mtybilly/Instruct2DS huggingface dataset:
QUADify: Extracting Meshes with Pixel-level Details and Materials from Images
Authors: Maximilian Frühauf, Hayko Riemenschneider, Markus Gross, Christopher Schroers
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite exciting progress in automatic 3D reconstruction from images excessive and irregular triangular faces in the resulting meshes still constitute a significant challenge when it comes to adoption in practical artist workflows. Therefore we propose a method to extract regular quad-dominant meshes from posed images. More specifically we generate a high-quality 3D model through decomposition into an easily editable quad-dominant mesh with pixel-level details such as displacement materials and lighting. To enable end-to-end learning of shape and quad topology we QUADify a neural implicit representation using our novel differentiable re-meshing objective. Distinct from previous work our method exploits artifact-free Catmull-Clark subdivision combined with vertex displacement to extract pixel-level details linked to the base geometry. Finally we apply differentiable rendering techniques for material and lighting decomposition to optimize for image reconstruction. Our experiments show the benefits of end-to-end re-meshing and that our method yields state-of-the-art geometric accuracy while providing lightweight meshes with displacements and textures that are directly compatible with professional renderers and game engines.
RecDiffusion: Rectangling for Image Stitching with Diffusion Models
Authors: Tianhao Zhou, Haipeng Li, Ziyi Wang, Ao Luo, Chen-Lin Zhang, Jiajun Li, Bing Zeng, Shuaicheng Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Image stitching from different captures often results in non-rectangular boundaries which is often considered unappealing. To solve non-rectangular boundaries current solutions involve cropping which discards image content inpainting which can introduce unrelated content or warping which can distort non-linear features and introduce artifacts. To overcome these issues we introduce a novel diffusion-based learning framework RecDiffusion for image stitching rectangling. This framework combines Motion Diffusion Models (MDM) to generate motion fields effectively transitioning from the stitched image's irregular borders to a geometrically corrected intermediary. Followed by Content Diffusion Models (CDM) for image detail refinement. Notably our sampling process utilizes a weighted map to identify regions needing correction during each iteration of CDM. Our RecDiffusion ensures geometric accuracy and overall visual appeal surpassing all previous methods in both quantitative and qualitative measures when evaluated on public benchmarks. Code is released at https://github.com/lhaippp/RecDiffusion.
Eclipse: Disambiguating Illumination and Materials using Unintended Shadows
Authors: Dor Verbin, Ben Mildenhall, Peter Hedman, Jonathan T. Barron, Todd Zickler, Pratul P. Srinivasan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light and it is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination---even from diffuse objects---by exploiting unintended shadows like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials the surrounding illumination environment and the shapes of the unseen light occluders who inadvertently cast shadows upon it.
Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields
Authors: Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Zehao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, Achuta Kadambi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times some work has emerged that aims to extend the functionality of NeRF beyond view synthesis for semantically aware tasks such as editing and segmentation using 3D feature field distillation from 2D foundation models. However these methods have two major limitations: (a) they are limited by the rendering speed of NeRF pipelines and (b) implicitly represented feature fields suffer from continuity artifacts reducing feature quality. Recently 3D Gaussian Splatting has shown state-of-the-art performance on real-time radiance field rendering. In this work we go one step further: in addition to radiance field rendering we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation. This translation is not straightforward: naively incorporating feature fields in the 3DGS framework encounters significant challenges notably the disparities in spatial resolution and channel consistency between RGB images and feature maps. We propose architectural and training changes to efficiently avert this problem. Our proposed method is general and our experiments showcase novel view semantic segmentation language-guided editing and segment anything through learning feature fields from state-of-the-art 2D foundation models such as SAM and CLIP-LSeg. Across experiments our distillation method is able to provide comparable or better results while being significantly faster to both train and render. Additionally to the best of our knowledge we are the first method to enable point and bounding-box prompting for radiance field manipulation by leveraging the SAM model. Project website at: https://feature-3dgs.github.io/
Balancing Act: Distribution-Guided Debiasing in Diffusion Models
Authors: Rishubh Parihar, Abhijnya Bhat, Abhipsa Basu, Saswat Mallick, Jogendra Nath Kundu, R. Venkatesh Babu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However DMs reflect the biases present in the training datasets. This is especially concerning in the context of faces where the DM prefers one demographic subgroup vs others (eg. female vs male). In this work we present a method for debiasing DMs without relying on additional reference data or model retraining. Specifically we propose Distribution Guidance which enforces the generated images to follow the prescribed attribute distribution. To realize this we build on the key insight that the latent features of denoising UNet hold rich demographic semantics and the same can be leveraged to guide debiased generation. We train Attribute Distribution Predictor (ADP) - a small mlp that maps the latent features to the distribution of attributes. ADP is trained with pseudo labels generated from existing attribute classifiers. The proposed Distribution Guidance with ADP enables us to do fair generation. Our method reduces bias across single/multiple attributes and outperforms the baseline by a significant margin for unconditional and text-conditional diffusion models. Further we present a downstream task of training a fair attribute classifier by augmenting the training set with our generated data.
