mtybilly/Instruct2DS huggingface dataset:
SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction
Authors: Zongxin Yang, Yi Yang Zechuan Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images along with predicting textures for unseen areas remains a significant challenge. A key limitation of previous methods is their insufficient prior guidance in transitioning from 2D to 3D and in texture prediction. In response we introduce SIFU (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction) a novel approach combining a Side-view Decoupling Transformer with a 3D Consistent Texture Refinement pipeline. SIFU employs a cross-attention mechanism within the transformer using SMPL-X normals as queries to effectively decouple side-view features in the process of mapping 2D features to 3D. This method not only improves the precision of the 3D models but also their robustness especially when SMPL-X estimates are not perfect. Our texture refinement process leverages text-to-image diffusion-based prior to generate realistic and consistent textures for invisible views. Through extensive experiments SIFU surpasses SOTA methods in both geometry and texture reconstruction showcasing enhanced robustness in complex scenarios and achieving an unprecedented Chamfer and P2S measurement. Our approach extends to practical applications such as 3D printing and scene building demonstrating its broad utility in real-world scenarios.
WinSyn: : A High Resolution Testbed for Synthetic Data
Authors: John Femiani, Peter Wonka Tom Kelly
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present WinSyn a unique dataset and testbed for creating high-quality synthetic data with procedural modeling techniques. The dataset contains high-resolution photographs of windows selected from locations around the world with 89318 individual window crops showcasing diverse geometric and material characteristics. We evaluate a procedural model by training semantic segmentation networks on both synthetic and real images and then comparing their performances on a shared test set of real images. Specifically we measure the difference in mean Intersection over Union (mIoU) and determine the effective number of real images to match synthetic data's training performance. We design a baseline procedural model as a benchmark and provide 21290 synthetically generated images. By tuning the procedural model key factors are identified which significantly influence the model's fidelity in replicating real-world scenarios. Importantly we highlight the challenge of procedural modeling using current techniques especially in their ability to replicate the spatial semantics of real-world scenarios. This insight is critical because of the potential of procedural models to bridge hidden scene aspects such as depth reflectivity material properties and lighting conditions.
Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers
Authors: Bineng Zhong, Zhiyi Mo, Shengping Zhang, Liangtao Shi, Shuxiang Song, Rongrong Ji Jinxia Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently the spatio-temporal information is far away from being fully explored. To alleviate this issue we propose an adaptive tracker with spatio-temporal transformers (named AQATrack) which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally based on the initial target template and learnt autoregressive queries a spatio-temporal information fusion module (STM) is designed for spatiotemporal formation aggregation to locate a target object. Benefiting from the STM we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker's performance on six popular tracking benchmarks: LaSOT LaSOText TrackingNet GOT-10k TNL2K and UAV123. Code and models will be https://github.com/orgs/GXNU-ZhongLab.
Misalignment-Robust Frequency Distribution Loss for Image Transformation
Authors: Juncheng Wu, Zian Wang, Wenhan Yang, Hanli Wang, Lin Ma Zhangkai Ni
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper aims to address a common challenge in deep learning-based image transformation methods such as image enhancement and super-resolution which heavily rely on precisely aligned paired datasets with pixel-level alignments. However creating precisely aligned paired images presents significant challenges and hinders the advancement of methods trained on such data. To overcome this challenge this paper introduces a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Specifically we transform image features into the frequency domain using Discrete Fourier Transformation (DFT). Subsequently frequency components (amplitude and phase) are processed separately to form the FDL loss function. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain. Extensive experimental evaluations focusing on image enhancement and super-resolution tasks demonstrate that FDL outperforms existing misalignment-robust loss functions. Furthermore we explore the potential of our FDL for image style transfer that relies solely on completely misaligned data. Our code is available at: https://github.com/eezkni/FDL
Language-aware Visual Semantic Distillation for Video Question Answering
Authors: Chao Yang, Yu Qiao, Chengbin Quan, Youjian Zhao Bo Zou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Significant advancements in video question answering (VideoQA) have been made thanks to thriving large image-language pretraining frameworks. Although these image-language models can efficiently represent both video and language branches they typically employ a goal-free vision perception process and do not interact vision with language well during the answer generation thus omitting crucial visual cues. In this paper we are inspired by the human recognition and learning pattern and propose VideoDistill a framework with language-aware (i.e. goal-driven) behavior in both vision perception and answer generation process. VideoDistill generates answers only from question-related visual embeddings and follows a thinking-observing-answering approach that closely resembles human behavior distinguishing it from previous research. Specifically we develop a language-aware gating mechanism to replace the standard cross-attention avoiding language's direct fusion into visual representations. We incorporate this mechanism into two key components of the entire framework. The first component is a differentiable sparse sampling module which selects frames containing the necessary dynamics and semantics relevant to the questions. The second component is a vision refinement module that merges existing spatial-temporal attention layers to ensure the extraction of multi-grained visual semantics associated with the questions. We conduct experimental evaluations on various challenging video question-answering benchmarks and VideoDistill achieves state-of-the-art performance in both general and long-form VideoQA datasets. In Addition we verify that VideoDistill can effectively alleviate the utilization of language shortcut solutions in the EgoTaskQA dataset.
