mtybilly/Instruct2DS huggingface dataset:
What Sketch Explainability Really Means for Downstream Tasks?
Authors: Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Tao Xiang, Yi-Zhe Song Hmrishav Bandyopadhyay
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we explore the unique modality of sketch for explainability emphasising the profound impact of human strokes compared to conventional pixel-oriented studies. Beyond explanations of network behavior we discern the genuine implications of explainability across diverse downstream sketch-related tasks. We propose a lightweight and portable explainability solution -- a seamless plugin that integrates effortlessly with any pre-trained model eliminating the need for re-training. Demonstrating its adaptability we present four applications: highly studied retrieval and generation and completely novel assisted drawing and sketch adversarial attacks. The centrepiece to our solution is a stroke-level attribution map that takes different forms when linked with downstream tasks. By addressing the inherent non-differentiability of rasterisation we enable explanations at both coarse stroke level (SLA) and partial stroke level (P-SLA) each with its advantages for specific downstream tasks.
Neural Exposure Fusion for High-Dynamic Range Object Detection
Authors: Maximilian Bömer, Fahim Mannan, Felix Heide Emmanuel Onzon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Computer vision in unconstrained outdoor scenarios must tackle challenging high dynamic range (HDR) scenes and rapidly changing illumination conditions. Existing methods address this problem with multi-capture HDR sensors and a hardware image signal processor (ISP) that produces a single fused image as input to a downstream neural network. The output of the HDR sensor is a set of low dynamic range (LDR) exposures and the fusion in the ISP is performed in image space and typically optimized for human perception on a display. Preferring tonemapped content with smooth transition regions over detail (and noise) in the resulting image this image fusion does typically not preserve all information from the LDR exposures that may be essential for downstream computer vision tasks. In this work we depart from conventional HDR image fusion and propose a learned task-driven fusion in the feature domain. Instead of using a single companded image we introduce a novel local cross-attention fusion mechanism that exploits semantic features from all exposures learned in an end-to-end fashion with supervision from downstream detection losses. The proposed method outperforms all tested conventional HDR exposure fusion and auto-exposure methods in challenging automotive HDR scenarios.
EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Priors
Authors: Minda Zhao, Chaoyi Zhao, Xinyue Liang, Lincheng Li, Zeng Zhao, Changjie Fan, Xiaowei Zhou, Xin Yu Zhipeng Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: While image diffusion models have made significant progress in text-driven 3D content creation they often fail to accurately capture the intended meaning of text prompts especially for view information. This limitation leads to the Janus problem where multi-faced 3D models are generated under the guidance of such diffusion models. In this paper we propose a robust high-quality 3D content generation pipeline by exploiting orthogonal-view image guidance. First we introduce a novel 2D diffusion model that generates an image consisting of four orthogonal-view sub-images based on the given text prompt. Then the 3D content is created using this diffusion model. Notably the generated orthogonal-view image provides strong geometric structure priors and thus improves 3D consistency. As a result it effectively resolves the Janus problem and significantly enhances the quality of 3D content creation. Additionally we present a 3D synthesis fusion network that can further improve the details of the generated 3D contents. Both quantitative and qualitative evaluations demonstrate that our method surpasses previous text-to-3D techniques. Project page: https://efficientdreamer.github.io.
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models
Authors: Xinyu Zhang, Shuai Li, Yang Gao, Aimin Hao, Xia Hou, Chenglizhao Chen, Ning Li, Hong Qin Wenfeng Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: To date the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehensive physics-centric model supporting real-world dynamics. To ameliorate this paper advocates HOIAnimator a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on human movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other making the overall motion more realistic. Additionally we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models ensuring that the animations are smooth and unified; (2) We devise an Interaction Contact Field (ICF) a sophisticated model that implicitly captures the essence of HOIs. Beyond mere predictive contact points the ICF assesses the proximity of human and object to their respective environment informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOIanimator's superior ability to produce dynamic context-aware animations that surpass existing benchmarks in text-driven animation synthesis.
SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
Authors: Wentao Hu, Yue Shi, Xiangyu Zhu, Xiaomei Zhang, Hao Zhao, Jun He, Hongyan Liu, Zhaoxin Fan Ziqiao Peng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Achieving high synchronization in the synthesis of realistic speech-driven talking head videos presents a significant challenge. Traditional Generative Adversarial Networks (GAN) struggle to maintain consistent facial identity while Neural Radiance Fields (NeRF) methods although they can address this issue often produce mismatched lip movements inadequate facial expressions and unstable head poses. A lifelike talking head requires synchronized coordination of subject identity lip movements facial expressions and head poses. The absence of these synchronizations is a fundamental flaw leading to unrealistic and artificial outcomes. To address the critical issue of synchronization identified as the "devil" in creating realistic talking heads we introduce SyncTalk. This NeRF-based method effectively maintains subject identity enhancing synchronization and realism in talking head synthesis. SyncTalk employs a Face-Sync Controller to align lip movements with speech and innovatively uses a 3D facial blendshape model to capture accurate facial expressions. Our HeadSync Stabilizer optimizes head poses achieving more natural head movements. The Portrait-Sync Generator restores hair details and blends the generated head with the torso for a seamless visual experience. Extensive experiments and user studies demonstrate that SyncTalk outperforms state-of-the-art methods in synchronization and realism. We recommend watching the supplementary video: https://ziqiaopeng.github.io/synctalk
SFOD: Spiking Fusion Object Detector
Authors: Wei Zhang, Changsong Liu, Mingyang Li, Wenrui Lu Yimeng Fan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Event cameras characterized by high temporal resolution high dynamic range low power consumption and high pixel bandwidth offer unique capabilities for object detection in specialized contexts. Despite these advantages the inherent sparsity and asynchrony of event data pose challenges to existing object detection algorithms. Spiking Neural Networks (SNNs) inspired by the way the human brain codes and processes information offer a potential solution to these difficulties. However their performance in object detection using event cameras is limited in current implementations. In this paper we propose the Spiking Fusion Object Detector (SFOD) a simple and efficient approach to SNN-based object detection. Specifically we design a Spiking Fusion Module achieving the first-time fusion of feature maps from different scales in SNNs applied to event cameras. Additionally through integrating our analysis and experiments conducted during the pretraining of the backbone network on the NCAR dataset we delve deeply into the impact of spiking decoding strategies and loss functions on model performance. Thereby we establish state-of-the-art classification results based on SNNs achieving 93.7% accuracy on the NCAR dataset. Experimental results on the GEN1 detection dataset demonstrate that the SFOD achieves a state-of-the-art mAP of 32.1% outperforming existing SNN-based approaches. Our research not only underscores the potential of SNNs in object detection with event cameras but also propels the advancement of SNNs. Code is available at https://github.com/yimeng-fan/SFOD.
Detector-Free Structure from Motion
Authors: Jiaming Sun, Yifan Wang, Sida Peng, Qixing Huang, Hujun Bao, Xiaowei Zhou Xingyi He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful detection of repeatable keypoints across multiple views as the first step which is difficult for texture-poor scenes and poor keypoint detection may break down the whole SfM system. We propose a detector-free SfM framework to draw benefits from the recent success of detector-free matchers to avoid the early determination of keypoints while solving the multi-view inconsistency issue of detector-free matchers. Specifically our framework first reconstructs a coarse SfM model from quantized detector-free matches. Then it refines the model by a novel iterative refinement pipeline which iterates between an attention-based multi-view matching module to refine feature tracks and a geometry refinement module to improve the reconstruction accuracy. Experiments demonstrate that the proposed framework outperforms existing detector-based SfM systems on common benchmark datasets. We also collect a texture-poor SfM dataset to demonstrate the capability of our framework to reconstruct texture-poor scenes. Based on this framework we take first place in Image Matching Challenge 2023.
CG-HOI: Contact-Guided 3D Human-Object Interaction Generation
Authors: Angela Dai Christian Diller
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Towards Surveillance Video-and-Language Understanding: New Dataset Baselines and Challenges
Authors: Xuange Zhang, Kun Liu, Bo Liu, Chen Chen, Jian Jin, Zhenzhen Jiao Tongtong Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring
Authors: Qingli Li, Yan Wang Xintian Mao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Learning to Remove Wrinkled Transparent Film with Polarized Prior
Authors: Ruizheng Wu, Xiaogang Xu, Sixing Hu, Ying-Cong Chen Jiaqi Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
OpenEQA: Embodied Question Answering in the Era of Foundation Models
Authors: Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul Mcvay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Alexander Sax, Aravind Rajeswaran Arjun Majumdar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation
Authors: Haonan Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning
Authors: Linfeng Tang, Xinyu Xiang, Xuhui Zuo, Jiayi Ma Hao Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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. 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.
