of spe- cially designed 2D masks are usually employed which not only reduces storage requirements but also offers potential privacy protection. Inspired by this to take one step further our approach builds upon the powerful 3D scene represen- tation capabilities of neural radiance fields (NeRF). Specif- ically we formulate the physical imaging process of SCI as part of the training of NeRF allowing us to exploit its impressive performance in capturing complex scene struc- tures. To assess the effectiveness of our method we con- duct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experi- mental results demonstrate that our proposed approach sur- passes the state-of-the-art methods in terms of image re- construction and novel view image synthesis.
To assess the effectiveness of our method we con- duct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experi- mental results demonstrate that our proposed approach sur- passes the state-of-the-art methods in terms of image re- construction and novel view image synthesis. Moreover our method also exhibits the ability to restore high frame- rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://github.com/WU-CVGL/SCINeRF.
We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. Unlike existing methods we discretize nonlinear hyperelasticity in a meshless way obviating the necessity for intermediate auxiliary shape proxies like a tetrahedral mesh or voxel grid. A quadratic generalized moving least square is employed to capture nonlinear dynamics and large deformation on the implicit model. Such meshless integration enables versatile simulations of complex and codimensional shapes. We adaptively place the least-square kernels according to the NeRF density field to significantly reduce the complexity of the nonlinear simulation. As a result physically realistic animations can be conveniently synthesized using our method for a wide range of hyperelastic materials at an interactive rate. For more information please visit https://fytalon.github.io/pienerf.
Vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image. Our work shows that the localization --"grounding'"-- abilities of these models can be further improved by finetuning for self-consistent visual explanations. We propose a strategy for augmenting existing text-image datasets with paraphrases using a large language model and SelfEQ a weakly-supervised strategy on visual explanation maps for paraphrases that encourages self-consistency. Specifically for an input textual phrase we attempt to generate a paraphrase and finetune the model so that the phrase and paraphrase map to the same region in the image. We posit that this both expands the vocabulary that the model is able to handle and improves the quality of the object locations highlighted by gradient-based visual explanation methods (e.g. GradCAM). We demonstrate that SelfEQ improves performance on Flickr30k ReferIt and RefCOCO+ over a strong baseline method and several prior works.
GradCAM). We demonstrate that SelfEQ improves performance on Flickr30k ReferIt and RefCOCO+ over a strong baseline method and several prior works. Particularly comparing to other methods that do not use any type of box annotations we obtain 84.07% on Flickr30k (an absolute improvement of 4.69%) 67.40% on ReferIt (an absolute improvement of 7.68%) and 75.10% 55.49% on RefCOCO+ test sets A and B respectively (an absolute improvement of 3.74% on average).
Large Multimodal Models (LMMs) have shown promise in vision-language tasks but struggle with high-resolution input and detailed scene understanding. Addressing these challenges we introduce Monkey to enhance LMM capabilities. Firstly Monkey processes input images by dividing them into uniform patches each matching the size (e.g. 448x448) used in the original training of the well-trained vision encoder. Equipped with individual adapter for each patch Monkey can handle higher resolutions up to 1344x896 pixels enabling the detailed capture of complex visual information. Secondly it employs a multi-level description generation method enriching the context for scene-object associations. This two-part strategy ensures more effective learning from generated data: the higher resolution allows for a more detailed capture of visuals which in turn enhances the effectiveness of comprehensive descriptions. Extensive ablative results validate the effectiveness of our designs. Additionally experiments on 18 datasets further demonstrate that Monkey surpasses existing LMMs in many tasks like Image Captioning and various Visual Question Answering formats. Specially in qualitative tests focused on dense text question answering Monkey has exhibited encouraging results compared with GPT4V. Code is available at https://github.com/Yuliang-Liu/Monkey.
We propose FlashAvatar a novel and lightweight 3D animatable avatar representation that could reconstruct a digital avatar from a short monocular video sequence in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade GPU. To achieve this we maintain a uniform 3D Gaussian field embedded in the surface of a parametric face model and learn extra spatial offset to model non-surface regions and subtle facial details. While full use of geometric priors can capture high-frequency facial details and preserve exaggerated expressions proper initialization can help reduce the number of Gaussians thus enabling super-fast rendering speed. Extensive experimental results demonstrate that FlashAvatar outperforms existing works regarding visual quality and personalized details and is almost an order of magnitude faster in rendering speed. Project page: https://ustc3dv.github.io/FlashAvatar/
Scene flow estimation which aims to predict per-point 3D displacements of dynamic scenes is a fundamental task in the computer vision field. However previous works commonly suffer from unreliable correlation caused by locally constrained searching ranges and struggle with accumulated inaccuracy arising from the coarse-to-fine structure. To alleviate these problems we propose a novel uncertainty-aware scene flow estimation network (DifFlow3D) with the diffusion probabilistic model. Iterative diffusion-based refinement is designed to enhance the correlation robustness and resilience to challenging cases e.g. dynamics noisy inputs repetitive patterns etc. To restrain the generation diversity three key flow-related features are leveraged as conditions in our diffusion model. Furthermore we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow. Our DifFlow3D achieves state-of-the-art performance with 24.0% and 29.1% EPE3D reduction respectively on FlyingThings3D and KITTI 2015 datasets. Notably our method achieves an unprecedented millimeter-level accuracy (0.0078m in EPE3D) on the KITTI dataset.
