CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
We propose CG-HOI the first method to address the task of generating dynamic 3D human-object interactions (HOIs) from text. We model the motion of both human and object in an interdependent fashion as semantically rich human motion rarely happens in isolation without any interactions. Our key insight is that explicitly modeling contact between the human body surface and object geometry can be used as strong proxy guidance both during training and inference. Using this guidance to bridge human and object motion enables generating more realistic and physically plausible interaction sequences where the human body and corresponding object move in a coherent manner. Our method first learns to model human motion object motion and contact in a joint diffusion process inter-correlated through cross-attention. We then leverage this learned contact for guidance during inference to synthesize realistic and coherent HOIs. Extensive evaluation shows that our joint contact-based human-object interaction approach generates realistic and physically plausible sequences and we show two applications highlighting the capabilities of our method. Conditioned on a given object trajectory we can generate the corresponding human motion without re-training demonstrating strong human-object interdependency learning. Our approach is also flexible and can be applied to static real-world 3D scene scans.
Surveillance videos are important for public security. However current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory semantic understanding although they have obtained considerable performance. To address this issue we propose a new research direction of surveillance video-and-language understanding(VALU) and construct the first multimodal surveillance video dataset. We manually annotate the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset UCA (UCF-Crime Annotation) contains 23542 sentences with an average length of 20 words and its annotated videos are as long as 110.7 hours. Furthermore we benchmark SOTA models for four multimodal tasks on this newly created dataset which serve as new baselines for surveillance VALU. Through experiments we find that mainstream models used in previously public datasets perform poorly on surveillance video demonstrating new challenges in surveillance VALU. We also conducted experiments on multimodal anomaly detection. These results demonstrate that our multimodal surveillance learning can improve the performance of anomaly detection. All the experiments highlight the necessity of constructing this dataset to advance surveillance AI.
Despite the recent progress in enhancing the efficacy of image deblurring the limited decoding capability constrains the upper limit of State-Of-The-Art (SOTA) methods. This paper proposes a pioneering work Adaptive Patch Exiting Reversible Decoder (AdaRevD) to explore their insufficient decoding capability. By inheriting the weights of the well-trained encoder we refactor a reversible decoder which scales up the single-decoder training to multi-decoder training while remaining GPU memory-friendly. Meanwhile we show that our reversible structure gradually disentangles high-level degradation degree and low-level blur pattern (residual of the blur image and its sharp counterpart) from compact degradation representation. Besides due to the spatially-variant motion blur kernels different blur patches have various deblurring difficulties. We further introduce a classifier to learn the degradation degree of image patches enabling them to exit at different sub-decoders for speedup. Experiments show that our AdaRevD pushes the limit of image deblurring e.g. achieving 34.60 dB in PSNR on GoPro dataset.
In this paper we study a new problem Film Removal (FR) which attempts to remove the interference of wrinkled transparent films and reconstruct the original information under films for industrial recognition systems. We first physically model the imaging of industrial materials covered by the film. Considering the specular highlight from the film can be effectively recorded by the polarized camera we build a practical dataset with polarization information containing paired data with and without transparent film. We aim to remove interference from the film (specular highlights and other degradations) with an end-to-end framework. To locate the specular highlight we use an angle estimation network to optimize the polarization angle with the minimized specular highlight. The image with minimized specular highlight is set as a prior for supporting the reconstruction network. Based on the prior and the polarized images the reconstruction network can decouple all degradations from the film. Extensive experiments show that our framework achieves SOTA performance in both image reconstruction and industrial downstream tasks. Our code will be released at https://github.com/jqtangust/FilmRemoval.
We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory exemplified by agents on smart glasses or by actively exploring the environment as in the case of mobile robots. We accompany our formulation with OpenEQA -- the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol we evaluate several state-of-the-art foundation models including GPT-4V and find that they significantly lag behind human-level performance. Consequently OpenEQA stands out as a straightforward measurable and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI conversational agents and world models.
