mtybilly/Instruct2DS huggingface dataset:
Multi-Object Tracking in the Dark
Authors: Kang Ma, Qiankun Liu, Yunhao Zou, Ying Fu Xinzhe Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Low-light scenes are prevalent in real-world applications (e.g. autonomous driving and surveillance at night). Recently multi-object tracking in various practical use cases have received much attention but multi-object tracking in dark scenes is rarely considered. In this paper we focus on multi-object tracking in dark scenes. To address the lack of datasets we first build a Low-light Multi-Object Tracking (LMOT) dataset. LMOT provides well-aligned low-light video pairs captured by our dual-camera system and high-quality multi-object tracking annotations for all videos. Then we propose a low-light multi-object tracking method termed as LTrack. We introduce the adaptive low-pass downsample module to enhance low-frequency components of images outside the sensor noises. The degradation suppression learning strategy enables the model to learn invariant information under noise disturbance and image quality degradation. These components improve the robustness of multi-object tracking in dark scenes. We conducted a comprehensive analysis of our LMOT dataset and proposed LTrack. Experimental results demonstrate the superiority of the proposed method and its competitiveness in real night low-light scenes. Dataset and Code: https://github.com/ying-fu/LMOT
UniHuman: A Unified Model For Editing Human Images in the Wild
Authors: Qing Liu, Krishna Kumar Singh, Yilin Wang, Jianming Zhang, Bryan A. Plummer, Zhe Lin Nannan Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human image editing includes tasks like changing a person's pose their clothing or editing the image according to a text prompt. However prior work often tackles these tasks separately overlooking the benefit of mutual reinforcement from learning them jointly. In this paper we propose UniHuman a unified model that addresses multiple facets of human image editing in real-world settings. To enhance the model's generation quality and generalization capacity we leverage guidance from human visual encoders and introduce a lightweight pose-warping module that can exploit different pose representations accommodating unseen textures and patterns. Furthermore to bridge the disparity between existing human editing benchmarks with real-world data we curated 400K high-quality human image-text pairs for training and collected 2K human images for out-of-domain testing both encompassing diverse clothing styles backgrounds and age groups. Experiments on both in-domain and out-of-domain test sets demonstrate that UniHuman outperforms task-specific models by a significant margin. In user studies UniHuman is preferred by the users in an average of 77% of cases. Our project is available at https://github.com/NannanLi999/UniHuman.
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model
Authors: Yue Yang, Kaipeng Zhang, Wenqi Shao, Yuxin Zhang, Yu Qiao, Ping Luo, Rongrong Ji Lirui Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image (T2I) generative models have attracted significant attention and found extensive applications within and beyond academic research. For example the Civitai community a platform for T2I innovation currently hosts an impressive array of 74492 distinct models. However this diversity presents a formidable challenge in selecting the most appropriate model and parameters a process that typically requires numerous trials. Drawing inspiration from the tool usage research of large language models (LLMs) we introduce DiffAgent an LLM agent designed to screen the accurate selection in seconds via API calls. DiffAgent leverages a novel two-stage training framework SFTA enabling it to accurately align T2I API responses with user input in accordance with human preferences. To train and evaluate DiffAgent's capabilities we present DABench a comprehensive dataset encompassing an extensive range of T2I APIs from the community. Our evaluations reveal that DiffAgent not only excels in identifying the appropriate T2I API but also underscores the effectiveness of the SFTA training framework. Codes are available at https://github.com/OpenGVLab/DiffAgent.
In Search of a Data Transformation That Accelerates Neural Field Training
Authors: Sangyoon Lee, Kwang In Kim, Jaeho Lee Junwon Seo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal. A key obstacle that prevents its widespread adoption is the encoding speed---generating neural fields requires an overfitting of a neural network which can take a significant number of SGD steps to reach the desired fidelity level. In this paper we delve into the impacts of data transformations on the speed of neural field training specifically focusing on how permuting pixel locations affect the convergence speed of SGD. Counterintuitively we find that randomly permuting the pixel locations can considerably accelerate the training. To explain this phenomenon we examine the neural field training through the lens of PSNR curves loss landscapes and error patterns. Our analyses suggest that the random pixel permutations remove the easy-to-fit patterns which facilitate easy optimization in the early stage but hinder capturing fine details of the signal.
Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis
Authors: Hayk Manukyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi Marianna Ohanyan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Zero-Painter a novel training-free framework for layout-conditional text-to-image synthesis that facilitates the creation of detailed and controlled imagery from textual prompts. Our method utilizes object masks and individual descriptions coupled with a global text prompt to generate images with high fidelity. Zero-Painter employs a two-stage process involving our novel Prompt-Adjusted Cross-Attention (PACA) and Region-Grouped Cross-Attention (ReGCA) blocks ensuring precise alignment of generated objects with textual prompts and mask shapes. Our extensive experiments demonstrate that Zero-Painter surpasses current state-of-the-art methods in preserving textual details and adhering to mask shapes. We will make the codes and the models publicly available.
DiffLoc: Diffusion Model for Outdoor LiDAR Localization
Authors: Yuyang Yang, Shangshu Yu, Guosheng Hu, Chenglu Wen, Ming Cheng, Cheng Wang Wen Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Absolute pose regression (APR) estimates global pose in an end-to-end manner achieving impressive results in learn-based LiDAR localization. However compared to the top-performing methods reliant on 3D-3D correspondence matching APR's accuracy still has room for improvement. We recognize APR's lack of robust features learning and iterative denoising process leads to suboptimal results. In this paper we propose DiffLoc a novel framework that formulates LiDAR localization as a conditional generation of poses. First we propose to utilize the foundation model and static-object-aware pool to learn robust features. Second we incorporate the iterative denoising process into APR via a diffusion model conditioned on the learned geometrically robust features. In addition due to the unique nature of diffusion models we propose to adapt our models to two additional applications: (1) using multiple inferences to evaluate pose uncertainty and (2) seamlessly introducing geometric constraints on denoising steps to improve prediction accuracy. Extensive experiments conducted on the Oxford Radar RobotCar and NCLT datasets demonstrate that DiffLoc outperforms better than the stateof-the-art methods. Especially on the NCLT dataset we achieve 35% and 34.7% improvement on position and orientation accuracy respectively. Our code is released at https://github.com/liw95/DiffLoc.
Towards 3D Vision with Low-Cost Single-Photon Cameras
Authors: Carter Sifferman, Sacha Jungerman, Yiquan Li, Mark Han, Michael Gleicher, Mohit Gupta, Yin Li Fangzhou Mu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature energy-efficient low-cost single-photon cameras. These cameras operating as time resolved image sensors illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process account for its non-idealities and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning and offers a step towards 3D vision with single-photon cameras.
WonderJourney: Going from Anywhere to Everywhere
Authors: Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T. Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, Charles Herrmann Hong-Xing Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce WonderJourney a modular framework for perpetual 3D scene generation. Unlike prior work on view generation that focuses on a single type of scenes we start at any user-provided location (by a text description or an image) and generate a journey through a long sequence of diverse yet coherently connected 3D scenes. We leverage an LLM to generate textual descriptions of the scenes in this journey a text-driven point cloud generation pipeline to make a compelling and coherent sequence of 3D scenes and a large VLM to verify the generated scenes. We show compelling diverse visual results across various scene types and styles forming imaginary "wonderjourneys". Project website: https://kovenyu.com/WonderJourney.
On Scaling Up a Multilingual Vision and Language Model
Authors: Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, AJ Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut Xi Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We explore the boundaries of scaling up a multilingual vision and language model both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks including multiple image-based captioning and question-answering tasks image-based document understanding and few-shot (in-context) learning as well as object detection video question answering and video captioning. Our model advances the state-of-the-art on most vision-and-language benchmarks considered (20+ of them). Finally we observe emerging capabilities such as complex counting and multilingual object detection tasks that are not explicitly in the training mix.
