GenAI4ELab/papercli-papers huggingface dataset:
From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic Segmentation
Authors: Hyeokjun Kweon; Kuk-Jin Yoon
Venue: CVPR 2024 (main)

Abstract: Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of segmentation using image-level class labels. Recent WSSS works have shown promising results by using the Segment Anything Model (SAM) a foundation model for segmentation during the inference phase. However we observe that these methods can still be vulnerable to the noise of class activation maps (CAMs) serving as initial seeds. As a remedy this paper introduces From-SAM-to-CAMs (S2C) a novel WSSS framework that directly transfers the knowledge of SAM to the classifier during the training process enhancing the quality of CAMs itself. S2C comprises SAM-segment Contrasting (SSC) and a CAM-based prompting module (CPM) which exploit SAM at the feature and logit levels respectively. SSC performs prototype-based contrasting using SAM's automatic segmentation results. It constrains each feature to be close to the prototype of its segment and distant from prototypes of the others. Meanwhile CPM extracts prompts from the CAM of each class and uses them to generate class-specific segmentation masks through SAM. The masks are aggregated into unified self-supervision based on the confidence score designed to consider the reliability of both SAM and CAMs. S2C achieves a new state-of-the-art performance across all benchmarks outperforming existing studies by significant margins. The code is available at https://github.com/sangrockEG/S2C.
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
Authors: Li-Yuan Tsao; Yi-Chen Lo; Chia-Che Chang; Hao-Wei Chen; Roy Tseng; Chien Feng; Chun-Yi Lee
Venue: CVPR 2024 (main)

Abstract: Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However these methods encounter several challenges during image generation such as grid artifacts exploding inverses and suboptimal results due to a fixed sampling temperature. To overcome these issues this work introduces a conditional learned prior to the inference phase of a flow-based SR model. This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image which is then transformed by the flow model into an SR image. Our framework is designed to seamlessly integrate with any contemporary flow-based SR model without modifying its architecture or pre-trained weights. We evaluate the effectiveness of our proposed framework through extensive experiments and ablation analyses. The proposed framework successfully addresses all the inherent issues in flow-based SR models and enhances their performance in various SR scenarios. Our code is available at: https://github.com/liyuantsao/FlowSR-LP
How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?
Authors: Subhadeep Koley; Ayan Kumar Bhunia; Aneeshan Sain; Pinaki Nath Chowdhury; Tao Xiang; Yi-Zhe Song
Venue: CVPR 2024 (main)

Abstract: In this paper we propose a novel abstraction-aware sketch-based image retrieval framework capable of handling sketch abstraction at varied levels. Prior works had mainly focused on tackling sub-factors such as drawing style and order we instead attempt to model abstraction as a whole and propose feature-level and retrieval granularity-level designs so that the system builds into its DNA the necessary means to interpret abstraction. On learning abstraction-aware features we for the first-time harness the rich semantic embedding of pre-trained StyleGAN model together with a novel abstraction-level mapper that deciphers the level of abstraction and dynamically selects appropriate dimensions in the feature matrix correspondingly to construct a feature matrix embedding that can be freely traversed to accommodate different levels of abstraction. For granularity-level abstraction understanding we dictate that the retrieval model should not treat all abstraction-levels equally and introduce a differentiable surrogate Acc.@q loss to inject that understanding into the system. Different to the gold-standard triplet loss our Acc.@q loss uniquely allows a sketch to narrow/broaden its focus in terms of how stringent the evaluation should be - the more abstract a sketch the less stringent (higher q). Extensive experiments depict our method to outperform existing state-of-the-arts in standard SBIR tasks along with challenging scenarios like early retrieval forensic sketch-photo matching and style-invariant retrieval.
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
Authors: Alex Trevithick; Matthew Chan; Towaki Takikawa; Umar Iqbal; Shalini De Mello; Manmohan Chandraker; Ravi Ramamoorthi; Koki Nagano
Venue: CVPR 2024 (main)

