mtybilly/Instruct2DS huggingface dataset:
CodedEvents: Optimal Point-Spread-Function Engineering for 3D-Tracking with Event Cameras
Authors: Sachin Shah, Matthew A. Chan, Haoming Cai, Jingxi Chen, Sakshum Kulshrestha, Chahat Deep Singh, Yiannis Aloimonos, Christopher A. Metzler
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Point-spread-function (PSF) engineering is a well-established computational imaging technique that uses phase masks and other optical elements to embed extra information (e.g. depth) into the images captured by conventional CMOS image sensors. To date however PSF-engineering has not been applied to neuromorphic event cameras; a powerful new image sensing technology that responds to changes in the log-intensity of light. This paper establishes theoretical limits (Cramer Rao bounds) on 3D point localization and tracking with PSF-engineered event cameras. Using these bounds we first demonstrate that existing Fisher phase masks are already near-optimal for localizing static flashing point sources (e.g. blinking fluorescent molecules). We then demonstrate that existing designs are sub-optimal for tracking moving point sources and proceed to use our theory to design optimal phase masks and binary amplitude masks for this task. To overcome the non-convexity of the design problem we leverage novel implicit neural representation based parameterizations of the phase and amplitude masks. We demonstrate the efficacy of our designs through extensive simulations. We also validate our method with a simple prototype.
GLOW: Global Layout Aware Attacks on Object Detection
Authors: Jun Bao, Buyu Liu, Kui Ren, Jun Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. Due to the limited research in structured attacks imposing consistency checks on natural multi-object scenes is a practical defense against conventional adversarial attacks. More desired attacks should be able to fool defenses with such consistency checks. Therefore we present the first approach GLOW that copes with various attack requests by generating global layout-aware adversarial attacks in which both categorical and geometric layout constraints are explicitly established. Specifically we focus on object detection tasks and given a victim image GLOW first localizes victim objects according to target labels. And then it generates multiple attack plans together with their context-consistency scores. GLOW on the one hand is capable of handling various types of requests including single or multiple victim objects with or without specified victim objects. On the other hand it produces a consistency score for each attack plan reflecting the overall contextual consistency that both semantic category and global scene layout are considered. We conduct our experiments on MS COCO and Pascal. Extensive experimental results demonstrate that we can achieve about 30% average relative improvement compared to state-of-the-art methods in conventional single object attack request; Moreover such superiority is also valid across more generic attack requests under both white-box and zero-query black-box settings. Finally we conduct comprehensive human analysis which not only validates our claim further but also provides strong evidence that our evaluation metrics reflect human reviews well.
Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
Authors: Suyeon Kim, Dongha Lee, SeongKu Kang, Sukang Chae, Sanghwan Jang, Hwanjo Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Label noise commonly found in real-world datasets has a detrimental impact on a model's generalization. To effectively detect incorrectly labeled instances previous works have mostly relied on distinguishable training signals such as training loss as indicators to differentiate between clean and noisy labels. However they have limitations in that the training signals incompletely reveal the model's behavior and are not effectively generalized to various noise types resulting in limited detection accuracy. In this paper we propose DynaCor framework that distinguishes incorrectly labeled instances from correctly labeled ones based on the dynamics of the training signals. To cope with the absence of supervision for clean and noisy labels DynaCor first introduces a label corruption strategy that augments the original dataset with intentionally corrupted labels enabling indirect simulation of the model's behavior on noisy labels. Then DynaCor learns to identify clean and noisy instances by inducing two clearly distinguishable clusters from the latent representations of training dynamics. Our comprehensive experiments show that DynaCor outperforms the state-of-the-art competitors and shows strong robustness to various noise types and noise rates.
Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes
Authors: Hao-Bin Duan, Miao Wang, Yan-Xun Li, Yong-Liang Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Neural 3D Strokes a novel technique to generate stylized images of a 3D scene at arbitrary novel views from multi-view 2D images. Different from existing methods which apply stylization to trained neural radiance fields at the voxel level our approach draws inspiration from image-to-painting methods simulating the progressive painting process of human artwork with vector strokes. We develop a palette of stylized 3D strokes from basic primitives and splines and consider the 3D scene stylization task as a multi-view reconstruction process based on these 3D stroke primitives. Instead of directly searching for the parameters of these 3D strokes which would be too costly we introduce a differentiable renderer that allows optimizing stroke parameters using gradient descent and propose a training scheme to alleviate the vanishing gradient issue. The extensive evaluation demonstrates that our approach effectively synthesizes 3D scenes with significant geometric and aesthetic stylization while maintaining a consistent appearance across different views. Our method can be further integrated with style loss and image-text contrastive models to extend its applications including color transfer and text-driven 3D scene drawing. Results and code are available at http://buaavrcg.github.io/Neural3DStrokes.
SIRA: Scalable Inter-frame Relation and Association for Radar Perception
Authors: Ryoma Yataka, Pu Wang, Petros Boufounos, Ryuhei Takahashi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Conventional radar feature extraction faces limitations due to low spatial resolution noise multipath reflection the presence of ghost targets and motion blur. Such limitations can be exacerbated by nonlinear object motion particularly from an ego-centric viewpoint. It becomes evident that to address these challenges the key lies in exploiting temporal feature relation over an extended horizon and enforcing spatial motion consistence for effective association. To this end this paper proposes SIRA (Scalable Inter-frame Relation and Association) with two designs. First inspired by Swin Transformer we introduce extended temporal relation generalizing the existing temporal relation layer from two consecutive frames to multiple inter-frames with temporally regrouped window attention for scalability. Second we propose motion consistency track with the concept of a pseudo-tracklet generated from observational data for better trajectory prediction and subsequent object association. Our approach achieves 58.11 mAP@0.5 for oriented object detection and 47.79 MOTA for multiple object tracking on the Radiate dataset surpassing previous state-of-the-art by a margin of +4.11 mAP@0.5 and +9.94 MOTA respectively.
VOODOO 3D: Volumetric Portrait Disentanglement For One-Shot 3D Head Reenactment
Authors: Phong Tran, Egor Zakharov, Long-Nhat Ho, Anh Tuan Tran, Liwen Hu, Hao Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a 3D-aware one-shot head reenactment method based on a fully volumetric neural disentanglement framework for source appearance and driver expressions. Our method is real-time and produces high-fidelity and view-consistent output suitable for 3D teleconferencing systems based on holographic displays. Existing cutting-edge 3D-aware reenactment methods often use neural radiance fields or 3D meshes to produce view-consistent appearance encoding but at the same time they rely on linear face models such as 3DMM to achieve its disentanglement with facial expressions. As a result their reenactment results often exhibit identity leakage from the driver or have unnatural expressions. To address these problems we propose a neural self-supervised disentanglement approach that lifts both the source image and driver video frame into a shared 3D volumetric representation based on tri-planes. This representation can then be freely manipulated with expression tri-planes extracted from the driving images and rendered from an arbitrary view using neural radiance fields. We achieve this disentanglement via self-supervised learning on a large in-the-wild video dataset. We further introduce a highly effective fine-tuning approach to improve the generalizability of the 3D lifting using the same real-world data. We demonstrate state-of-the-art performance on a wide range of datasets and also showcase high-quality 3D-aware head reenactment on highly challenging and diverse subjects including non-frontal head poses and complex expressions for both source and driver.