Day-Night Cross-domain Vehicle Re-identification
Authors: Jingong Chen, Aihua Zheng, Yong Wu, Yonglong Luo Hongchao Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Previous advances in vehicle re-identification (ReID) are mostly reported under favorable lighting conditions while cross-day-and-night performance is neglected which greatly hinders the development of related traffic intelligence applications. This work instead develops a novel Day-Night Dual-domain Modulation (DNDM) vehicle re-identification framework for day-night cross-domain traffic scenarios. Specifically a unique night-domain glare suppression module is provided to attenuate the headlight glare from raw nighttime vehicle images. To enhance vehicle features under low-light environments we propose a dual-domain structure enhancement module in the feature extractor which enhances geometric structures between appearance features. To alleviate day-night domain discrepancies we develop a cross-domain class awareness module that facilitates the interaction between appearance and structure features in both domains. In this work we address the Day-Night cross-domain ReID (DN-ReID) problem and provide a new cross-domain dataset named DN-Wild including day and night images of 2286 identities giving in total 85945 daytime images and 54952 nighttime images. Furthermore we also take into account the matter of balance between day and night samples and provide a dataset called DN-348. Exhaustive experiments demonstrate the robustness of the proposed framework in the DN-ReID problem. The code and benchmark are released at https://github.com/chenjingong/DN-ReID.
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Authors: Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, David B. Lindell Sherwin Bahmani
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes. However current text-to-4D methods face a three-way tradeoff between the quality of scene appearance 3D structure and motion. For example text-to-image models and their 3D-aware variants are trained on internet-scale image datasets and can be used to produce scenes with realistic appearance and 3D structure---but no motion. Text-to-video models are trained on relatively smaller video datasets and can produce scenes with motion but poorer appearance and 3D structure. While these models have complementary strengths they also have opposing weaknesses making it difficult to combine them in a way that alleviates this three-way tradeoff. Here we introduce hybrid score distillation sampling an alternating optimization procedure that blends supervision signals from multiple pre-trained diffusion models and incorporates benefits of each for high-fidelity text-to-4D generation. Using hybrid SDS we demonstrate synthesis of 4D scenes with compelling appearance 3D structure and motion.
Adversarial Distillation Based on Slack Matching and Attribution Region Alignment
Authors: Zhen Xiao, Mingxuan Song, Jieyi Long Shenglin Yin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Adversarial distillation (AD) is a highly effective method for enhancing the robustness of small models. Contrary to expectations a high-performing teacher model does not always result in a more robust student model. This is due to two main reasons. First when there are significant differences in predictions between the teacher model and the student model exact matching of predicted values using KL divergence interferes with training leading to poor performance of existing methods. Second matching solely based on the output prevents the student model from fully understanding the behavior of the teacher model. To address these challenges this paper proposes a novel AD method named SmaraAD. During the training process we facilitate the student model in better understanding the teacher model's behavior by aligning the attribution region that the student model focuses on with that of the teacher model. Concurrently we relax the condition of exact matching in KL divergence and replace it with a more flexible matching criterion thereby enhancing the model's robustness. Extensive experiments substantiate the effectiveness of our method in improving the robustness of small models outperforming previous SOTA methods.
Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike Fluctuations
Authors: Ruiqin Xiong, Jing Zhao, Jian Zhang, Xiaopeng Fan, Zhaofei Yu, Tiejun Huang Rui Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: As a bio-inspired vision sensor with ultra-high speed spike cameras exhibit great potential in recording dynamic scenes with high-speed motion or drastic light changes. Different from traditional cameras each pixel in spike cameras records the arrival of photons continuously by firing binary spikes at an ultra-fine temporal granularity. In this process multiple factors impact the imaging including the photons' Poisson arrival thermal noises from circuits and quantization effects in spike readout. These factors introduce fluctuations to spikes making the recorded spike intervals unstable and unable to reflect accurate light intensities. In this paper we present an approach to deal with spike fluctuations and boost spike camera image reconstruction. We first analyze the quantization effects and reveal the unbiased estimation attribute of the reciprocal of differential of spike firing time (DSFT). Based on this we propose a spike representation module to use DSFT with multiple orders for fluctuation suppression where DSFT with higher orders indicates spike integration duration between multiple spikes. We also propose a module for inter-moment feature alignment at multiple granularities. The coarser alignment is based on patch-level cross-attention with a local search strategy and the finer alignment is based on deformable convolution at the pixel level. Experimental results demonstrate the effectiveness of our method on both synthetic and real-captured data. The source code and dataset are available at https://github.com/ruizhao26/BSF.
