GenAI4ELab/papercli-papers huggingface dataset:
Advancing Saliency Ranking with Human Fixations: Dataset Models and Benchmarks
Authors: Bowen Deng; Siyang Song; Andrew P. French; Denis Schluppeck; Michael P. Pound
Venue: CVPR 2024 (main)

Abstract: Saliency ranking detection (SRD) has emerged as a challenging task in computer vision aiming not only to identify salient objects within images but also to rank them based on their degree of saliency. Existing SRD datasets have been created primarily using mouse-trajectory data which inadequately captures the intricacies of human visual perception. Addressing this gap this paper introduces the first large-scale SRD dataset SIFR constructed using genuine human fixation data thereby aligning more closely with real visual perceptual processes. To establish a baseline for this dataset we propose QAGNet a novel model that leverages salient instance query features from a transformer detector within a tri-tiered nested graph. Through extensive experiments we demonstrate that our approach outperforms existing state-of-the-art methods across two widely used SRD datasets and our newly proposed dataset. Code and dataset are available at https://github.com/EricDengbowen/QAGNet.
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis
Authors: Willi Menapace; Aliaksandr Siarohin; Ivan Skorokhodov; Ekaterina Deyneka; Tsai-Shien Chen; Anil Kag; Yuwei Fang; Aleksei Stoliar; Elisa Ricci; Jian Ren; Sergey Tulyakov
Venue: CVPR 2024 (main)

Abstract: Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages the research community repurposes them to generate videos. Since video content is highly redundant we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity visual quality and impairs scalability. In this work we build Snap Video a video-first model that systematically addresses these challenges. To do that we first extend the EDM framework to take into account spatially and temporally redundant pixels and naturally support video generation. Second we show that a U-Net--a workhorse behind image generation--scales poorly when generating videos requiring significant computational overhead. Hence we propose a new transformer-based architecture that trains 3.31 times faster than U-Nets (and is  4.5 faster at inference). This allows us to efficiently train a text-to-video model with billions of parameters for the first time reach state-of-the-art results on a number of benchmarks and generate videos with substantially higher quality temporal consistency and motion complexity. The user studies showed that our model was favored by a large margin over the most recent methods.
Unsupervised Deep Unrolling Networks for Phase Unwrapping
Authors: Zhile Chen; Yuhui Quan; Hui Ji
Venue: CVPR 2024 (main)

Abstract: Phase unwrapping (PU) is a technique to reconstruct original phase images from their noisy wrapped counterparts finding many applications in scientific imaging. Although supervised learning has shown promise in PU its utility is limited in ground-truth (GT) scarce scenarios. This paper presents an unsupervised learning approach that eliminates the need for GTs during end-to-end training. Our approach leverages the insight that both the gradients and wrapped gradients of wrapped phases serve as noisy labels for GT phase gradients along with sparse outliers induced by the wrapping operation. A recorruption-based self-reconstruction loss in the gradient domain is proposed to mitigate the adverse effects of label noise complemented with a self-distillation loss for improved generalization. Additionally by unfolding a variational model of PU that utilizes wrapped gradients of wrapped phases for its data-fitting term we develop a deep unrolling network that encodes physics of phase wrapping and incorporates special treatments on outliers. In the experiments on three types of phase data our approach outperforms existing GT-free methods and competes well against the supervised ones.
Federated Generalized Category Discovery
Authors: Nan Pu; Wenjing Li; Xingyuan Ji; Yalan Qin; Nicu Sebe; Zhun Zhong
Venue: CVPR 2024 (main)

Abstract: Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are distributed in local clients and cannot be shared among clients. Fed-GCD aims to train a generic GCD model by client collaboration under the privacy-protected constraint. The Fed-GCD leads to two challenges: 1) representation degradation caused by training each client model with fewer data than centralized GCD learning and 2) highly heterogeneous label spaces across different clients. To this end we propose a novel Associated Gaussian Contrastive Learning (AGCL) framework based on learnable GMMs which consists of a Client Semantics Association (CSA) and a global-local GMM Contrastive Learning (GCL). On the server CSA aggregates the heterogeneous categories of local-client GMMs to generate a global GMM containing more comprehensive category knowledge. On each client GCL builds class-level contrastive learning with both local and global GMMs. The local GCL learns robust representation with limited local data. The global GCL encourages the model to produce more discriminative representation with the comprehensive category relationships that may not exist in local data. We build a benchmark based on six visual datasets to facilitate the study of Fed-GCD. Extensive experiments show that our AGCL outperforms multiple baselines on all datasets.
JointSQ: Joint Sparsification-Quantization for Distributed Learning
Authors: Weiying Xie; Haowei Li; Jitao Ma; Yunsong Li; Jie Lei; Donglai Liu; Leyuan Fang
Venue: CVPR 2024 (main)