Abstract: 3D-aware Generative Adversarial Networks (GANs) have shown remarkable progress in learning to generate multi-view-consistent images and 3D geometries of scenes from collections of 2D images via neural volume rendering. Yet the significant memory and computational costs of dense sampling in volume rendering have forced 3D GANs to adopt patch-based training or employ low-resolution rendering with post-processing 2D super resolution which sacrifices multiview consistency and the quality of resolved geometry. Consequently 3D GANs have not yet been able to fully resolve the rich 3D geometry present in 2D images. In this work we propose techniques to scale neural volume rendering to the much higher resolution of native 2D images thereby resolving fine-grained 3D geometry with unprecedented detail. Our approach employs learning-based samplers for accelerating neural rendering for 3D GAN training using up to 5 times fewer depth samples. This enables us to explicitly "render every pixel" of the full-resolution image during training and inference without post-processing superresolution in 2D. Together with our strategy to learn high-quality surface geometry our method synthesizes high-resolution 3D geometry and strictly view-consistent images while maintaining image quality on par with baselines relying on post-processing super resolution. We demonstrate state-of-the-art 3D gemetric quality on FFHQ and AFHQ setting a new standard for unsupervised learning of 3D shapes in 3D GANs.
Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
Authors: Jiwoo Chung; Sangeek Hyun; Jae-Pil Heo
Venue: CVPR 2024 (main)

Abstract: Despite the impressive generative capabilities of diffusion models existing diffusion model-based style transfer methods require inference-stage optimization (e.g. fine-tuning or textual inversion of style) which is time-consuming or fails to leverage the generative ability of large-scale diffusion models. To address these issues we introduce a novel artistic style transfer method based on a pre-trained large-scale diffusion model without any optimization. Specifically we manipulate the features of self-attention layers as the way the cross-attention mechanism works; in the generation process substituting the key and value of content with those of style image. This approach provides several desirable characteristics for style transfer including 1) preservation of content by transferring similar styles into similar image patches and 2) transfer of style based on similarity of local texture (e.g. edge) between content and style images. Furthermore we introduce query preservation and attention temperature scaling to mitigate the issue of disruption of original content and initial latent Adaptive Instance Normalization (AdaIN) to deal with the disharmonious color (failure to transfer the colors of style). Our experimental results demonstrate that our proposed method surpasses state-of-the-art methods in both conventional and diffusion-based style transfer baselines.
Towards Robust Learning to Optimize with Theoretical Guarantees
Authors: Qingyu Song; Wei Lin; Juncheng Wang; Hong Xu
Venue: CVPR 2024 (main)

Abstract: Learning to optimize (L2O) is an emerging technique to solve mathematical optimization problems with learning-based methods. Although with great success in many real-world scenarios such as wireless communications computer networks and electronic design existing L2O works lack theoretical demonstration of their performance and robustness in out-of-distribution (OOD) scenarios. We address this gap by providing comprehensive proofs. First we prove a sufficient condition for a robust L2O model with homogeneous convergence rates over all In-Distribution (InD) instances. We assume an L2O model achieves robustness for an InD scenario. Based on our proposed methodology of aligning OOD problems to InD problems we also demonstrate that the L2O model's convergence rate in OOD scenarios will deteriorate by an equation of the L2O model's input features. Moreover we propose an L2O model with a concise gradient-only feature construction and a novel gradient-based history modeling method. Numerical simulation demonstrates that our proposed model outperforms the state-of-the-art baseline in both InD and OOD scenarios and achieves up to 10 xconvergence speedup. The code of our method can be found from https://github.com/NetX-lab/GoMathL2O-Official.
Differentiable Neural Surface Refinement for Modeling Transparent Objects
Authors: Weijian Deng; Dylan Campbell; Chunyi Sun; Shubham Kanitkar; Matthew E. Shaffer; Stephen Gould
Venue: CVPR 2024 (main)

Abstract: Neural implicit surface reconstruction leveraging volume rendering has led to significant advances in multi-view reconstruction. However results for transparent objects can be very poor primarily because the rendering function fails to account for the intricate light transport induced by refraction and reflection. In this study we introduce transparent neural surface refinement (TNSR) a novel surface reconstruction framework that explicitly incorporates physical refraction and reflection tracing. Beginning with an initial approximate surface our method employs sphere tracing combined with Snell's law to cast both reflected and refracted rays. Central to our proposal is an innovative differentiable technique devised to allow signals from the photometric evidence to propagate back to the surface model by considering how the surface bends and reflects light rays. This allows us to connect surface refinement with volume rendering enabling end-to-end optimization solely on multi-view RGB images. In our experiments TNSR demonstrates significant improvements in novel view synthesis and geometry estimation of transparent objects without prior knowledge of the refractive index.
OrthCaps: An Orthogonal CapsNet with Sparse Attention Routing and Pruning
Authors: Xinyu Geng; Jiaming Wang; Jiawei Gong; Yuerong Xue; Jun Xu; Fanglin Chen; Xiaolin Huang
Venue: CVPR 2024 (main)