Text-guided Explorable Image Super-resolution
Authors: Paramanand Chandramouli Kanchana Vaishnavi Gandikota
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we introduce the problem of zero-shot text-guided exploration of the solutions to open-domain image super-resolution. Our goal is to allow users to explore diverse semantically accurate reconstructions that preserve data consistency with the low-resolution inputs for different large downsampling factors without explicitly training for these specific degradations. We propose two approaches for zero-shot text-guided super-resolution - i) modifying the generative process of text-to-image (T2I) diffusion models to promote consistency with low-resolution inputs and ii) incorporating language guidance into zero-shot diffusion-based restoration methods. We show that the proposed approaches result in diverse solutions that match the semantic meaning provided by the text prompt while preserving data consistency with the degraded inputs. We evaluate the proposed baselines for the task of extreme super-resolution and demonstrate advantages in terms of restoration quality diversity and explorability of solutions.
FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition
Authors: Fangzhou Mu, Kuan Heng Lin, Yanli Liu, Bochen Guan, Yin Li, Bolei Zhou Sicheng Mo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models. However auxiliary modules have to be trained for each spatial condition type model architecture and checkpoint putting them at odds with the diverse intents and preferences a human designer would like to convey to the AI models during the content creation process. In this work we present FreeControl a training-free approach for controllable T2I generation that supports multiple conditions architectures and checkpoints simultaneously. FreeControl enforces structure guidance to facilitate the global alignment with a guidance image and appearance guidance to collect visual details from images generated without control. Extensive qualitative and quantitative experiments demonstrate the superior performance of FreeControl across a variety of pre-trained T2I models. In particular FreeControl enables convenient training-free control over many different architectures and checkpoints allows the challenging input conditions on which most of the existing training-free methods fail and achieves competitive synthesis quality compared to training-based approaches. Project page:https://genforce.github.io/freecontrol/.
VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
Authors: Geon Yeong Park, Jong Chul Ye Hyeonho Jeong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-video diffusion models have advanced video generation significantly. However customizing these models to generate videos with tailored motions presents a substantial challenge. In specific they encounter hurdles in (1) accurately reproducing motion from a target video and (2) creating diverse visual variations. For example straightforward extensions of static image customization methods to video often lead to intricate entanglements of appearance and motion data. To tackle this here we present the Video Motion Customization (VMC) framework a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive noisy latent frames as a motion reference. The diffusion process then preserve low-frequency motion trajectories while mitigating high-frequency motion-unrelated noise in image space. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts. Our codes and data can be found at: https://video-motion-customization.github.io/
Holodeck: Language Guided Generation of 3D Embodied AI Environments
Authors: Fan-Yun Sun, Luca Weihs, Eli VanderBilt, Alvaro Herrasti, Winson Han, Jiajun Wu, Nick Haber, Ranjay Krishna, Lingjie Liu, Chris Callison-Burch, Mark Yatskar, Aniruddha Kembhavi, Christopher Clark Yue Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D simulated environments play a critical role in Embodied AI but their creation requires expertise and extensive manual effort restricting their diversity and scope. To mitigate this limitation we present Holodeck a system that generates 3D environments to match a user-supplied prompt fully automatedly. Holodeck can generate diverse scenes e.g. arcades spas and museums adjust the designs for styles and can capture the semantics of complex queries such as "apartment for a researcher with a cat" and "office of a professor who is a fan of Star Wars". Holodeck leverages a large language model (i.e. GPT-4) for common sense knowledge about what the scene might look like and uses a large collection of 3D assets from Objaverse to populate the scene with diverse objects. To address the challenge of positioning objects correctly we prompt GPT-4 to generate spatial relational constraints between objects and then optimize the layout to satisfy those constraints. Our large-scale human evaluation shows that annotators prefer Holodeck over manually designed procedural baselines in residential scenes and that Holodeck can produce high-quality outputs for diverse scene types. We also demonstrate an exciting application of Holodeck in Embodied AI training agents to navigate in novel scenes like music rooms and daycares without human-constructed data which is a significant step forward in developing general-purpose embodied agents.