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation
Authors: Yunhao Ge, Xiaohui Zeng, Jacob Samuel Huffman, Tsung-Yi Lin, Ming-Yu Liu, Yin Cui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing automatic captioning methods for visual content face challenges such as lack of detail content hallucination and poor instruction following. In this work we propose VisualFactChecker (VFC) a flexible training-free pipeline that generates high-fidelity and detailed captions for both 2D images and 3D objects. VFC consists of three steps: 1) proposal where image-to-text captioning models propose multiple initial captions; 2) verification where a large language model (LLM) utilizes tools such as object detection and VQA models to fact-check proposed captions; 3) captioning where an LLM generates the final caption by summarizing caption proposals and the fact check verification results. In this step VFC can flexibly generate captions in various styles following complex instructions. We conduct comprehensive captioning evaluations using four metrics: 1) CLIP-Score for image-text similarity; 2) CLIP-Image-Score for measuring the image-image similarity between the original and the reconstructed image generated by a text-to-image model using the caption. 3) human study on Amazon Mechanical Turk; 4) GPT-4V for fine-grained evaluation. Evaluation results show that VFC outperforms state-of-the-art open-sourced captioning methods for 2D images on the COCO dataset and 3D assets on the Objaverse dataset. Our study demonstrates that by combining open-source models into a pipeline we can attain captioning capability comparable to proprietary models such as GPT-4V despite being over 10x smaller in model size.
Communication-Efficient Collaborative Perception via Information Filling with Codebook
Authors: Yue Hu, Juntong Peng, Sifei Liu, Junhao Ge, Si Liu, Siheng Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Collaborative perception empowers each agent to improve its perceptual ability through the exchange of perceptual messages with other agents. It inherently results in a fundamental trade-off between perception ability and communication cost. To address this bottleneck issue our core idea is to optimize the collaborative messages from two key aspects: representation and selection. The proposed codebook-based message representation enables the transmission of integer codes rather than high-dimensional feature maps. The proposed information-filling-driven message selection optimizes local messages to collectively fill each agent's information demand preventing information overflow among multiple agents. By integrating these two designs we propose CodeFilling a novel communication-efficient collaborative perception system which significantly advances the perception-communication trade-off and is inclusive to both homogeneous and heterogeneous collaboration settings. We evaluate CodeFilling in both a real-world dataset DAIR-V2X and a new simulation dataset OPV2VH+. Results show that CodeFilling outperforms previous SOTA Where2comm on DAIR-V2X/OPV2VH+ with 1333/1206x lower communication volume. Our code is available at https://github.com/PhyllisH/CodeFilling.
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
Authors: Sikai Bai, Jie Zhang, Song Guo, Shuaicheng Li, Jingcai Guo, Jun Hou, Tao Han, Xiaocheng Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains). However most existing FL methods assume that domain labels are provided during training and their evaluation imposes explicit constraints on the number of domains which must strictly match the number of clients. Because of the underutilization of numerous edge devices and additional cross-client domain annotations in the real world such restrictions may be impractical and involve potential privacy leaks. In this paper we propose an efficient and novel approach called Disentangled Prompt Tuning (DiPrompT) a method that tackles the above restrictions by learning adaptive prompts for domain generalization in a distributed manner. Specifically we first design two types of prompts i.e. global prompt to capture general knowledge across all clients and domain prompts to capture domain-specific knowledge. They eliminate the restriction on the one-to-one mapping between source domains and local clients. Furthermore a dynamic query metric is introduced to automatically search the suitable domain label for each sample which includes two-substep text-image alignments based on prompt tuning without labor-intensive annotation. Extensive experiments on multiple datasets demonstrate that our DiPrompT achieves superior domain generalization performance over state-of-the-art FL methods when domain labels are not provided and even outperforms many centralized learning methods using domain labels.
MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation
Authors: Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, Shubham Tulsiani
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. While recent methods pursuing 3D inference advocate learning novel-view generative models these generations are not 3D-consistent and require a distillation process to generate a 3D output. We instead cast the task of 3D inference as directly generating mutually-consistent multiple views and build on the insight that additionally inferring depth can provide a mechanism for enforcing this consistency. Specifically we train a denoising diffusion model to generate multi-view RGB-D images given a single RGB input image and leverage the (intermediate noisy) depth estimates to obtain reprojection-based conditioning to maintain multi-view consistency. We train our model using large-scale synthetic dataset Obajverse as well as the real-world CO3D dataset comprising of generic camera viewpoints. We demonstrate that our approach can yield more accurate synthesis compared to recent state-of-the-art including distillation-based 3D inference and prior multi-view generation methods. We also evaluate the geometry induced by our multi-view depth prediction and find that it yields a more accurate representation than other direct 3D inference approaches.