Abstract: Redundancy is a persistent challenge in Capsule Networks (CapsNet) leading to high computational costs and parameter counts. Although previous studies have introduced pruning after the initial capsule layer dynamic routing's fully connected nature and non-orthogonal weight matrices reintroduce redundancy in deeper layers. Besides dynamic routing requires iterating to converge further increasing computational demands. In this paper we propose an Orthogonal Capsule Network (OrthCaps) to reduce redundancy improve routing performance and decrease parameter counts. Firstly an efficient pruned capsule layer is introduced to discard redundant capsules. Secondly dynamic routing is replaced with orthogonal sparse attention routing eliminating the need for iterations and fully connected structures. Lastly weight matrices during routing are orthogonalized to sustain low capsule similarity which is the first approach to use Householder orthogonal decomposition to enforce orthogonality in CapsNet. Our experiments on baseline datasets affirm the efficiency and robustness of OrthCaps in classification tasks in which ablation studies validate the criticality of each component. OrthCaps-Shallow outperforms other Capsule Network benchmarks on four datasets utilizing only 110k parameters - a mere 1.25% of a standard Capsule Network's total. To the best of our knowledge it achieves the smallest parameter count among existing Capsule Networks. Similarly OrthCaps-Deep demonstrates competitive performance across four datasets utilizing only 1.2% of the parameters required by its counterparts.
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
Authors: Kaipeng Fang; Jingkuan Song; Lianli Gao; Pengpeng Zeng; Zhi-Qi Cheng; Xiyao Li; Heng Tao Shen
Venue: CVPR 2024 (main)

Abstract: The goal of Universal Cross-Domain Retrieval (UCDR) is to achieve robust performance in generalized test scenarios wherein data may belong to strictly unknown domains and categories during training. Recently pre-trained models with prompt tuning have shown strong generalization capabilities and attained noteworthy achievements in various downstream tasks such as few-shot learning and video-text retrieval. However applying them directly to UCDR may not be sufficient to handle both domain shift (i.e. adapting to unfamiliar domains) and semantic shift (i.e. transferring to unknown categories). To this end we propose Prompting-to-Simulate (ProS) the first method to apply prompt tuning for UCDR. ProS employs a two-step process to simulate Content-aware Dynamic Prompts (CaDP) which can impact models to produce generalized features for UCDR. Concretely in Prompt Units Learning stage we introduce two Prompt Units to individually capture domain and semantic knowledge in a mask-and-align way. Then in Context-aware Simulator Learning stage we train a Content-aware Prompt Simulator under a simulated test scenario to produce the corresponding CaDP. Extensive experiments conducted on three benchmark datasets show that our method achieves new state-of-the-art performance without bringing excessive parameters. Code is available at https://github.com/fangkaipeng/ProS
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
Authors: Bin Xiao; Haiping Wu; Weijian Xu; Xiyang Dai; Houdong Hu; Yumao Lu; Michael Zeng; Ce Liu; Lu Yuan
Venue: CVPR 2024 (main)

Abstract: We introduce Florence-2 a novel vision foundation model with a unified prompt-based representation for various computer vision and vision-language tasks. While existing large vision models excel in transfer learning they struggle to perform diverse tasks with simple instructions a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms whether it be captioning object detection grounding or segmentation. This multi-task learning setup demands large-scale high-quality annotated data. To this end we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with unprecedented zero-shot and fine-tuning capabilities.
NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild
Authors: Weining Ren; Zihan Zhu; Boyang Sun; Jiaqi Chen; Marc Pollefeys; Songyou Peng
Venue: CVPR 2024 (main)

Abstract: Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing photorealistic views from multi-view images of static scenes but face challenges in dynamic real-world environments with distractors like moving objects shadows and lighting changes. Existing methods manage controlled environments and low occlusion ratios but fall short in render quality especially under high occlusion scenarios. In this paper we introduce NeRF On-the-go a simple yet effective approach that enables the robust synthesis of novel views in complex in-the-wild scenes from only casually captured image sequences. Delving into uncertainty our method not only efficiently eliminates distractors even when they are predominant in captures but also achieves a notably faster convergence speed. Through comprehensive experiments on various scenes our method demonstrates a significant improvement over state-of-the-art techniques. This advancement opens new avenues for NeRF in diverse and dynamic real-world applications.
3D Human Pose Perception from Egocentric Stereo Videos
Authors: Hiroyasu Akada; Jian Wang; Vladislav Golyanik; Christian Theobalt
Venue: CVPR 2024 (main)