Distilled Datamodel with Reverse Gradient Matching
Authors: Ruonan Yu, Songhua Liu, Xinchao Wang Jingwen Ye
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The proliferation of large-scale AI models trained on extensive datasets has revolutionized machine learning. With these models taking on increasingly central roles in various applications the need to understand their behavior and enhance interpretability has become paramount. To investigate the impact of changes in training data on a pre-trained model a common approach is leave-one-out retraining. This entails systematically altering the training dataset by removing specific samples to observe resulting changes within the model. However retraining the model for each altered dataset presents a significant computational challenge given the need to perform this operation for every dataset variation. In this paper we introduce an efficient framework for assessing data impact comprising offline training and online evaluation stages. During the offline training phase we approximate the influence of training data on the target model through a distilled synset formulated as a reversed gradient matching problem. For online evaluation we expedite the leave-one-out process using the synset which is then utilized to compute the attribution matrix based on the evaluation objective. Experimental evaluations including training data attribution and assessments of data quality demonstrate that our proposed method achieves comparable model behavior evaluation while significantly speeding up the process compared to the direct retraining method.
DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
Authors: Tianle Cai, Jiaxin Cao, Qinsheng Zhang, Han Cai, Junjie Bai, Yangqing Jia, Kai Li, Song Han Muyang Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion models have achieved great success in synthesizing high-quality images. However generating high-resolution images with diffusion models is still challenging due to the enormous computational costs resulting in a prohibitive latency for interactive applications. In this paper we propose DistriFusion to tackle this problem by leveraging parallelism across multiple GPUs. Our method splits the model input into multiple patches and assigns each patch to a GPU. However naively implementing such an algorithm breaks the interaction between patches and loses fidelity while incorporating such an interaction will incur tremendous communication overhead. To overcome this dilemma we observe the high similarity between the input from adjacent diffusion steps and propose Displaced Patch Parallelism which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step. Therefore our method supports asynchronous communication which can be pipelined by computation. Extensive experiments show that our method can be applied to recent Stable Diffusion XL with no quality degradation and achieve up to a 6.1x speedup on eight NVIDIA A100s compared to one. Our code is publicly available at https://github.com/mit-han-lab/distrifuser.
Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
Authors: Yongyi Su, Xun Xu, Kui Jia Haojie Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prompt engineering. Segment-Anything (SAM) among others is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot generalization. Despite the success recent studies reveal the weakness of SAM under strong distribution shift. In particular SAM performs awkwardly on corrupted natural images camouflaged images medical images etc. Motivated by the observations we aim to develop a self-training based strategy to adapt SAM to target distribution. Given the unique challenges of large source dataset high computation cost and incorrect pseudo label we propose a weakly supervised self-training architecture with anchor regularization and low-rank finetuning to improve the robustness and computation efficiency of adaptation. We validate the effectiveness on 5 types of downstream segmentation tasks including natural clean/corrupted images medical images camouflaged images and robotic images. Our proposed method is task-agnostic in nature and outperforms pre-trained SAM and state-of-the-art domain adaptation methods on almost all downstream tasks with the same testing prompt inputs.