Viewpoint-Aware Visual Grounding in 3D Scenes
Authors: Xiangxi Shi, Zhonghua Wu, Stefan Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Referring expressions for visual objects often include descriptions of relative spatial arrangements to other objects -- e.g. "to the right of" -- that depend on the point of view of the speaker. In 2D referring expression tasks this viewpoint is captured unambiguously in the image. However grounding expressions with such spatial language in 3D without viewpoint annotations can be ambiguous. In this paper we investigate the significance of viewpoint information in 3D visual grounding -- introducing a model that explicitly predicts the speaker's viewpoint based on the referring expression and scene. We pretrain this model on a synthetically generated dataset that provides viewpoint annotations and then finetune on 3D referring expression datasets. Further we introduce an auxiliary uniform object representation loss to encourage viewpoint invariance in learned object representations. We find that our proposed ViewPoint Prediction Network (VPP-Net) achieves state-of-the-art performance on ScanRefer SR3D and NR3D -- improving Accuracy@0.25IoU by 1.06% 0.60% and 2.00% respectively compared to prior work.
4K4D: Real-Time 4D View Synthesis at 4K Resolution
Authors: Zhen Xu, Sida Peng, Haotong Lin, Guangzhao He, Jiaming Sun, Yujun Shen, Hujun Bao, Xiaowei Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recent methods on dynamic view synthesis have shown impressive rendering quality. However their speed is still limited when rendering high-resolution images. To overcome this problem we propose 4K4D a 4D point cloud representation that supports hardware rasterization and network pre-computation to enable unprecedented rendering speed with a high rendering quality. Our representation is built on a 4D feature grid so that the points are naturally regularized and can be robustly optimized. In addition we design a novel hybrid appearance model that significantly boosts the rendering quality while preserving efficiency. Moreover we develop a differentiable depth peeling algorithm to effectively learn the proposed model from RGB videos. Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU which is 30x faster than previous methods and achieves the state-of-the-art rendering quality. Our project page is available at https://zju3dv.github.io/4k4d.
View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network
Authors: Quan Zhang, Lei Wang, Vishal M. Patel, Xiaohua Xie, Jianhaung Lai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras such as ground-ground matching. However as a more practical scenario aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features namely hierarchical subtractive separation and orthogonal loss where the former separates these two features inside the VDT and the latter constrains these two to be independent. In addition we contribute a large-scale AGPReID dataset called CARGO consisting of five/eight aerial/ground cameras 5000 identities and 108563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID keeping the same magnitude of computational complexity. Our project is available at https://github.com/LinlyAC/VDT-AGPReID
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation
Authors: Lingjun Zhao, Jingyu Song, Katherine A. Skinner
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the field of 3D object detection for autonomous driving LiDAR-Camera (LC) fusion is the top-performing sensor configuration. Still LiDAR is relatively high cost which hinders adoption of this technology for consumer automobiles. Alternatively camera and radar are commonly deployed on vehicles already on the road today but performance of Camera-Radar (CR) fusion falls behind LC fusion. In this work we propose Camera-Radar Knowledge Distillation (CRKD) to bridge the performance gap between LC and CR detectors with a novel cross-modality KD framework. We use the Bird's-Eye-View (BEV) representation as the shared feature space to enable effective knowledge distillation. To accommodate the unique cross-modality KD path we propose four distillation losses to help the student learn crucial features from the teacher model. We present extensive evaluations on the nuScenes dataset to demonstrate the effectiveness of the proposed CRKD framework. The project page for CRKD is https://song-jingyu.github.io/CRKD.