Lane2Seq: Towards Unified Lane Detection via Sequence Generation
Authors: Kunyang Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we present a novel sequence generation-based framework for lane detection called Lane2Seq. It unifies various lane detection formats by casting lane detection as a sequence generation task. This is different from previous lane detection methods which depend on well-designed task-specific head networks and corresponding loss functions. Lane2Seq only adopts a plain transformer-based encoder-decoder architecture with a simple cross-entropy loss. Additionally we propose a new multi-format model tuning based on reinforcement learning to incorporate the task-specific knowledge into Lane2Seq. Experimental results demonstrate that such a simple sequence generation paradigm not only unifies lane detection but also achieves competitive performance on benchmarks. For example Lane2Seq gets 97.95% and 97.42% F1 score on Tusimple and LLAMAS datasets establishing a new state-of-the-art result for two benchmarks.
Disentangled Prompt Representation for Domain Generalization
Authors: Zhipeng Xu, Xinyang Jiang, Nannan Wang, Dongsheng Li, Xinbo Gao De Cheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Domain Generalization (DG) aims to develop a versatile model capable of performing well on unseen target domains. Recent advancements in pre-trained Visual Foundation Models (VFMs) such as CLIP show significant potential in enhancing the generalization abilities of deep models. Although there is a growing focus on VFM-based domain prompt tuning for DG effectively learning prompts that disentangle invariant features across all domains remains a major challenge. In this paper we propose addressing this challenge by leveraging the controllable and flexible language prompt of the VFM. Observing that the text modality of VFMs is inherently easier to disentangle we introduce a novel text feature guided visual prompt tuning framework. This framework first automatically disentangles the text prompt using a large language model (LLM) and then learns domain-invariant visual representation guided by the disentangled text feature. Moreover we also devise domain-specific prototype learning to fully exploit domain-specific information to combine with the invariant feature prediction. Extensive experiments on mainstream DG datasets namely PACS VLCS OfficeHome DomainNet and TerraInc demonstrate that the proposed method achieves superior performances to state-of-the-art DG methods.
Abductive Ego-View Accident Video Understanding for Safe Driving Perception
Authors: Lei-lei Li, Junfei Zhou, Junbin Xiao, Hongkai Yu, Chen Lv, Jianru Xue, Tat-Seng Chua Jianwu Fang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present MM-AU a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11727 in-the-wild ego-view accident videos each with temporally aligned text descriptions. We annotate over 2.23 million object boxes and 58650 pairs of video-based accident reasons covering 58 accident categories. MM-AU supports various accident understanding tasks particularly multimodal video diffusion to understand accident cause-effect chains for safe driving. With MM-AU we present an Abductive accident Video understanding framework for Safe Driving perception (AdVersa-SD). AdVersa-SD performs video diffusion via an Object-Centric Video Diffusion (OAVD) method which is driven by an abductive CLIP model. This model involves a contrastive interaction loss to learn the pair co-occurrence of normal near-accident accident frames with the corresponding text descriptions such as accident reasons prevention advice and accident categories. OAVD enforces the object region learning while fixing the content of the original frame background in video generation to find the dominant objects for certain accidents. Extensive experiments verify the abductive ability of AdVersa-SD and the superiority of OAVD against the state-of-the-art diffusion models. Additionally we provide careful benchmark evaluations for object detection and accident reason answering since AdVersa-SD relies on precise object and accident reason information.