Notably our method achieves an unprecedented millimeter-level accuracy (0.0078m in EPE3D) on the KITTI dataset. Additionally our diffusion-based refinement paradigm can be readily integrated as a plug-and-play module into existing scene flow networks significantly increasing their estimation accuracy. Codes are released at https://github.com/IRMVLab/DifFlow3D.
While standard Empirical Risk Minimization (ERM) training is proven effective for image classification on in-distribution data it fails to perform well on out-of-distribution samples. One of the main sources of distribution shift for image classification is the compositional nature of images. Specifically in addition to the main object or component(s) determining the label some other image components usually exist which may lead to the shift of input distribution between train and test environments. More importantly these components may have spurious correlations with the label. To address this issue we propose Decompose-and-Compose (DaC) which improves robustness to correlation shift by a compositional approach based on combining elements of images. Based on our observations models trained with ERM usually highly attend to either the causal components or the components having a high spurious correlation with the label (especially in datapoints on which models have a high confidence). In fact according to the amount of spurious correlation and the easiness of classification based on the causal or non-causal components the model usually attends to one of these more (on samples with high confidence). Following this we first try to identify the causal components of images using class activation maps of models trained with ERM.
Following this we first try to identify the causal components of images using class activation maps of models trained with ERM. Afterward we intervene on images by combining them and retraining the model on the augmented data including the counterfactual ones. This work proposes a group-balancing method by intervening on images without requiring group labels or information regarding the spurious features during training. The method has an overall better worst group accuracy compared to previous methods with the same amount of supervision on the group labels in correlation shift. Our code is available at https://github.com/fhn98/DaC.
In recent years there has been significant progress in the development of text-to-image generative models. Evaluating the quality of the generative models is one essential step in the development process. Unfortunately the evaluation process could consume a significant amount of computational resources making the required periodic evaluation of model performance (e.g. monitoring training progress) impractical. Therefore we seek to improve the evaluation efficiency by selecting the representative subset of the text-image dataset. We systematically investigate the design choices including the selection criteria (textural features or imagebased metrics) and the selection granularity (prompt-level or set-level). We find that the insights from prior work on subset selection for training data do not generalize to this problem and we propose FlashEval an iterative search algorithm tailored to evaluation data selection. We demonstrate the effectiveness of FlashEval on ranking diffusion models with various configurations including architectures quantization levels and sampler schedules on COCO and DiffusionDB datasets. Our searched 50-item subset could achieve comparable evaluation quality to the randomly sampled 500-item subset for COCO annotations on unseen models achieving a 10x evaluation speedup.
Our searched 50-item subset could achieve comparable evaluation quality to the randomly sampled 500-item subset for COCO annotations on unseen models achieving a 10x evaluation speedup. We release the condensed subset of these commonly used datasets to help facilitate diffusion algorithm design and evaluation and open-source FlashEval as a tool for condensing future datasets accessible at https://github.com/thu-nics/FlashEval.
This paper presents a novel zero-shot method for jointly denoising and enhancing real-word low-light images. The proposed method is independent of training data and noise distribution. Guided by illumination we integrate denoising and enhancing processes seamlessly enabling end-to-end training. Pairs of downsampled images are extracted from a single original low-light image and processed to preliminarily reduce noise. Based on the smoothness of illumination near-authentic illumination can be estimated from the denoised low-light image. Specifically the illumination is constrained by the denoised image's brightness uniformly amplifying pixels to raise overall brightness to normal-light level. We simultaneously restrict the illumination by scaling each pixel of the denoised image based on its intensity controlling the enhancement amplitude for different pixels. Applying the illumination to the original low-light image yields an adaptively enhanced reflection. This prevents under-enhancement and localized overexposure. Notably we concatenate the reflection with the illumination preserving their computational relationship to ultimately remove noise from the original low-light image in the form of reflection. This provides sufficient image information for the denoising procedure without changing the noise characteristics. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods.
This provides sufficient image information for the denoising procedure without changing the noise characteristics. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods. The source code is available at https://github.com/Doyle59217/ZeroIG.