Differentiable Point-based Inverse Rendering
Authors: Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present differentiable point-based inverse rendering DPIR an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end we adopt point-based rendering eliminating the need for multiple samplings per ray typical of volumetric rendering thus significantly enhancing the speed of inverse rendering. To realize this idea we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast rendering through point-based splatting while retaining the geometric details and stability inherent to SDF-based representations. The regularized basis-BRDF mitigates the ill-posedness of inverse rendering stemming from limited light-view angular samples. We also propose an efficient shadow detection method using point-based shadow map rendering. Our extensive evaluations demonstrate that DPIR outperforms prior works in terms of reconstruction accuracy computational efficiency and memory footprint. Furthermore our explicit point-based representation and rendering enables intuitive geometry and reflectance editing.
OED: Towards One-stage End-to-End Dynamic Scene Graph Generation
Authors: Guan Wang, Zhimin Li, Qingchao Chen, Yang Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pipelines which typically consist of object detection temporal association and multi-relation classification. However these methods exhibit inherent limitations due to the separation of multiple stages and independent optimization of these sub-problems may yield sub-optimal solutions. To remedy these limitations we propose a one-stage end-to-end framework termed OED which streamlines the DSGG pipeline. This framework reformulates the task as a set prediction problem and leverages pair-wise features to represent each subject-object pair within the scene graph. Moreover another challenge of DSGG is capturing temporal dependencies we introduce a Progressively Refined Module (PRM) for aggregating temporal context without the constraints of additional trackers or handcrafted trajectories enabling end-to-end optimization of the network. Extensive experiments conducted on the Action Genome benchmark demonstrate the effectiveness of our design. The code and models are available at https://github.com/guanw-pku/OED.
CoG-DQA: Chain-of-Guiding Learning with Large Language Models for Diagram Question Answering
Authors: Shaowei Wang, Lingling Zhang, Longji Zhu, Tao Qin, Kim-Hui Yap, Xinyu Zhang, Jun Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diagram Question Answering (DQA) is a challenging task requiring models to answer natural language questions based on visual diagram contexts. It serves as a crucial basis for academic tutoring technical support and more practical applications. DQA poses significant challenges such as the demand for domain-specific knowledge and the scarcity of annotated data which restrict the applicability of large-scale deep models. Previous approaches have explored external knowledge integration through pre-training but these methods are costly and can be limited by domain disparities. While Large Language Models (LLMs) show promise in question-answering there is still a gap in how to cooperate and interact with the diagram parsing process. In this paper we introduce the Chain-of-Guiding Learning Model for Diagram Question Answering (CoG-DQA) a novel framework that effectively addresses DQA challenges. CoG-DQA leverages LLMs to guide diagram parsing tools (DPTs) through the guiding chains enhancing the precision of diagram parsing while introducing rich background knowledge. Our experimental findings reveal that CoG-DQA surpasses all comparison models in various DQA scenarios achieving an average accuracy enhancement exceeding 5% and peaking at 11% across four datasets. These results underscore CoG-DQA's capacity to advance the field of visual question answering and promote the integration of LLMs into specialized domains.