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images novel challenges arise with a nuanced task of "identity fine editing" - precisely modifying specific features of a subject while maintaining its inherent identity and context. Existing personalization methods either require time-consuming optimization or learning additional encoders adept in "identity re-contextualization". However they often struggle with detailed and sensitive tasks like human face editing. To address these challenges we introduce DreamSalon a noise-guided staged-editing framework uniquely focusing on detailed image manipulations and identity-context preservation. By discerning editing and boosting stages via the frequency and gradient of predicted noises DreamSalon first performs detailed manipulations on specific features in the editing stage guided by high-frequency information and then employs stochastic denoising in the boosting stage to improve image quality. For more precise editing DreamSalon semantically mixes source and target textual prompts guided by differences in their embedding covariances to direct the model's focus on specific manipulation areas. Our experiments demonstrate DreamSalon's ability to efficiently and faithfully edit fine details on human faces outperforming existing methods both qualitatively and quantitatively.
We propose a controllable visual enhancer named DDBF which is based on cross-modal conditional adversarial learning and aims to dispel darkness and achieve better visible and infrared modalities fusion. Specifically a guided restoration module (GRM) is firstly designed to enhance weakened information in the low-light visible modality. The GRM utilizes the light-invariant high-contrast characteristics of the infrared modality as the central target distribution and constructs a multi-level conditional adversarial sample set to enable continuous controlled brightness enhancement of visible images. Then we develop an information fusion module (IFM) to integrate the advantageous features of the enhanced visible image and the infrared image. Thanks to customized explicit information preservation and hue fidelity constraints the IFM produces visually pleasing results with rich textures significant contrast and vivid colors. The brightened visible image and the final fused image compose the dual output of our DDBF to meet the diverse visual preferences of users. We evaluate DDBF on the public datasets achieving state-of-the-art performances of low-light enhancement and information integration that is available for both day and night scenarios. The experiments also demonstrate that our DDBF is effective in improving decision accuracy for object detection and semantic segmentation.
We evaluate DDBF on the public datasets achieving state-of-the-art performances of low-light enhancement and information integration that is available for both day and night scenarios. The experiments also demonstrate that our DDBF is effective in improving decision accuracy for object detection and semantic segmentation. Moreover we offer a user-friendly interface for the convenient application of our model. The code is publicly available at https://github.com/HaoZhang1018/DDBF.
Recent advancements in language models pre-trained on large-scale corpora have significantly propelled developments in the NLP domain and advanced progress in multimodal tasks. In this paper we propose a Parameter-Efficient multimodal language model learning strategy named QaP (Querying as Prompt). Its core innovation is a novel modality-bridging method that allows a set of modality-specific queries to be input as soft prompts into a frozen pre-trained language model. Specifically we introduce an efficient Text-Conditioned Resampler that is easy to incorporate into the language models which enables adaptive injection of text-related multimodal information at different levels of the model through query learning. This approach effectively bridges multimodal information to the language models while fully leveraging its token fusion and representation potential. We validated our method across four datasets in three distinct multimodal tasks. The results demonstrate that our QaP multimodal language model achieves state-of-the-art performance in various tasks with training only 4.6% parameters.
This work breaks through the Base-New Tradeoff (BNT) dilemma in prompt tuning i.e. the better the tuned model generalizes to the base (or target) task the worse it generalizes to new tasks and vice versa. Specifically through an in-depth analysis of the learned features of the base and new tasks we observe that the BNT stems from a channel bias issue--the vast majority of feature channels are occupied by base-specific knowledge leading to the collapse of task-shared knowledge important to new tasks. To address this we propose the Decoupled Prompt Tuning (DePT) framework which decouples base-specific knowledge from feature channels into an isolated feature space during prompt tuning so as to maximally preserve task-shared knowledge in the original feature space for achieving better zero-shot generalization on new tasks. Importantly our DePT is orthogonal to existing prompt tuning approaches and can enhance them with negligible additional computational cost. Extensive experiments on several datasets show the flexibility and effectiveness of DePT.
It is time-consuming to render high-resolution images in applications such as video games and virtual reality and thus super-resolution technologies become increasingly popular for real-time rendering. However it is challenging to preserve sharp texture details keep the temporal stability and avoid the ghosting artifacts in real-time super-resolution rendering. To address this issue we introduce radiance demodulation to separate the rendered image or radiance into a lighting component and a material component considering the fact that the light component is smoother than the rendered image so that the high-resolution material component with detailed textures can be easily obtained. We perform the super-resolution on the lighting component only and re-modulate it with the high-resolution material component to obtain the final super-resolution image with more texture details. A reliable warping module is proposed by explicitly marking the occluded regions to avoid the ghosting artifacts. To further enhance the temporal stability we design a frame-recurrent neural network and a temporal loss to aggregate the previous and current frames which can better capture the spatial-temporal consistency among reconstructed frames. As a result our method is able to produce temporally stable results in real-time rendering with high-quality details even in the challenging 4 x4 super-resolution scenarios.