Cross-spectral Gated-RGB Stereo Depth Estimation
Authors: Stefanie Walz, Mario Bijelic, Felix Heide Samuel Brucker
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Gated cameras flood-illuminate a scene and capture the time-gated impulse response of a scene. By employing nanosecond-scale gates existing sensors are capable of capturing mega-pixel gated images delivering dense depth improving on today's LiDAR sensors in spatial resolution and depth precision. Although gated depth estimation methods deliver a million of depth estimates per frame their resolution is still an order below existing RGB imaging methods. In this work we combine high-resolution stereo HDR RCCB cameras with gated imaging allowing us to exploit depth cues from active gating multi-view RGB and multi-view NIR sensing -- multi-view and gated cues across the entire spectrum. The resulting capture system consists only of low-cost CMOS sensors and flood-illumination. We propose a novel stereo-depth estimation method that is capable of exploiting these multi-modal multi-view depth cues including the active illumination that is measured by the RCCB camera when removing the IR-cut filter. The proposed method achieves accurate depth at long ranges outperforming the next best existing method by 39% for ranges of 100 to 220 m in MAE on accumulated LiDAR ground-truth. Our code models and datasets are available here (https://light.princeton.edu/gatedrccbstereo/).
KVQ: Kwai Video Quality Assessment for Short-form Videos
Authors: Xin Li, Yajing Pei, Kun Yuan, Qizhi Xie, Yunpeng Qu, Ming Sun, Chao Zhou, Zhibo Chen Yiting Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Short-form UGC video platforms like Kwai and TikTok have been an emerging and irreplaceable mainstream media form thriving on user-friendly engagement and kaleidoscope creation etc. However the advancing content generation modes e.g. special effects and sophisticated processing workflows e.g. de-artifacts have introduced significant challenges to recent UGC video quality assessment: (i) the ambiguous contents hinder the identification of quality-determined regions. (ii) the diverse and complicated hybrid distortions are hard to distinguish. To tackle the above challenges and assist in the development of short-form videos we establish the first large-scale Kwai short Video database for Quality assessment termed KVQ which comprises 600 user-uploaded short videos and 3600 processed videos through the diverse practical processing workflows including pre-processing transcoding and enhancement. Among them the absolute quality score of each video and partial ranking score among indistinguish samples are provided by a team of professional researchers specializing in image processing. Based on this database we propose the first short-form video quality evaluator i.e. KSVQE which enables the quality evaluator to identify the quality-determined semantics with the content understanding of large vision language models (i.e. CLIP) and distinguish the distortions with the distortion under- standing module. Experimental results have shown the effectiveness of KSVQE on our KVQ database and popular VQA databases. The project can be found at https: //lixinustc.github.io/projects/KVQ/.
Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories
Authors: Sergey Prokudin, Marko Mihajlovic, Qianli Ma, Siyu Tang Yan Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Understanding the dynamics of generic 3D scenes is fundamentally challenging in computer vision essential in enhancing applications related to scene reconstruction motion tracking and avatar creation. In this work we address the task as the problem of inferring dense long-range motion of 3D points. By observing a set of point trajectories we aim to learn an implicit motion field parameterized by a neural network to predict the movement of novel points within the same domain without relying on any data-driven or scene-specific priors. To achieve this our approach builds upon the recently introduced dynamic point field model that learns smooth deformation fields between the canonical frame and individual observation frames. However temporal consistency between consecutive frames is neglected and the number of required parameters increases linearly with the sequence length due to per-frame modeling. To address these shortcomings we exploit the intrinsic regularization provided by SIREN and modify the input layer to produce a spatiotemporally smooth motion field. Additionally we analyze the motion field Jacobian matrix and discover that the motion degrees of freedom (DOFs) in an infinitesimal area around a point and the network hidden variables have different behaviors to affect the model's representational power. This enables us to improve the model representation capability while retaining the model compactness. Furthermore to reduce the risk of overfitting we introduce a regularization term based on the assumption of piece-wise motion smoothness. Our experiments assess the model's performance in predicting unseen point trajectories and its application in temporal mesh alignment with guidance. The results demonstrate its superiority and effectiveness. The code and data for the project are publicly available at https://yz-cnsdqz.github.io/eigenmotion/DOMA.