Transferable and Principled Efficiency for Open-Vocabulary Segmentation
Authors: Jingxuan Xu, Wuyang Chen, Yao Zhao, Yunchao Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent success of pre-trained foundation vision-language models makes Open-Vocabulary Segmentation (OVS) possible. Despite the promising performance this approach introduces heavy computational overheads for two challenges: 1) large model sizes of the backbone; 2) expensive costs during the fine-tuning. These challenges hinder this OVS strategy from being widely applicable and affordable in real-world scenarios. Although traditional methods such as model compression and efficient fine-tuning can address these challenges they often rely on heuristics. This means that their solutions cannot be easily transferred and necessitate re-training on different models which comes at a cost. In the context of efficient OVS we target achieving performance that is comparable to or even better than prior OVS works based on large vision-language foundation models by utilizing smaller models that incur lower training costs. The core strategy is to make our efficiency principled and thus seamlessly transferable from one OVS framework to others without further customization. Comprehensive experiments on diverse OVS benchmarks demonstrate our superior trade-off between segmentation accuracy and computation costs over previous works. Our code is available on https://github.com/Xujxyang/OpenTrans
A Unified and Interpretable Emotion Representation and Expression Generation
Authors: Reni Paskaleva, Mykyta Holubakha, Andela Ilic, Saman Motamed, Luc Van Gool, Danda Paudel
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Canonical emotions such as happy sad and fear are easy to understand and annotate. However emotions are often compound e.g. happily surprised and can be mapped to the action units (AUs) used for expressing emotions and trivially to the canonical ones. Intuitively emotions are continuous as represented by the arousal-valence (AV) model. An interpretable unification of these four modalities --namely Canonical Compound AUs and AV-- is highly desirable for a better representation and understanding of emotions. However such unification remains to be unknown in the current literature. In this work we propose an interpretable and unified emotion model referred as C2A2. We also develop a method that leverages labels of the non-unified models to annotate the novel unified one. Finally we modify the text-conditional diffusion models to understand continuous numbers which are then used to generate continuous expressions using our unified emotion model. Through quantitative and qualitative experiments we show that our generated images are rich and capture subtle expressions. Our work allows a fine-grained generation of expressions in conjunction with other textual inputs and offers a new label space for emotions at the same time.
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
Authors: Shangchen Zhou, Peiqing Yang, Jianyi Wang, Yihang Luo, Chen Change Loy
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-based diffusion models have exhibited remarkable success in generation and editing showing great promise for enhancing visual content with their generative prior. However applying these models to video super-resolution remains challenging due to the high demands for output fidelity and temporal consistency which is complicated by the inherent randomness in diffusion models. Our study introduces Upscale-A-Video a text-guided latent diffusion framework for video upscaling. This framework ensures temporal coherence through two key mechanisms: locally it integrates temporal layers into U-Net and VAE-Decoder maintaining consistency within short sequences; globally without training a flow-guided recurrent latent propagation module is introduced to enhance overall video stability by propagating and fusing latent across the entire sequences. Thanks to the diffusion paradigm our model also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation enabling a trade-off between fidelity and quality. Extensive experiments show that Upscale-A-Video surpasses existing methods in both synthetic and real-world benchmarks as well as in AI-generated videos showcasing impressive visual realism and temporal consistency.
EvDiG: Event-guided Direct and Global Components Separation
Authors: Xinyu Zhou, Peiqi Duan, Boyu Li, Chu Zhou, Chao Xu, Boxin Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Separating the direct and global components of a scene aids in shape recovery and basic material understanding. Conventional methods capture multiple frames under high frequency illumination patterns or shadows requiring the scene to keep stationary during the image acquisition process. Single-frame methods simplify the capture procedure but yield lower-quality separation results. In this paper we leverage the event camera to facilitate the separation of direct and global components enabling video-rate separation of high quality. In detail we adopt an event camera to record rapid illumination changes caused by the shadow of a line occluder sweeping over the scene and reconstruct the coarse separation results through event accumulation. We then design a network to resolve the noise in the coarse separation results and restore color information. A real-world dataset is collected using a hybrid camera system for network training and evaluation. Experimental results show superior performance over state-of-the-art methods.