Effective Video Mirror Detection with Inconsistent Motion Cues
Authors: Alex Warren, Ke Xu, Jiaying Lin, Gary K.L. Tam, Rynson W.H. Lau
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Image-based mirror detection has recently undergone rapid research due to its significance in applications such as robotic navigation semantic segmentation and scene reconstruction. Recently VMD-Net was proposed as the first video mirror detection technique by modeling dual correspondences between the inside and outside of the mirror both spatially and temporally. However this approach is not reliable as correspondences can occur completely inside or outside of the mirrors. In addition the proposed dataset VMD-D contains many small mirrors limiting its applicability to real-world scenarios. To address these problems we developed a more challenging dataset that includes mirrors of various shapes and sizes at different locations of the frames providing a better reflection of real-world scenarios. Next we observed that the motions between the inside and outside of the mirror are often inconsistent. For instance when moving in front of a mirror the motion inside the mirror is often much smaller than the motion outside due to increased depth perception. With these observations we propose modeling inconsistent motion cues to detect mirrors and a new network with two novel modules. The Motion Attention Module (MAM) explicitly models inconsistent motions around mirrors via optical flow and the Motion-Guided Edge Detection Module (MEDM) uses motions to guide mirror edge feature learning. Experimental results on our proposed dataset show that our method outperforms state-of-the-arts. The code and dataset are available at https://github.com/AlexAnthonyWarren/MG-VMD.
Multi-Object Tracking in the Dark
Authors: Xinzhe Wang, Kang Ma, Qiankun Liu, Yunhao Zou, Ying Fu
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: Nannan Li, Qing Liu, Krishna Kumar Singh, Yilin Wang, Jianming Zhang, Bryan A. Plummer, Zhe Lin
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: Lirui Zhao, Yue Yang, Kaipeng Zhang, Wenqi Shao, Yuxin Zhang, Yu Qiao, Ping Luo, Rongrong Ji
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: Junwon Seo, Sangyoon Lee, Kwang In Kim, Jaeho Lee
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: Marianna Ohanyan, Hayk Manukyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi
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: Wen Li, Yuyang Yang, Shangshu Yu, Guosheng Hu, Chenglu Wen, Ming Cheng, Cheng Wang
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: Fangzhou Mu, Carter Sifferman, Sacha Jungerman, Yiquan Li, Mark Han, Michael Gleicher, Mohit Gupta, Yin Li
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: Hong-Xing Yu, Haoyi Duan, Junhwa Hur, Kyle Sargent, Michael Rubinstein, William T. Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, Charles Herrmann
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: Xi Chen, 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
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: Hongchao Li, Jingong Chen, Aihua Zheng, Yong Wu, Yonglong Luo
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: Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, David B. Lindell
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: Shenglin Yin, Zhen Xiao, Mingxuan Song, Jieyi Long
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: Rui Zhao, Ruiqin Xiong, Jing Zhao, Jian Zhang, Xiaopeng Fan, Zhaofei Yu, Tiejun Huang
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: Kanchana Vaishnavi Gandikota, Paramanand Chandramouli
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: Sicheng Mo, Fangzhou Mu, Kuan Heng Lin, Yanli Liu, Bochen Guan, Yin Li, Bolei Zhou
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: Hyeonho Jeong, Geon Yeong Park, Jong Chul Ye
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: Yue Yang, 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
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: Jingwen Ye, Ruonan Yu, Songhua Liu, Xinchao Wang
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: Muyang Li, Tianle Cai, Jiaxin Cao, Qinsheng Zhang, Han Cai, Junjie Bai, Yangqing Jia, Kai Li, Song Han
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: Haojie Zhang, Yongyi Su, Xun Xu, Kui Jia
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.