As a result our method is able to produce temporally stable results in real-time rendering with high-quality details even in the challenging 4 x4 super-resolution scenarios. Code is available at: \href https://github.com/Riga2/NSRD  https://github.com/Riga2/NSRD .
Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction. Nonetheless cutting-edge dynamic neural rendering methods rely heavily on these implicit representations which frequently struggle to capture the intricate details of objects in the scene. Furthermore implicit methods have difficulty achieving real-time rendering in general dynamic scenes limiting their use in a variety of tasks. To address the issues we propose a deformable 3D Gaussians splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space with a deformation field to model monocular dynamic scenes. We also introduce an annealing smoothing training mechanism with no extra overhead which can mitigate the impact of inaccurate poses on the smoothness of time interpolation tasks in real-world scenes. Through a differential Gaussian rasterizer the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed. Experiments show that our method outperforms existing methods significantly in terms of both rendering quality and speed making it well-suited for tasks such as novel-view synthesis time interpolation and real-time rendering.
Multi-camera-based 3D object detection has made notable progress in the past several years. However we observe that there are cases (e.g. faraway regions) in which popular 2D object detectors are more reliable than state-of-the-art 3D detectors. In this paper to improve the performance of query-based 3D object detectors we present a novel query generating approach termed QAF2D which infers 3D query anchors from 2D detection results. A 2D bounding box of an object in an image is lifted to a set of 3D anchors by associating each sampled point within the box with depth yaw angle and size candidates. Then the validity of each 3D anchor is verified by comparing its projection in the image with its corresponding 2D box and only valid anchors are kept and used to construct queries. The class information of the 2D bounding box associated with each query is also utilized to match the predicted boxes with ground truth for the set-based loss. The image feature extraction backbone is shared between the 3D detector and 2D detector by adding a small number of prompt parameters.
The class information of the 2D bounding box associated with each query is also utilized to match the predicted boxes with ground truth for the set-based loss. The image feature extraction backbone is shared between the 3D detector and 2D detector by adding a small number of prompt parameters. We integrate QAF2D into three popular query-based 3D object detectors and carry out comprehensive evaluations on the nuScenes dataset. The largest improvement that QAF2D can bring about on the nuScenes validation subset is 2.3% NDS and 2.7% mAP. Code is available at https://github.com/max-vision/QAF2D.
For privacy and security concerns the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios erasure requests originate at any time from both users and model owners. These requests usually form a sequence. Therefore under such a setting selective information is expected to be continuously removed from a pre-trained model while maintaining the rest. We define this problem as continual forgetting and identify two key challenges. (i) For unwanted knowledge efficient and effective deleting is crucial. (ii) For remaining knowledge the impact brought by the forgetting procedure should be minimal. To address them we propose Group Sparse LoRA (GS-LoRA). Specifically towards (i) we use LoRA modules to fine-tune the FFN layers in Transformer blocks for each forgetting task independently and towards (ii) a simple group sparse regularization is adopted enabling automatic selection of specific LoRA groups and zeroing out the others. GS-LoRA is effective parameter-efficient data-efficient and easy to implement. We conduct extensive experiments on face recognition object detection and image classification and demonstrate that GS-LoRA manages to forget specific classes with minimal impact on other classes. Codes will be released on https://github.com/bjzhb666/GS-LoRA.
We present a new dataset called Real Acoustic Fields (RAF) that captures real acoustic room data from multiple modalities. The dataset includes high-quality and densely captured room impulse response data paired with multi-view images and precise 6DoF pose tracking data for sound emitters and listeners in the rooms. We used this dataset to evaluate existing methods for novel-view acoustic synthesis and impulse response generation which previously relied on synthetic data. In our evaluation we thoroughly assessed existing audio and audio-visual models against multiple criteria and proposed settings to enhance their performance on real-world data. We also conducted experiments to investigate the impact of incorporating visual data (i.e. images and depth) into neural acoustic field models. Additionally we demonstrated the effectiveness of a simple sim2real approach where a model is pre-trained with simulated data and fine-tuned with sparse real-world data resulting in significant improvements in the few-shot learning approach. RAF is the first dataset to provide densely captured room acoustic data making it an ideal resource for researchers working on audio and audio-visual neural acoustic field modeling techniques.