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning
Authors: Shuai Shao, Yu Bai, Yan Wang, Baodi Liu, Yicong Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Open-World Few-Shot Learning (OFSL) is a critical field of research concentrating on the precise identification of target samples in environments with scarce data and unreliable labels thus possessing substantial practical significance. Recently the evolution of foundation models like CLIP has revealed their strong capacity for representation even in settings with restricted resources and data. This development has led to a significant shift in focus transitioning from the traditional method of "building models from scratch" to a strategy centered on "efficiently utilizing the capabilities of foundation models to extract relevant prior knowledge tailored for OFSL and apply it judiciously". Amidst this backdrop we unveil the Direct-and-Inverse CLIP (DeIL) an innovative method leveraging our proposed "Direct-and-Inverse" concept to activate CLIP-based methods for addressing OFSL. This concept transforms conventional single-step classification into a nuanced two-stage process: initially filtering out less probable categories followed by accurately determining the specific category of samples. DeIL comprises two key components: a pre-trainer (frozen) for data denoising and an adapter (tunable) for achieving precise final classification. In experiments DeIL achieves SOTA performance on 11 datasets.
4D-DRESS: A 4D Dataset of Real-World Human Clothing With Semantic Annotations
Authors: Wenbo Wang, Hsuan-I Ho, Chen Guo, Boxiang Rong, Artur Grigorev, Jie Song, Juan Jose Zarate, Otmar Hilliges
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The studies of human clothing for digital avatars have predominantly relied on synthetic datasets. While easy to collect synthetic data often fall short in realism and fail to capture authentic clothing dynamics. Addressing this gap we introduce 4D-DRESS the first real-world 4D dataset advancing human clothing research with its high-quality 4D textured scans and garment meshes. 4D-DRESS captures 64 outfits in 520 human motion sequences amounting to 78k textured scans. Creating a real-world clothing dataset is challenging particularly in annotating and segmenting the extensive and complex 4D human scans. To address this we develop a semi-automatic 4D human parsing pipeline. We efficiently combine a human-in-the-loop process with automation to accurately label 4D scans in diverse garments and body movements. Leveraging precise annotations and high-quality garment meshes we establish several benchmarks for clothing simulation and reconstruction. 4D-DRESS offers realistic and challenging data that complements synthetic sources paving the way for advancements in research of lifelike human clothing. Website: https://ait.ethz.ch/4d-dress
Feedback-Guided Autonomous Driving
Authors: Jimuyang Zhang, Zanming Huang, Arijit Ray, Eshed Ohn-Bar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: While behavior cloning has recently emerged as a highly successful paradigm for autonomous driving humans rarely learn to perform complex tasks such as driving via imitation or behavior cloning alone. In contrast learning in humans often involves additional detailed guidance throughout the interactive learning process i.e. where feedback often via language provides detailed information as to which part of their trial was performed incorrectly or suboptimally and why. Motivated by this observation we introduce an efficient feedback-based framework for improving behavior-cloning-based training of sensorimotor driving agents. Our key insight is to leverage recent advances in Large Language Models (LLMs) to provide corrective fine-grained feedback regarding the underlying reason behind driving prediction failures. Moreover our introduced network architecture is efficient enabling the first sensorimotor end-to-end training and evaluation of LLM-based driving models. The resulting agent achieves state-of-the-art performance in open-loop evaluation on nuScenes outperforming prior state-of-the-art by over 8.1% and 57.1% in accuracy and collision rate respectively. In CARLA our camera-based agent improves by 16.6% in driving score over prior LIDAR-based approaches.
Large Language Models are Good Prompt Learners for Low-Shot Image Classification
Authors: Zhaoheng Zheng, Jingmin Wei, Xuefeng Hu, Haidong Zhu, Ram Nevatia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Low-shot image classification where training images are limited or inaccessible has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only have confined class-specific information. Large Language Models (LLMs) with their vast encyclopedic knowledge emerge as the complement. Thus in this paper we discuss the integration of LLMs to enhance pre-trained VL models specifically on low-shot classification. However the domain gap between language and vision blocks the direct application of LLMs. Thus we propose LLaMP Large Language Models as Prompt learners that produces adaptive prompts for the CLIP text encoder establishing it as the connecting bridge. Experiments show that compared with other state-of-the-art prompt learning methods LLaMP yields better performance on both zero-shot generalization and few-shot image classification over a spectrum of 11 datasets. Code will be made available at: https://github.com/zhaohengz/LLaMP.