LEMON: Learning 3D Human-Object Interaction Relation from 2D Images
Authors: Wei Zhai, Hongchen Luo, Yang Cao, Zheng-Jun Zha Yuhang Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interaction elements e.g. human contact object affordance and human-object spatial relation primarily from the perspective of either the human or the object. Which underexploit certain correlations between the interaction counterparts (human and object) and struggle to address the uncertainty in interactions. Actually objects' functionalities potentially affect humans' interaction intentions which reveals what the interaction is. Meanwhile the interacting humans and objects exhibit matching geometric structures which presents how to interact. In light of this we propose harnessing these inherent correlations between interaction counterparts to mitigate the uncertainty and jointly anticipate the above interaction elements in 3D space. To achieve this we present LEMON (LEarning 3D huMan-Object iNteraction relation) a unified model that mines interaction intentions of the counterparts and employs curvatures to guide the extraction of geometric correlations combining them to anticipate the interaction elements. Besides the 3D Interaction Relation dataset (3DIR) is collected to serve as the test bed for training and evaluation. Extensive experiments demonstrate the superiority of LEMON over methods estimating each element in isolation. The code and dataset are available at https://yyvhang.github.io/LEMON/
Low-Latency Neural Stereo Streaming
Authors: Farzad Farhadzadeh, Amir Said, Guillaume Sautiere, Hoang Le Qiqi Hou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The rise of new video modalities like virtual reality or autonomous driving has increased the demand for efficient multi-view video compression methods both in terms of rate-distortion (R-D) performance and in terms of delay and runtime. While most recent stereo video compression approaches have shown promising performance they compress left and right views sequentially leading to poor parallelization and runtime performance. This work presents Low-Latency neural codec for Stereo video Streaming (LLSS) a novel parallel stereo video coding method designed for fast and efficient low-latency stereo video streaming. Instead of using a sequential cross-view motion compensation like existing methods LLSS introduces a bidirectional feature shifting module to directly exploit mutual information among views and encode them effectively with a joint cross-view prior model for entropy coding. Thanks to this design LLSS processes left and right views in parallel minimizing latency; all while substantially improving R-D performance compared to both existing neural and conventional codecs.
Understanding Video Transformers via Universal Concept Discovery
Authors: Achal Dave, Rares Ambrus, Adrien Gaidon, Konstantinos G. Derpanis, Pavel Tokmakov Matthew Kowal
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely we seek to explain the decision-making process of video transformers based on high-level spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively video models deal with the added temporal dimension increasing complexity and posing challenges in identifying dynamic concepts over time. In this work we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts and ranking their importance to the output of a model. The resulting concepts are highly interpretable revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations we discover that some of these mechanism are universal in video transformers. Finally we show that VTCD can be used for fine-grained action recognition and video object segmentation.
Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
Authors: Yinpeng Dong, Siyuan Zhang, Tianzan Min, Hang Su, Jun Zhu Yichi Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Although Multimodal Large Language Models (MLLMs) have demonstrated promising versatile capabilities their performance is still inferior to specialized models on downstream tasks which makes adaptation necessary to enhance their utility. However fine-tuning methods require independent training for every model leading to huge computation and memory overheads. In this paper we propose a novel setting where we aim to improve the performance of diverse MLLMs with a group of shared parameters optimized for a downstream task. To achieve this we propose Transferable Visual Prompting (TVP) a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model. We introduce two strategies to address the issue of cross-model feature corruption of existing visual prompting methods and enhance the transferability of the learned prompts including 1) Feature Consistency Alignment: which imposes constraints to the prompted feature changes to maintain task-agnostic knowledge; 2) Task Semantics Enrichment: which encourages the prompted images to contain richer task-specific semantics with language guidance. We validate the effectiveness of TVP through extensive experiments with 6 modern MLLMs on a wide variety of tasks ranging from object recognition and counting to multimodal reasoning and hallucination correction.
PointOBB: Learning Oriented Object Detection via Single Point Supervision
Authors: Xue Yang, Yi Yu, Qingyun Li, Junchi Yan, Yansheng Li Junwei Luo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Single point-supervised object detection is gaining attention due to its cost-effectiveness. However existing approaches focus on generating horizontal bounding boxes (HBBs) while ignoring oriented bounding boxes (OBBs) commonly used for objects in aerial images. This paper proposes PointOBB the first single Point-based OBB generation method for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view a resized view and a rotated/flipped (rot/flp) view. Upon the original view we leverage the resized and rot/flp views to build a scale augmentation module and an angle acquisition module respectively. In the former module a Scale-Sensitive Consistency (SSC) loss is designed to enhance the deep network's ability to perceive the object scale. For accurate object angle predictions the latter module incorporates self-supervised learning to predict angles which is associated with a scale-guided Dense-to-Sparse (DS) matching strategy for aggregating dense angles corresponding to sparse objects. The resized and rot/flp views are switched using a progressive multi-view switching strategy during training to achieve coupled optimization of scale and angle. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance and significantly outperforms potential point-supervised baselines.
Intrinsic Image Diffusion for Indoor Single-view Material Estimation
Authors: Vincent Sitzmann, Matthias Nießner Peter Kocsis
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Intrinsic Image Diffusion a generative model for appearance decomposition of indoor scenes. Given a single input view we sample multiple possible material explanations represented as albedo roughness and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue we advocate for a probabilistic formulation where instead of attempting to directly predict the true material properties we employ a conditional generative model to sample from the solution space. Furthermore we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper more consistent and more detailed materials outperforming state-of-the-art methods by 1.5dB on PSNR and by 45% better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.
SHAP-EDITOR: Instruction-Guided Latent 3D Editing in Seconds
Authors: Junyu Xie, Iro Laina, Andrea Vedaldi Minghao Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a novel feed-forward 3D editing framework called Shap-Editor. Prior research on editing 3D objects primarily concentrated on editing individual objects by leveraging off-the-shelf 2D image editing networks utilizing a process called 3D distillation which transfers knowledge from the 2D network to the 3D asset. Distillation necessitates at least tens of minutes per asset to attain satisfactory editing results thus it is not very practical. In contrast we ask whether 3D editing can be carried out directly by a feed-forward network eschewing test-time optimization. In particular we hypothesise that this process can be greatly simplified by first encoding 3D objects into a suitable latent space. We validate this hypothesis by building upon the latent space of Shap-E. We demonstrate that direct 3D editing in this space is possible and efficient by learning a feed-forward editor network that only requires approximately one second per edit. Our experiments show that Shap-Editor generalises well to both in-distribution and out-of-distribution 3D assets with different prompts and achieves superior performance compared to methods that carry out test-time optimisation for each edited instance.
HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generation
Authors: Tolga Birdal, Xiaohui Liang, Federico Tombari Zhiying Leng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D shape generation from text is a fundamental task in 3D representation learning. The text-shape pairs exhibit a hierarchical structure where a general text like "chair" covers all 3D shapes of the chair while more detailed prompts refer to more specific shapes. Furthermore both text and 3D shapes are inherently hierarchical structures. However existing Text2Shape methods such as SDFusion do not exploit that. In this work we propose HyperSDFusion a dual-branch diffusion model that generates 3D shapes from a given text. Since hyperbolic space is suitable for handling hierarchical data we propose to learn the hierarchical representations of text and 3D shapes in hyperbolic space. First we introduce a hyperbolic text-image encoder to learn the sequential and multi-modal hierarchical features of text in hyperbolic space. In addition we design a hyperbolic text-graph convolution module to learn the hierarchical features of text in hyperbolic space. In order to fully utilize these text features we introduce a dual-branch structure to embed text features in 3D feature space. At last to endow the generated 3D shapes with a hierarchical structure we devise a hyperbolic hierarchical loss. Our method is the first to explore the hyperbolic hierarchical representation for text-to-shape generation. Experimental results on the existing text-to-shape paired dataset Text2Shape achieved state-of-the-art results. We release our implementation under HyperSDFusion.github.io.
OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table Recognition
Authors: Sibo Song, Wenwen Yu, Yuliang Liu, Wenqing Cheng, Fei Huang, Xiang Bai, Cong Yao, Zhibo Yang Jianqiang Wan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently visually-situated text parsing (VsTP) has experienced notable advancements driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However due to the diversified targets and heterogeneous schemas previous works usually design task-specific architectures and objectives for individual tasks which inadvertently leads to modal isolation and complex workflow. In this paper we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically we devise a universal model called OmniParser which can simultaneously handle three typical visually-situated text parsing tasks: text spotting key information extraction and table recognition. In OmniParser all tasks share the unified encoder-decoder architecture the unified objective: point-conditioned text generation and the unified input & output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks despite its unified concise design. The code is available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery.
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization
Authors: Dongcheng Zhao, Tenglong Li, Jindong Li, Yi Zeng Guobin Shen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Spiking Neural Networks (SNNs) have been widely praised for their high energy efficiency and immense potential. However comprehensive research that critically contrasts and correlates SNNs with quantized Artificial Neural Networks (ANNs) remains scant often leading to skewed comparisons lacking fairness towards ANNs. This paper introduces a unified perspective illustrating that the time steps in SNNs and quantized bit-widths of activation values present analogous representations. Building on this we present a more pragmatic and rational approach to estimating the energy consumption of SNNs. Diverging from the conventional Synaptic Operations (SynOps) we champion the "Bit Budget" concept. This notion permits an intricate discourse on strategically allocating computational and storage resources between weights activation values and temporal steps under stringent hardware constraints. Guided by the Bit Budget paradigm we discern that pivoting efforts towards spike patterns and weight quantization rather than temporal attributes elicits profound implications for model performance. Utilizing the Bit Budget for holistic design consideration of SNNs elevates model performance across diverse data types encompassing static imagery and neuromorphic datasets. Our revelations bridge the theoretical chasm between SNNs and quantized ANNs and illuminate a pragmatic trajectory for future endeavors in energy-efficient neural computations.
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation
Authors: Yunhe Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: A major focus of clinical imaging workflow is disease diagnosis and management leading to medical imaging datasets strongly tied to specific clinical objectives. This scenario has led to the prevailing practice of developing task-specific segmentation models without gaining insights from widespread imaging cohorts. Inspired by the training program of medical radiology residents we propose a shift towards universal medical image segmentation a paradigm aiming to build medical image understanding foundation models by leveraging the diversity and commonality across clinical targets body regions and imaging modalities. Towards this goal we develop Hermes a novel context-prior learning approach to address the challenges of data heterogeneity and annotation differences in medical image segmentation. In a large collection of eleven diverse datasets (2438 3D images) across five modalities (CT PET T1 T2 and cine MRI) and multiple body regions we demonstrate the merit of the universal paradigm over the traditional paradigm on addressing multiple tasks within a single model. By exploiting the synergy across tasks Hermes achieves state-of-the-art performance on all testing datasets and shows superior model scalability. Results on two additional datasets reveals Hermes' strong performance for transfer learning incremental learning and generalization to downstream tasks. Hermes's learned priors demonstrate an appealing trait to reflect the intricate relations among tasks and modalities which aligns with the established anatomical and imaging principles in radiology. The code is available.