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Authors: Minghao Chen, Shuai Xiao, Liang Peng, Hengjia Li, Binbin Lin, Ping Li, Wenxiao Wang, Boxi Wu, Deng Cai Zhanwei Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent self-training techniques have shown notable improvements in unsupervised domain adaptation for 3D object detection (3D UDA). These techniques typically select pseudo labels i.e. 3D boxes to supervise models for the target domain. However this selection process inevitably introduces unreliable 3D boxes in which 3D points cannot be definitively assigned as foreground or background. Previous techniques mitigate this by reweighting these boxes as pseudo labels but these boxes can still poison the training process. To resolve this problem in this paper we propose a novel pseudo label refinery framework. Specifically in the selection process to improve the reliability of pseudo boxes we propose a complementary augmentation strategy. This strategy involves either removing all points within an unreliable box or replacing it with a high-confidence box. Moreover the point numbers of instances in high-beam datasets are considerably higher than those in low-beam datasets also degrading the quality of pseudo labels during the training process. We alleviate this issue by generating additional proposals and aligning RoI features across different domains. Experimental results demonstrate that our method effectively enhances the quality of pseudo labels and consistently surpasses the state-of-the-art methods on six autonomous driving benchmarks. Code will be available at https://github.com/Zhanwei-Z/PERE.
Reconstructing Hands in 3D with Transformers
Authors: Dandan Shan, Ilija Radosavovic, Angjoo Kanazawa, David Fouhey, Jitendra Malik Georgios Pavlakos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present an approach that can reconstruct hands in 3D from monocular input. Our approach for Hand Mesh Recovery HaMeR follows a fully transformer-based architecture and can analyze hands with significantly increased accuracy and robustness compared to previous work. The key to HaMeR's success lies in scaling up both the data used for training and the capacity of the deep network for hand reconstruction. For training data we combine multiple datasets that contain 2D or 3D hand annotations. For the deep model we use a large scale Vision Transformer architecture. Our final model consistently outperforms the previous baselines on popular 3D hand pose benchmarks. To further evaluate the effect of our design in non-controlled settings we annotate existing in-the-wild datasets with 2D hand keypoint annotations. On this newly collected dataset of annotations HInt we demonstrate significant improvements over existing baselines. We will make our code data and models publicly available upon publication. We make our code data and models available on the project website: https://geopavlakos.github.io/hamer/.
AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search
Authors: Bumsub Ham Junghyup Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Training-free network architecture search (NAS) aims to discover high-performing networks with zero-cost proxies capturing network characteristics related to the final performance. However network rankings estimated by previous training-free NAS methods have shown weak correlations with the performance. To address this issue we propose AZ-NAS a novel approach that leverages the ensemble of various zero-cost proxies to enhance the correlation between a predicted ranking of networks and the ground truth substantially in terms of the performance. To achieve this we introduce four novel zero-cost proxies that are complementary to each other analyzing distinct traits of architectures in the views of expressivity progressivity trainability and complexity. The proxy scores can be obtained simultaneously within a single forward and backward pass making an overall NAS process highly efficient. In order to integrate the rankings predicted by our proxies effectively we introduce a non-linear ranking aggregation method that highlights the networks highly-ranked consistently across all the proxies. Experimental results conclusively demonstrate the efficacy and efficiency of AZ-NAS outperforming state-of-the-art methods on standard benchmarks all while maintaining a reasonable runtime cost.
Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis
Authors: Jingen Jiang, Lei Ma, Shiqing Xin, Gaofeng Meng, Dong-Ming Yan Mingyang Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper presents a novel non-rigid point set registration method that is inspired by unsupervised clustering analysis. Unlike previous approaches that treat the source and target point sets as separate entities we develop a holistic framework where they are formulated as clustering centroids and clustering members separately. We then adopt Tikhonov regularization with an ?1-induced Laplacian kernel instead of the commonly used Gaussian kernel to ensure smooth and more robust displacement fields. Our formulation delivers closed-form solutions theoretical guarantees independence from dimensions and the ability to handle large deformations. Subsequently we introduce a clustering-improved Nystrom method to effectively reduce the computational complexity and storage of the Gram matrix to linear while providing a rigorous bound for the low rank approximation. Our method achieves high accuracy results across various scenarios and surpasses competitors by a significant margin particularly on shapes with substantial deformations. Additionally we demonstrate the versatility of our method in challenging tasks such as shape transfer and medical registration.