Abstract: Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures. Specifically we mathematically formulate JointSQ as a mixed-precision quantization problem expanding the solution space. It can be solved by the designed MCKP-Greedy algorithm. Theoretical analysis demonstrates the minimal compression noise of JointSQ and extensive experiments on various network architectures including CNN RNN and Transformer also validate this point. Under the introduction of computation overhead consistent with or even lower than previous methods JointSQ achieves a compression ratio of 1000xon different models while maintaining near-lossless accuracy and brings 1.4xto 2.9xspeedup over existing methods.
A Unified Framework for Human-centric Point Cloud Video Understanding
Authors: Yiteng Xu; Kecheng Ye; Xiao Han; Yiming Ren; Xinge Zhu; Yuexin Ma
Venue: CVPR 2024 (main)

Abstract: Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds further advancing downstream human-centric tasks and applications. Previous works usually focus on tackling one specific task and rely on huge labeled data which has poor generalization capability. Considering that human has specific characteristics including the structural semantics of human body and the dynamics of human motions we propose a unified framework to make full use of the prior knowledge and explore the inherent features in the data itself for generalized human-centric point cloud video understanding. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various human-related tasks including action recognition and 3D pose estimation. All datasets and code will be released soon.
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior
Authors: Wonseok Roh; Hwanhee Jung; Giljoo Nam; Jinseop Yeom; Hyunje Park; Sang Ho Yoon; Sangpil Kim
Venue: CVPR 2024 (main)

Abstract: While recent 3D instance segmentation approaches show promising results based on transformer architectures they often fail to correctly identify instances with similar appearances. They also ambiguously determine edges leading to multiple misclassifications of adjacent edge points. In this work we introduce a novel framework called EASE to overcome these challenges and improve the perception of complex 3D instances. We first propose a semantic guidance network to leverage rich semantic knowledge from a language model as intelligent priors enhancing the functional understanding of real-world instances beyond relying solely on geometrical information. We explicitly instruct the basic instance queries using text embeddings of each instance to learn deep semantic details. Further we utilize the edge prediction module encouraging the segmentation network to be edge-aware. We extract voxel-wise edge maps from point features and use them as auxiliary information for learning edge cues. In our extensive experiments on large-scale benchmarks ScanNetV2 ScanNet200 S3DIS and STPLS3D our EASE outperforms existing state-of-the-art models demonstrating its superior performance.
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference
Authors: Wei-Yu Chen; Aswin C. Sankaranarayanan; Anat Levin; Matthew O'Toole
Venue: CVPR 2024 (main)

Abstract: Passive depth estimation based on stereo or defocus relies on the presence of the texture on an object to resolve its depth. Hence recovering the depth of a textureless object-- for example a large white wall--is not just hard but perhaps even impossible. Or is it? We show that spatial coherence a property of natural light sources can be used to resolve the depth of a scene point even when it is textureless. Our approach relies on the idea that natural light scattered off a scene point is locally coherent with itself while incoherent with the light scattered from other surface points; we use this insight to design an optical setup that uses self-interference as a texture feature for estimating depth. Our lab prototype is capable of resolving the depths of textureless objects in sunlight as well as indoor lights.
Enhancing the Power of OOD Detection via Sample-Aware Model Selection
Authors: Feng Xue; Zi He; Yuan Zhang; Chuanlong Xie; Zhenguo Li; Falong Tan
Venue: CVPR 2024 (main)

Abstract: In this work we present a novel perspective on detecting out-of-distribution (OOD) samples and propose an algorithm for sample-aware model selection to enhance the effectiveness of OOD detection. Our algorithm determines for each test input which pre-trained models in the model zoo are capable of identifying the test input as an OOD sample. If no such models exist in the model zoo the test input is classified as an in-distribution (ID) sample. We theoretically demonstrate that our method maintains the true positive rate of ID samples and accurately identifies OOD samples with high probability when there are a sufficient number of diverse pre-trained models in the model zoo. Extensive experiments were conducted to validate our method demonstrating that it leverages the complementarity among single-model detectors to consistently improve the effectiveness of OOD sample identification. Compared to base-line methods our approach improved the relative performance by 65.40% and 37.25% on the CIFAR10 and ImageNet benchmarks respectively.
Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles
Authors: Rui Song; Chenwei Liang; Hu Cao; Zhiran Yan; Walter Zimmer; Markus Gross; Andreas Festag; Alois Knoll
Venue: CVPR 2024 (main)

Abstract: Collaborative perception in automated vehicles leverages the exchange of information between agents aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features and (ii) compressed orthogonal attention features shared between vehicles. Additionally due to the lack of a collaborative perception dataset designed for semantic occupancy prediction we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30% and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications showcasing enhanced accuracy and enriched semantic-awareness in road environments.
Generative Multi-modal Models are Good Class Incremental Learners
Authors: Xusheng Cao; Haori Lu; Linlan Huang; Xialei Liu; Ming-Ming Cheng
Venue: CVPR 2024 (main)

Abstract: In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models we would explore replacing discriminative models with generative ones for CIL. However transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated textual information into the classification of distinct categories. Additionally it requires formulating the task of CIL within a generative framework. To this end we propose a novel generative multi-modal model (GMM) framework for class incremental learning. Our approach directly generates labels for images using an adapted generative model. After obtaining the detailed text we use a text encoder to extract text features and employ feature matching to determine the most similar label as the classification prediction. In the conventional CIL settings we achieve significantly better results in long-sequence task scenarios. Under the Few-shot CIL setting we have improved by at least 14% over the current state-of-the-art methods with significantly less forgetting.
Low-Resource Vision Challenges for Foundation Models
Authors: Yunhua Zhang; Hazel Doughty; Cees G. M. Snoek
Venue: CVPR 2024 (main)