Material Palette: Extraction of Materials from a Single Image
Authors: Fabio Pizzati, Raoul de Charette Ivan Lopes
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Physically-Based Rendering (PBR) is key to modeling the interaction between light and materials and finds extensive applications across computer graphics domains. However acquiring PBR materials is costly and requires special apparatus. In this paper we propose a method to extract PBR materials from a single real-world image. We do so in two steps: first we map regions of the image to material concept tokens using a diffusion model allowing the sampling of texture images resembling each material in the scene. Second we leverage a separate network to decompose the generated textures into spatially varying BRDFs (SVBRDFs) offering us readily usable materials for rendering applications. Our approach relies on existing synthetic material libraries with SVBRDF ground truth. It exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. Along with video we share code and models as open-source on the project page: https://github.com/astra-vision/MaterialPalette
Initialization Matters for Adversarial Transfer Learning
Authors: Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin Andong Hua
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: With the prevalence of the Pretraining-Finetuning paradigm in transfer learning the robustness of downstream tasks has become a critical concern. In this work we delve into adversarial robustness in transfer learning and reveal the critical role of initialization including both the pretrained model and the linear head. First we discover the necessity of an adversarially robust pretrained model. Specifically we reveal that with a standard pretrained model Parameter-Efficient Finetuning (PEFT) methods either fail to be adversarially robust or continue to exhibit significantly degraded adversarial robustness on downstream tasks even with adversarial training during finetuning. Leveraging a robust pretrained model surprisingly we observe that a simple linear probing can outperform full finetuning and other PEFT methods with random initialization on certain datasets. We further identify that linear probing excels in preserving robustness from the robust pretraining. Based on this we propose Robust Linear Initialization (RoLI) for adversarial finetuning which initializes the linear head with the weights obtained by adversarial linear probing to maximally inherit the robustness from pretraining. Across five different image classification datasets we demonstrate the effectiveness of RoLI and achieve new state-of-the-art results. Our code is available at https://github.com/DongXzz/RoLI.
RealCustom: Narrowing Real Text Word for Real-Time Open-Domain Text-to-Image Customization
Authors: Zhendong Mao, Mingcong Liu, Qian He, Yongdong Zhang Mengqi Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image customization which aims to synthesize text-driven images for the given subjects has recently revolutionized content creation. Existing works follow the pseudo-word paradigm i.e. represent the given subjects as pseudo-words and then compose them with the given text. However the inherent entangled influence scope of pseudo-words with the given text results in a dual-optimum paradox i.e. the similarity of the given subjects and the controllability of the given text could not be optimal simultaneously. We present RealCustom that for the first time disentangles similarity from controllability by precisely limiting subject influence to relevant parts only achieved by gradually narrowing real text word from its general connotation to the specific subject and using its cross-attention to distinguish relevance. Specifically RealCustom introduces a novel "train-inference" decoupled framework: (1) during training RealCustom learns general alignment between visual conditions to original textual conditions by a novel adaptive scoring module to adaptively modulate influence quantity; (2) during inference a novel adaptive mask guidance strategy is proposed to iteratively update the influence scope and influence quantity of the given subjects to gradually narrow the generation of the real text word. Comprehensive experiments demonstrate the superior real-time customization ability of RealCustom in the open domain achieving both unprecedented similarity of the given subjects and controllability of the given text for the first time. The project page is https://corleone-huang.github.io/realcustom/.
MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections
Authors: Zihao Wei, Hongru Zhu, Fei Xia, Yuyin Zhou Mude Hui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Volumetric optical microscopy using non-diffracting beams enables rapid imaging of 3D volumes by projecting them axially to 2D images but lacks crucial depth information. Addressing this we introduce MicroDiffusion a pioneering tool facilitating high-quality depth-resolved 3D volume reconstruction from limited 2D projections. While existing Implicit Neural Representation (INR) models often yield incomplete outputs and Denoising Diffusion Probabilistic Models (DDPM) excel at capturing details our method integrates INR's structural coherence with DDPM's fine-detail enhancement capabilities. We pretrain an INR model to transform 2D axially-projected images into a preliminary 3D volume. This pretrained INR acts as a global prior guiding DDPM's generative process through a linear interpolation between INR outputs and noise inputs. This strategy enriches the diffusion process with structured 3D information enhancing detail and reducing noise in localized 2D images.By conditioning the diffusion model on the closest 2D projection MicroDiffusion substantially enhances fidelity in resulting 3D reconstructions surpassing INR and standard DDPM outputs with unparalleled image quality and structural fidelity. Our code and dataset are available athttps://github.com/UCSC-VLAA/MicroDiffusion.