Specularity Factorization for Low-Light Enhancement
Authors: Saurabh Saini, P J Narayanan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition. Our model-driven RSFNet estimates these factors by unrolling the optimization into network layers requiring only a few scalars to be learned. The resultant factors are interpretable by design and can be fused for different image enhancement tasks via a network or combined directly by the user in a controllable fashion. Based on RSFNet we detail a zero-reference Low Light Enhancement (LLE) application trained without paired or unpaired supervision. Our system improves the state-of-the-art performance on standard benchmarks and achieves better generalization on multiple other datasets. We also integrate our factors with other task specific fusion networks for applications like deraining deblurring and dehazing with negligible overhead thereby highlighting the multi-domain and multi-task generalizability of our proposed RSFNet. The code and data is released for reproducibility on the project homepage.
Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models
Authors: Xianfang Zeng, Xin Chen, Zhongqi Qi, Wen Liu, Zibo Zhao, Zhibin Wang, Bin Fu, Yong Liu, Gang Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper presents Paint3D a novel coarse-to-fine generative framework that is capable of producing high-resolution lighting-less and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and perform multi-view texture fusion producing an initial coarse texture map. However as 2D models cannot fully represent 3D shapes and disable lighting effects the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware refinement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process Paint3D can produce high-quality 2K UV textures that maintain semantic consistency while being lighting-less significantly advancing the state-of-the-art in texturing 3D objects.
VILA: On Pre-training for Visual Language Models
Authors: Ji Lin, Hongxu Yin, Wei Ping, Pavlo Molchanov, Mohammad Shoeybi, Song Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs but lacks an in-depth study of the visual language pre-training process where the model learns to perform joint modeling on both modalities. In this work we examine the design options for VLM pre-training by augmenting LLM towards VLM through step-by-step con- trollable comparisons. We introduce three main findings: (1) freezing LLMs during pre-training can achieve decent zero-shot performance but lack in-context learning capabil- ity which requires unfreezing the LLM; (2) interleaved pre- training data is beneficial whereas image-text pairs alone are not optimal; (3) re-blending text-only instruction data to image-text data during instruction fine-tuning not only remedies the degradation of text-only tasks but also boosts VLM task accuracy. With an enhanced pre-training recipe we build VILA a Visual Language model family that consis- tently outperforms the state-of-the-art models e.g. LLaVA- 1.5 across main benchmarks without bells and whistles. Multi-modal pre-training also helps unveil appealing prop- erties of VILA including multi-image reasoning enhanced in-context learning and better world knowledge. VILA is also deployable on Jetson Orin for on-device VLM.
DiLiGenRT: A Photometric Stereo Dataset with Quantified Roughness and Translucency
Authors: Heng Guo, Jieji Ren, Feishi Wang, Boxin Shi, Mingjun Ren, Yasuyuki Matsushita
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Photometric stereo faces challenges from non-Lambertian reflectance in real-world scenarios. Systematically measuring the reliability of photometric stereo methods in handling such complex reflectance necessitates a real-world dataset with quantitatively controlled reflectances. This paper introduces DiLiGenRT the first real-world dataset for evaluating photometric stereo methods under quantified reflectances by manufacturing 54 hemispheres with varying degrees of two reflectance properties: Roughness and Translucency. Unlike qualitative and semantic labels such as diffuse and specular that have been used in previous datasets our quantified dataset allows comprehensive and systematic benchmark evaluations. In addition it facilitates selecting best-fit photometric stereo methods based on the quantitative reflectance properties. Our dataset and benchmark results are available at https://photometricstereo.github.io/diligentrt.html.