Improving Physics-Augmented Continuum Neural Radiance Field-Based Geometry-Agnostic System Identification with Lagrangian Particle Optimization
Authors: Takuhiro Kaneko
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Geometry-agnostic system identification is a technique for identifying the geometry and physical properties of an object from video sequences without any geometric assumptions. Recently physics-augmented continuum neural radiance fields (PAC-NeRF) has demonstrated promising results for this technique by utilizing a hybrid Eulerian-Lagrangian representation in which the geometry is represented by the Eulerian grid representations of NeRF the physics is described by a material point method (MPM) and they are connected via Lagrangian particles. However a notable limitation of PAC-NeRF is that its performance is sensitive to the learning of the geometry from the first frames owing to its two-step optimization. First the grid representations are optimized with the first frames of video sequences and then the physical properties are optimized through video sequences utilizing the fixed first-frame grid representations. This limitation can be critical when learning of the geometric structure is difficult for example in a few-shot (sparse view) setting. To overcome this limitation we propose Lagrangian particle optimization (LPO) in which the positions and features of particles are optimized through video sequences in Lagrangian space. This method allows for the optimization of the geometric structure across the entire video sequence within the physical constraints imposed by the MPM. The experimental results demonstrate that the LPO is useful for geometric correction and physical identification in sparse-view settings.
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Authors: Kuofeng Gao, Shaobo Min, Shu-Tao Xia, Zhifeng Li, Wei Liu Jiawang Bai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Contrastive Vision-Language Pre-training known as CLIP has shown promising effectiveness in addressing downstream image recognition tasks. However recent works revealed that the CLIP model can be implanted with a downstream-oriented backdoor. On downstream tasks one victim model performs well on clean samples but predicts a specific target class whenever a specific trigger is present. For injecting a backdoor existing attacks depend on a large amount of additional data to maliciously fine-tune the entire pre-trained CLIP model which makes them inapplicable to data-limited scenarios. In this work motivated by the recent success of learnable prompts we address this problem by injecting a backdoor into the CLIP model in the prompt learning stage. Our method named BadCLIP is built on a novel and effective mechanism in backdoor attacks on CLIP i.e. influencing both the image and text encoders with the trigger. It consists of a learnable trigger applied to images and a trigger-aware context generator such that the trigger can change text features via trigger-aware prompts resulting in a powerful and generalizable attack. Extensive experiments conducted on 11 datasets verify that the clean accuracy of BadCLIP is similar to those of advanced prompt learning methods and the attack success rate is higher than 99% in most cases. BadCLIP is also generalizable to unseen classes and shows a strong generalization capability under cross-dataset and cross-domain settings. The code is available at https://github.com/jiawangbai/BadCLIP.
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
Authors: Junghun Oh, Kyoung Mu Lee Jaeha Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In real-world scenarios image recognition tasks such as semantic segmentation and object detection often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However due to the ill-posed property of SR it is challenging for typical SR methods to restore task-relevant high-frequency contents which may dilute the advantage of utilizing the SR method. Therefore in this paper we propose Super-Resolution for Image Recognition (SR4IR) that effectively guides the generation of SR images beneficial to achieving satisfactory image recognition performance when processing LR images. The critical component of our SR4IR is the task-driven perceptual (TDP) loss that enables the SR network to acquire task-specific knowledge from a network tailored for a specific task. Moreover we propose a cross-quality patch mix and an alternate training framework that significantly enhances the efficacy of the TDP loss by addressing potential problems when employing the TDP loss. Through extensive experiments we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task including semantic segmentation object detection and image classification. The implementation code is available at https://github.com/JaehaKim97/SR4IR.
PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
Authors: Guangzhi Wang, Mohan Kankanhalli Yangyang Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Recent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy however leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically when applied to three vision-only and one vision-language Transformer models our approach often demonstrates a merely 0.6-point decrease in performance while reducing the original parameter size by 1/3 to 2/3.
XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
Authors: Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, Francis Williams Xuanchi Ren
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present XCube a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to 1024^3 in a feed-forward fashion without time-consuming test-time optimization. To achieve this we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure. Apart from generating high-resolution objects we demonstrate the effectiveness of XCube on large outdoor scenes at scales of 100m x 100m with a voxel size as small as 10cm. We observe clear qualitative and quantitative improvements over past approaches. In addition to unconditional generation we show that our model can be used to solve a variety of tasks such as user-guided editing scene completion from a single scan and text-to-3D.
PixelRNN: In-pixel Recurrent Neural Networks for End-to-end-optimized Perception with Neural Sensors
Authors: Laurie Bose, Piotr Dudek, Gordon Wetzstein Haley M. So
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Conventional image sensors digitize high-resolution images at fast frame rates producing a large amount of data that needs to be transmitted off the sensor for further processing. This is challenging for perception systems operating on edge devices because communication is power inefficient and induces latency. Fueled by innovations in stacked image sensor fabrication emerging sensor--processors offer programmability and processing capabilities directly on the sensor. We exploit these capabilities by developing an efficient recurrent neural network architecture PixelRNN that encodes spatio-temporal features on the sensor using purely binary operations. PixelRNN reduces the amount of data to be transmitted off the sensor by factors up to 256 compared to the raw sensor data while offering competitive accuracy for hand gesture recognition and lip reading tasks. We experimentally validate PixelRNN using a prototype implementation on the SCAMP-5 sensor--processor platform.
Reconstruction-free Cascaded Adaptive Compressive Sensing
Authors: Tao Yue, Xuemei Hu Chenxi Qiu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-stage framework for adaptively allocating the CS sampling based on previous CS measurements. However reconstruction is commonly required for analyzing and steering the successive CS sampling which bottlenecks the ACS speed and impedes the practical application in time-sensitive scenarios. Addressing this challenge we propose a reconstruction-free cascaded ACS method which requires NO reconstruction during the adaptive sampling process. A lightweight Score Network (ScoreNet) is proposed to directly determine the ACS allocation with previous CS measurements and a differentiable adaptive sampling module is proposed for end-to-end training. For image reconstruction we propose a Multi-Grid Spatial-Attention Network (MGSANet) that could facilitate efficient multi-stage training and inferencing. By introducing the reconstruction-fidelity supervision outside the loop of the multi-stage sampling process ACS can be efficiently optimized and achieve high imaging fidelity. The effectiveness of the proposed method is demonstrated with extensive quantitative and qualitative experiments compared with the state-of-the-art CS algorithms.
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Authors: Shangqian Gao, Zeyu Zhang, Zhenzhen Li, Runxue Bao, Yanfu Zhang, Xiaoqian Wang, Heng Huang Xidong Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise making their widespread adoption challenging. To address the limitation the Only-Train-Once (OTO) and OTOv2 are proposed to eliminate the need for additional fine-tuning steps by directly training and compressing a general DNN from scratch. Nevertheless the static design of optimizers (in OTO) can lead to convergence issues of local optima. In this paper we proposed the Auto-Train-Once (ATO) an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. During the model training phase our approach not only trains the target model but also leverages a controller network as an architecture generator to guide the learning of target model weights. Furthermore we developed a novel stochastic gradient algorithm that enhances the coordination between model training and controller network training thereby improving pruning performance. We provide a comprehensive convergence analysis as well as extensive experiments and the results show that our approach achieves state-of-the-art performance across various model architectures (including ResNet18 ResNet34 ResNet50 ResNet56 and MobileNetv2) on standard benchmark datasets (CIFAR-10 CIFAR-100 and ImageNet).