Querying as Prompt: Parameter-Efficient Learning for Multimodal Language Model
Authors: Jing Huang, Ming Kong, Luyuan Chen, Qiang Zhu Tian Liang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Neural Super-Resolution for Real-time Rendering with Radiance Demodulation
Authors: Ziling Chen, Xiaolong Wu, Lu Wang, Beibei Wang, Lei Zhang Jia Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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. Code is available at: \href https://github.com/Riga2/NSRD  https://github.com/Riga2/NSRD .
Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
Authors: Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, Xiaogang Jin Ziyi Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors
Authors: Pengpeng Liang, Erkang Cheng Haoxuanye Ji
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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. 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.
Continual Forgetting for Pre-trained Vision Models
Authors: Bolin Ni, Junsong Fan, Yuxi Wang, Yuntao Chen, Gaofeng Meng, Zhaoxiang Zhang Hongbo Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark
Authors: Israel D. Gebru, Christian Richardt, Anurag Kumar, William Laney, Andrew Owens, Alexander Richard Ziyang Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
A Generative Approach for Wikipedia-Scale Visual Entity Recognition
Authors: Ahmet Iscen, Alireza Fathi, Cordelia Schmid Mathilde Caron
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
A Physics-informed Low-rank Deep Neural Network for Blind and Universal Lens Aberration Correction
Authors: Runzhao Yang, Weihang Zhang, Jinli Suo, Qionghai Dai Jin Gong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Open-Vocabulary Object 6D Pose Estimation
Authors: Davide Boscaini, Changjae Oh, Andrea Cavallaro, Fabio Poiesi Jaime Corsetti
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Plug and Play Active Learning for Object Detection
Authors: Lichao Huang, Elliot J. Crowley Chenhongyi Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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. PPAL makes no change to model architectures or detector training pipelines; hence it can be easily generalized to different object detectors. We benchmark PPAL on the MS-COCO and Pascal VOC datasets using different detector architectures and show that our method outperforms prior work by a large margin. Code is available at https://github.com/ChenhongyiYang/PPAL
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
Authors: Yifei Min, Weicheng Dai, Jasjeet S. Sekhon, Lawrence Staib, James S. Duncan Chenyu You
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Fine-tuning pre-trained vision-language models like CLIP has yielded success on diverse downstream tasks. However several pain points persist for this paradigm: (i) directly tuning entire pre-trained models becomes both time-intensive and computationally costly. Additionally these tuned models tend to become highly specialized limiting their practicality for real-world deployment; (ii) recent studies indicate that pre-trained vision-language classifiers may overly depend on spurious features -- patterns that correlate with the target in training data but are not related to the true labeling function; and (iii) existing studies on mitigating the reliance on spurious features largely based on the assumption that we can identify such features does not provide definitive assurance for real-world applications. As a piloting study this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation. To this end we systematically study the existence of spurious correlation on CLIP and CILP+ERM. We first following recent work on Deep Feature Reweighting (DFR) verify that last-layer retraining can greatly improve group robustness on pretrained CLIP. In view of them we advocate a lightweight representation calibration method for fine-tuning CLIP by first generating a calibration set using the pretrained CLIP and then calibrating representations of samples within this set through contrastive learning all without the need for group labels. Extensive experiments and in-depth visualizations on several benchmarks validate the effectiveness of our proposals largely reducing reliance and significantly boosting the model generalization.
LiSA: LiDAR Localization with Semantic Awareness
Authors: Zijun Li, Wen Li, Zhipeng Cai, Chenglu Wen, Yu Zang, Matthias Muller, Cheng Wang Bochun Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: LiDAR localization is a fundamental task in robotics and computer vision which estimates the pose of a LiDAR point cloud within a global map. Scene Coordinate Regression (SCR) has demonstrated state-of-the-art performance in this task. In SCR a scene is represented as a neural network which outputs the world coordinates for each point in the input point cloud. However SCR treats all points equally during localization ignoring the fact that not all objects are beneficial for localization. For example dynamic objects and repeating structures often negatively impact SCR. To address this problem we introduce LiSA the first method that incorporates semantic awareness into SCR to boost the localization robustness and accuracy. To avoid extra computation or network parameters during inference we distill the knowledge from a segmentation model to the original SCR network. Experiments show the superior performance of LiSA on standard LiDAR localization benchmarks compared to state-of-the-art methods. Applying knowledge distillation not only preserves high efficiency but also achieves higher localization accuracy than introducing extra semantic segmentation modules. We also analyze the benefit of semantic information for LiDAR localization. Our code is released at https://github.com/Ybchun/LiSA.