Abstract: While head-mounted devices are becoming more compact they provide egocentric views with significant self-occlusions of the device user. Hence existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation which leverages the scene information and temporal context of egocentric stereo videos. Specifically we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios such as crouching and sitting. Furthermore we introduce two new benchmark datasets i.e. UnrealEgo2 and UnrealEgo-RW (RealWorld). UnrealEgo2 is a large-scale in-the-wild dataset captured in synthetic 3D scenes. UnrealEgo-RW is a real-world dataset captured with our newly developed device. The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. UnrealEgo2 UnrealEgo-RW and trained models are available on our project page and Benchmark Challenge.
Grid Diffusion Models for Text-to-Video Generation
Authors: Taegyeong Lee; Soyeong Kwon; Taehwan Kim
Venue: CVPR 2024 (main)

Abstract: Recent advances in the diffusion models have significantly improved text-to-image generation. However generating videos from text is a more challenging task than generating images from text due to the much larger dataset and higher computational cost required. Most existing video generation methods use either a 3D U-Net architecture that considers the temporal dimension or autoregressive generation. These methods require large datasets and are limited in terms of computational costs compared to text-to-image generation. To tackle these challenges we propose a simple but effective novel grid diffusion for text-to-video generation without temporal dimension in architecture and a large text-video paired dataset. We can generate a high-quality video using a fixed amount of GPU memory regardless of the number of frames by representing the video as a grid image. Additionally since our method reduces the dimensions of the video to the dimensions of the image various image-based methods can be applied to videos such as text-guided video manipulation from image manipulation. Our proposed method outperforms the existing methods in both quantitative and qualitative evaluations demonstrating the suitability of our model for real-world video generation.
Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation
Authors: Zhipeng Du; Miaojing Shi; Jiankang Deng
Venue: CVPR 2024 (main)

Abstract: Detecting objects in low-light scenarios presents a persistent challenge as detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. Previous methods mitigate this issue by exploring image enhancement or object detection techniques with real low-light image datasets. However the progress is impeded by the inherent difficulties about collecting and annotating low-light images. To address this challenge we propose to boost low-light object detection with zero-shot day-night domain adaptation which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data. Revisiting Retinex theory in the low-level vision we first design a reflectance representation learning module to learn Retinex-based illumination invariance in images with a carefully designed illumination invariance reinforcement strategy. Next an interchange-redecomposition-coherence procedure is introduced to improve over the vanilla Retinex image decomposition process by performing two sequential image decompositions and introducing a redecomposition cohering loss. Extensive experiments on ExDark DARK FACE and CODaN datasets show strong low-light generalizability of our method. Our code is available at https://github.com/ZPDu/DAI-Net.
LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching
Authors: Yixun Liang; Xin Yang; Jiantao Lin; Haodong Li; Xiaogang Xu; Yingcong Chen
Venue: CVPR 2024 (main)

Abstract: The recent advancements in text-to-3D generation mark a significant milestone in generative models unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS that it brings inconsistent and low-quality updating direction for the 3D model causing the over-smoothing effect. To address this we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild
Authors: Kun Yuan; Hongbo Liu; Mading Li; Muyi Sun; Ming Sun; Jiachao Gong; Jinhua Hao; Chao Zhou; Yansong Tang
Venue: CVPR 2024 (main)

Abstract: Video quality assessment (VQA) is a challenging problem due to the numerous factors that can affect the perceptual quality of a video e.g. content attractiveness distortion type motion pattern and level. However annotating the Mean opinion score (MOS) for videos is expensive and time-consuming which limits the scale of VQA datasets and poses a significant obstacle for deep learning-based methods. In this paper we propose a VQA method named PTM-VQA which leverages PreTrained Models to transfer knowledge from models pretrained on various pre-tasks enabling benefits for VQA from different aspects. Specifically we extract features of videos from different pretrained models with frozen weights and integrate them to generate representation. Since these models possess various fields of knowledge and are often trained with labels irrelevant to quality we propose an Intra-Consistency and Inter-Divisibility (ICID) loss to impose constraints on features extracted by multiple pretrained models. The intra-consistency constraint ensures that features extracted by different pretrained models are in the same unified quality-aware latent space while the inter-divisibility introduces pseudo clusters based on the annotation of samples and tries to separate features of samples from different clusters. Furthermore with a constantly growing number of pretrained models it is crucial to determine which models to use and how to use them. To address this problem we propose an efficient scheme to select suitable candidates. Models with better clustering performance on VQA datasets are chosen to be our candidates. Extensive experiments demonstrate the effectiveness of the proposed method.