Task-Conditioned Adaptation of Visual Features in Multi-Task Policy Learning
Authors: Laetitia Matignon, Olivier Simonin, Christian Wolf Pierre Marza
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Successfully addressing a wide variety of tasks is a core ability of autonomous agents requiring flexibly adapting the underlying decision-making strategies and as we argue in this work also adapting the perception modules. An analogical argument would be the human visual system which uses top-down signals to focus attention determined by the current task. Similarly we adapt pre-trained large vision models conditioned on specific downstream tasks in the context of multi-task policy learning. We introduce task-conditioned adapters that do not require finetuning any pre-trained weights combined with a single policy trained with behavior cloning and capable of addressing multiple tasks. We condition the visual adapters on task embeddings which can be selected at inference if the task is known or alternatively inferred from a set of example demonstrations. To this end we propose a new optimization-based estimator. We evaluate the method on a wide variety of tasks from the CortexBench benchmark and show that compared to existing work it can be addressed with a single policy. In particular we demonstrate that adapting visual features is a key design choice and that the method generalizes to unseen tasks given a few demonstrations.
L0-Sampler: An L0 Model Guided Volume Sampling for NeRF
Authors: Juyong Zhang Liangchen Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Since its proposal Neural Radiance Fields (NeRF) has achieved great success in related tasks mainly adopting the hierarchical volume sampling (HVS) strategy for volume rendering. However the HVS of NeRF approximates distributions using piecewise constant functions which provides a relatively rough estimation. Based on the observation that a well-trained weight function w(t) and the L_0 distance between points and the surface have very high similarity we propose L_0-Sampler by incorporating the L_0 model into w(t) to guide the sampling process. Specifically we propose using piecewise exponential functions rather than piecewise constant functions for interpolation which can not only approximate quasi-L_0 weight distributions along rays quite well but can be easily implemented with a few lines of code change without additional computational burden. Stable performance improvements can be achieved by applying L_0-Sampler to NeRF and related tasks like 3D reconstruction. Code is available at https://ustc3dv.github.io/L0-Sampler/.
Hybrid Proposal Refiner: Revisiting DETR Series from the Faster R-CNN Perspective
Authors: Fangyun Wei, Chang Xu Jinjing Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: With the transformative impact of the Transformer DETR pioneered the application of the encoder-decoder architecture to object detection. A collection of follow-up research e.g. Deformable DETR aims to enhance DETR while adhering to the encoder-decoder design. In this work we revisit the DETR series through the lens of Faster R-CNN. We find that the DETR resonates with the underlying principles of Faster R-CNN's RPN-refiner design but benefits from end-to-end detection owing to the incorporation of Hungarian matching. We systematically adapt the Faster R-CNN towards the Deformable DETR by integrating or repurposing each component of Deformable DETR and note that Deformable DETR's improved performance over Faster R-CNN is attributed to the adoption of advanced modules such as a superior proposal refiner (e.g. deformable attention rather than RoI Align). When viewing the DETR through the RPN-refiner paradigm we delve into various proposal refinement techniques such as deformable attention cross attention and dynamic convolution. These proposal refiners cooperate well with each other; thus we synergistically combine them to establish a Hybrid Proposal Refiner (HPR). Our HPR is versatile and can be incorporated into various DETR detectors. For instance by integrating HPR to a strong DETR detector we achieve an AP of 54.9 on the COCO benchmark utilizing a ResNet-50 backbone and a 36-epoch training schedule. Code and models are available at https://github.com/ZhaoJingjing713/HPR.