MMM: Generative Masked Motion Model
Authors: Pu Wang, Minwoo Lee, Chen Chen Ekkasit Pinyoanuntapong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advances in text-to-motion generation using diffusion and autoregressive models have shown promising results. However these models often suffer from a trade-off between real-time performance high fidelity and motion editability. To address this gap we introduce MMM a novel yet simple motion generation paradigm based on Masked Motion Model. MMM consists of two key components: (1) a motion tokenizer that transforms 3D human motion into a sequence of discrete tokens in latent space and (2) a conditional masked motion transformer that learns to predict randomly masked motion tokens conditioned on the pre-computed text tokens. By attending to motion and text tokens in all directions MMM explicitly captures inherent dependency among motion tokens and semantic mapping between motion and text tokens. During inference this allows parallel and iterative decoding of multiple motion tokens that are highly consistent with fine-grained text descriptions therefore simultaneously achieving high-fidelity and high-speed motion generation. In addition MMM has innate motion editability. By simply placing mask tokens in the place that needs editing MMM automatically fills the gaps while guaranteeing smooth transitions between editing and non-editing parts. Extensive experiments on the HumanML3D and KIT-ML datasets demonstrate that MMM surpasses current leading methods in generating high-quality motion (evidenced by superior FID scores of 0.08 and 0.429) while offering advanced editing features such as body-part modification motion in-betweening and the synthesis of long motion sequences. In addition MMM is two orders of magnitude faster on a single mid-range GPU than editable motion diffusion models. Our project page is available at https://exitudio.github.io/MMM-page/.
PEGASUS: Personalized Generative 3D Avatars with Composable Attributes
Authors: Byungjun Kim, Hanbyul Joo Hyunsoo Cha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present PEGASUS a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selectively alter the facial attributes (e.g. hair or nose) while preserving the identity. Our approach consists of two stages: synthetic database generation and constructing a personalized generative avatar. We generate a synthetic video collection of the target identity with varying facial attributes where the videos are synthesized by borrowing the attributes from monocular videos of diverse identities. Then we build a person-specific generative 3D avatar that can modify its attributes continuously while preserving its identity. Through extensive experiments we demonstrate that our method of generating a synthetic database and creating a 3D generative avatar is the most effective in preserving identity while achieving high realism. Subsequently we introduce a zero-shot approach to achieve the same goal of generative modeling more efficiently by leveraging a previously constructed personalized generative model.
LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Authors: Yuxuan Hu, Letian Wang, Guanglu Song, Steven L. Waslander, Yu Liu, Hongsheng Li Hao Shao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite significant recent progress in the field of autonomous driving modern methods still struggle and can incur serious accidents when encountering long-tail unforeseen events and challenging urban scenarios. On the one hand large language models (LLM) have shown impressive reasoning capabilities that approach "Artificial General Intelligence". On the other hand previous autonomous driving methods tend to rely on limited-format inputs (e.g. sensor data and navigation waypoints) restricting the vehicle's ability to understand language information and interact with humans. To this end this paper introduces LMDrive a novel language-guided end-to-end closed-loop autonomous driving framework. LMDrive uniquely processes and integrates multi-modal sensor data with natural language instructions enabling interaction with humans and navigation software in realistic instructional settings. To facilitate further research in language-based closed-loop autonomous driving we also publicly release the corresponding dataset which includes approximately 64K instruction-following data clips and the LangAuto benchmark that tests the system's ability to handle complex instructions and challenging driving scenarios. Extensive closed-loop experiments are conducted to demonstrate LMDrive's effectiveness. To the best of our knowledge we're the very first work to leverage LLMs for closed-loop end-to-end autonomous driving. Code is available at https://github.com/opendilab/LMDrive