De-Diffusion Makes Text a Strong Cross-Modal Interface
Authors: Chen Wei, Chenxi Liu, Siyuan Qiao, Zhishuai Zhang, Alan Yuille, Jiahui Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We demonstrate text as a strong cross-modal interface. Rather than relying on deep embeddings to connect image and language as the interface representation our approach represents an image as text from which we enjoy the interpretability and flexibility inherent to natural language. We employ an autoencoder that uses a pre-trained text-to-image diffusion model for decoding. The encoder is trained to transform an input image into text which is then fed into the fixed text-to-image diffusion decoder to reconstruct the original input a process we term De-Diffusion. Experiments validate both the precision and comprehensiveness of De-Diffusion text representing images such that it can be readily ingested by off-the-shelf text-to-image tools and LLMs for diverse multi-modal tasks. For example a single De-Diffusion model can generalize to provide transferable prompts for different text-to-image tools and also achieves a new state of the art on open-ended vision-language tasks by simply prompting large language models with few-shot examples. Project page: https://dediffusion.github.io/
End-to-End Spatio-Temporal Action Localisation with Video Transformers
Authors: Alexey A. Gritsenko, Xuehan Xiong, Josip Djolonga, Mostafa Dehghani, Chen Sun, Mario Lucic, Cordelia Schmid, Anurag Arnab
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks. We propose a fully end-to-end transformer based model that directly ingests an input video and outputs tubelets -- a sequence of bounding boxes and the action classes at each frame. Our flexible model can be trained with either sparse bounding-box supervision on individual frames or full tubelet annotations. And in both cases it predicts coherent tubelets as the output. Moreover our end-to-end model requires no additional pre-processing in the form of proposals or post-processing in terms of non-maximal suppression. We perform extensive ablation experiments and significantly advance the state-of-the-art on five different spatio-temporal action localisation benchmarks with both sparse keyframes and full tubelet annotations.
Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation
Authors: Mingyu Lee, Jongwon Choi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object learned from extensive text library documents to generate non-defective data images resembling the input image. The proposed framework ensures that the generated non-defective images align with anticipated distributions derived from textual and image-based knowledge ensuring stability and generality. Experimental results demonstrate the effectiveness of our approach surpassing previous methods even with limited non-defective data. Our approach is validated through generalization tests across four baseline models and three distinct datasets. We present an additional analysis to enhance the effectiveness of anomaly detection models by utilizing the generated images.
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution
Authors: Qingping Zheng, Ling Zheng, Yuanfan Guo, Ying Li, Songcen Xu, Jiankang Deng, Hang Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content eliminating any distortions or synthetic details. While traditional diffusion-based SR techniques have demonstrated remarkable abilities to enhance image detail they are prone to artifact introduction during iterative procedures. Such artifacts ranging from trivial noise to unauthentic textures deviate from the true structure of the source image thus challenging the integrity of the super-resolution process. In this work we propose Self-Adaptive Reality-Guided Diffusion (SARGD) a training-free method that delves into the latent space to effectively identify and mitigate the propagation of artifacts. Our SARGD begins by using an artifact detector to identify implausible pixels creating a binary mask that highlights artifacts. Following this the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations improving alignment with the original image. Nonetheless initial realistic-latent representations from lower-quality images result in over-smoothing in the final output. To address this we introduce a Self-Adaptive Guidance (SAG) mechanism. It dynamically computes a reality score enhancing the sharpness of the realistic latent. These alternating mechanisms collectively achieve artifact-free super-resolution. Extensive experiments demonstrate the superiority of our method delivering detailed artifact-free high-resolution images while reducing sampling steps by 2X. We release our code at https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion.git.