In this paper we address web-scale visual entity recognition specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way of approaching a problem of such scale is using dual encoder models (e.g. CLIP) where all the entity names and query images are embedded into a unified space paving the way for an approximate kNN search. Alternatively it is also possible to re-purpose a captioning model to directly generate the entity names for a given image. In contrast we introduce a novel Generative Entity Recognition (GER) framework which given an input image learns to auto-regressively decode a semantic and discriminative "code" identifying the target entity. Our experiments demonstrate the efficacy of this GER paradigm showcasing state-of-the-art performance on the challenging OVEN benchmark. GER surpasses strong captioning dual-encoder visual matching and hierarchical classification baselines affirming its advantage in tackling the complexities of web-scale recognition.
High-end lenses although offering high-quality images suffer from both insufficient affordability and bulky design which hamper their applications in low-budget scenarios or on low-payload platforms. A flexible scheme is to tackle the optical aberration of low-end lenses computationally. However it is highly demanded but quite challenging to build a general model capable of handling non-stationary aberrations and covering diverse lenses especially in a blind manner. To address this issue we propose a universal solution by extensively utilizing the physical properties of camera lenses: (i) reducing the complexity of lens aberrations i.e. lens-specific non-stationary blur by warping annual-ring-shaped sub-images into rectangular stripes to transform non-uniform degenerations into a uniform one (ii) building a low-dimensional non-negative orthogonal representation of lens blur kernels to cover diverse lenses; (iii) designing a decoupling network to decompose the input low-quality image into several components degenerated by above kernel bases and applying corresponding pre-trained deconvolution networks to reverse the degeneration. Benefiting from the proper incorporation of lenses' physical properties and unique network design the proposed method achieves superb imaging quality wide applicability for various lenses high running efficiency and is totally free of kernel calibration. These advantages bring great potential for scenarios requiring lightweight high-quality photography.
We introduce the new setting of open-vocabulary object 6D pose estimation in which a textual prompt is used to specify the object of interest. In contrast to existing approaches in our setting (i) the object of interest is specified solely through the textual prompt (ii) no object model (e.g. CAD or video sequence) is required at inference and (iii) the object is imaged from two RGBD viewpoints of different scenes. To operate in this setting we introduce a novel approach that leverages a Vision-Language Model to segment the object of interest from the scenes and to estimate its relative 6D pose. The key of our approach is a carefully devised strategy to fuse object-level information provided by the prompt with local image features resulting in a feature space that can generalize to novel concepts. We validate our approach on a new benchmark based on two popular datasets REAL275 and Toyota-Light which collectively encompass 34 object instances appearing in four thousand image pairs. The results demonstrate that our approach outperforms both a well-established hand-crafted method and a recent deep learning-based baseline in estimating the relative 6D pose of objects in different scenes. Code and dataset are available at https://jcorsetti.github.io/oryon.
Annotating datasets for object detection is an expensive and time-consuming endeavor. To minimize this burden active learning (AL) techniques are employed to select the most informative samples for annotation within a constrained "annotation budget". Traditional AL strategies typically rely on model uncertainty or sample diversity for query sampling while more advanced methods have focused on developing AL-specific object detector architectures to enhance performance. However these specialized approaches are not readily adaptable to different object detectors due to the significant engineering effort required for integration. To overcome this challenge we introduce Plug and Play Active Learning (PPAL) a simple and effective AL strategy for object detection. PPAL is a two-stage method comprising uncertainty-based and diversity-based sampling phases. In the first stage our Difficulty Calibrated Uncertainty Sampling leverage a category-wise difficulty coefficient that combines both classification and localisation difficulties to re-weight instance uncertainties from which we sample a candidate pool for the subsequent diversity-based sampling. In the second stage we propose Category Conditioned Matching Similarity to better compute the similarities of multi-instance images as ensembles of their instance similarities which is used by the k-Means++ algorithm to sample the final AL queries.