End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames
Authors: Shuming Liu, Chen-Lin Zhang, Chen Zhao, Bernard Ghanem
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently temporal action detection (TAD) has seen significant performance improvement with end-to-end training. However due to the memory bottleneck only models with limited scales and limited data volumes can afford end-to-end training which inevitably restricts TAD performance. In this paper we reduce the memory consumption for end-to-end training and manage to scale up the TAD backbone to 1 billion parameters and the input video to 1536 frames leading to significant detection performance. The key to our approach lies in our proposed temporal-informative adapter (TIA) which is a novel lightweight module that reduces training memory. Using TIA we free the humongous backbone from learning to adapt to the TAD task by only updating the parameters in TIA. TIA also leads to better TAD representation by temporally aggregating context from adjacent frames throughout the backbone. We evaluate our model across four representative datasets. Owing to our efficient design we are able to train end-to-end on VideoMAEv2-giant and achieve 75.4% mAP on THUMOS14 being the first end-to-end model to outperform the best feature-based methods.
Multimodal Representation Learning by Alternating Unimodal Adaptation
Authors: Xiaohui Zhang, Jaehong Yoon, Mohit Bansal, Huaxiu Yao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multimodal learning which integrates data from diverse sensory modes plays a pivotal role in artificial intelligence. However existing multimodal learning methods often struggle with challenges where some modalities appear more dominant than others during multimodal learning resulting in suboptimal performance. To address this challenge we propose MLA (Multimodal Learning with Alternating Unimodal Adaptation). MLA reframes the conventional joint multimodal learning process by transforming it into an alternating unimodal learning process thereby minimizing interference between modalities. Simultaneously it captures cross-modal interactions through a shared head which undergoes continuous optimization across different modalities. This optimization process is controlled by a gradient modification mechanism to prevent the shared head from losing previously acquired information. During the inference phase MLA utilizes a test-time uncertainty-based model fusion mechanism to integrate multimodal information. Extensive experiments are conducted on five diverse datasets encompassing scenarios with complete modalities and scenarios with missing modalities. These experiments demonstrate the superiority of MLA over competing prior approaches. Our code is available at https://github.com/Cecile-hi/Multimodal-Learning-with-Alternating-Unimodal-Adaptation.
MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints
Authors: Pengfei Xie, Wenqiang Xu, Tutian Tang, Zhenjun Yu, Cewu Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This work proposes a novel learning framework for visual hand dynamics analysis that takes into account the physiological aspects of hand motion. The existing models which are simplified joint-actuated systems often produce unnatural motions. To address this we integrate a musculoskeletal system with a learnable parametric hand model MANO to create a new model MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system imposing physiologically realistic constraints on the resulting torque trajectories. We further propose a simulation-in-the-loop pose refinement framework BioPR that refines the initial estimated pose through a multi-layer perceptron (MLP) network. Our evaluation of the accuracy of MS-MANO and the efficacy of the BioPR is conducted in two separate parts. The accuracy of MS-MANO is compared with MyoSuite while the efficacy of BioPR is benchmarked against two large-scale public datasets and two recent state-of-the-art methods. The results demonstrate that our approach consistently improves the baseline methods both quantitatively and qualitatively.
Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models
Authors: Bin Fu, Fanghua Yu, Anran Liu, Zixuan Wang, Jie Wen, Junjun He, Yu Qiao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-content disentanglement paradigm and employ the Generative Adversarial Network (GAN) to generate target fonts by combining the decoupled content and style representations. The complicated structure and detailed style are simultaneously generated in those methods which may be the sub-optimal solutions for FFG task. Inspired by most manual font design processes of expert designers in this paper we model font generation as a multi-stage generative process. Specifically as the injected noise and the data distribution in diffusion models can be well-separated into different sub-spaces we are able to incorporate the font transfer process into these models. Based on this observation we generalize diffusion methods to model font generative process by separating the reverse diffusion process into three stages with different functions: The structure construction stage first generates the structure information for the target character based on the source image and the font transfer stage subsequently transforms the source font to the target font. Finally the font refinement stage enhances the appearances and local details of the target font images. Based on the above multi-stage generative process we construct our font generation framework named MSD-Font with a dual-network approach to generate font images. The superior performance demonstrates the effectiveness of our model. The code is available at: https://github.com/fubinfb/MSD-Font .