Abstract: Low-resource settings are well-established in natural lan- guage processing where many languages lack sufficient data for deep learning at scale. However low-resource problems are under-explored in computer vision. In this paper we address this gap and explore the challenges of low-resource image tasks with vision foundation models. We first collect a benchmark of genuinely low-resource image data covering historic maps circuit diagrams and mechanical drawings. These low-resource settings all share three challenges: data scarcity fine-grained differences and the distribution shift from natural images to the specialized domain of interest. While existing foundation models have shown impressive generalizability we find they cannot transfer well to our low-resource tasks. To begin to tackle the challenges of low-resource vision we introduce one simple baseline per challenge. Specifically we i) enlarge the data space by generative models ii) adopt the best sub-kernels to encode local regions for fine-grained difference discovery and iii) learn attention for specialized domains. Experiments on our three low-resource tasks demonstrate our proposals already provide a better baseline than transfer learning data aug- mentation and fine-grained methods. This highlights the unique characteristics and challenges of low-resource vision for foundation models that warrant further investigation. Project page: https://xiaobai1217.github.io/ Low-Resource-Vision/.
RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos
Authors: Hongchi Xia; Yang Fu; Sifei Liu; Xiaolong Wang
Venue: CVPR 2024 (main)

Abstract: We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video; (ii) multiple objects in one video; and (iii) an object with a static hand in one video. The dataset is annotated with object masks real-world scale camera poses and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis camera pose estimation object 6d pose estimation and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://wildrgbd.github.io/.
Shadow-Enlightened Image Outpainting
Authors: Hang Yu; Ruilin Li; Shaorong Xie; Jiayan Qiu
Venue: CVPR 2024 (main)

Abstract: Conventional image outpainting methods usually treat unobserved areas as unknown and extend the scene only in terms of semantic consistency thus overlooking the hidden information in shadows cast by unobserved areas such as the invisible shapes and semantics. In this paper we propose to extract and utilize the hidden information of unobserved areas from their shadows to enhance image outpainting. To this end we propose an end-to-end deep approach that explicitly looks into the shadows within the image. Specifically we extract shadows from the input image and identify instance-level shadow regions cast by the unobserved areas. Then the instance-level shadow representations are concatenated to predict the scene layout of each unobserved instance and outpaint the unobserved areas. Finally two discriminators are implemented to enhance alignment between the extended semantics and their shadows. In the experiments we show that our proposed approach provides complementary cues for outpainting and achieves considerable improvement on all datasets by adopting our approach as a plug-in module.
Towards Generalizable Tumor Synthesis
Authors: Qi Chen; Xiaoxi Chen; Haorui Song; Zhiwei Xiong; Alan Yuille; Chen Wei; Zongwei Zhou
Venue: CVPR 2024 (main)

Abstract: Tumor synthesis enables the creation of artificial tumors in medical images facilitating the training of AI models for tumor detection and segmentation. However success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and furthermore the resulting AI models being capable of detecting real tumors in images sourced from different domains (e.g. hospitals). This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation: early-stage tumors (< 2cm) tend to have similar imaging characteristics in computed tomography (CT) whether they originate in the liver pancreas or kidneys. We have ascertained that generative AI models e.g. Diffusion Models can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ. Moreover we have shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes encompassing a broad spectrum of patient demographics imaging protocols and healthcare facilities.
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
Authors: Haoyu Chen; Wenbo Li; Jinjin Gu; Jingjing Ren; Haoze Sun; Xueyi Zou; Zhensong Zhang; Youliang Yan; Lei Zhu
Venue: CVPR 2024 (main)

Abstract: For image super-resolution (SR) bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel "Low-Res Leads the Way" (LWay) training framework merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images merging them with super-resolved outputs for LR reconstruction. Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images. The integration of Discrete Wavelet Transform (DWT) further refines the focus on high-frequency details. Extensive evaluations show that our method significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets outperforming existing methods. Our training regime is universally compatible requiring no network architecture modifications making it a practical solution for real-world SR applications.
BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics
Authors: Wenqian Zhang; Molin Huang; Yuxuan Zhou; Juze Zhang; Jingyi Yu; Jingya Wang; Lan Xu
Venue: CVPR 2024 (main)

Abstract: The recently emerging text-to-motion advances have spired numerous attempts for convenient and interactive human motion generation. Yet existing methods are largely limited to generating body motions only without considering the rich two-hand motions let alone handling various conditions like body dynamics or texts. To break the data bottleneck we propose BOTH57M a novel multi-modal dataset for two-hand motion generation. Our dataset includes accurate motion tracking for the human body and hands and provides pair-wised finger-level hand annotations and body descriptions. We further provide a strong baseline method BOTH2Hands for the novel task: generating vivid two-hand motions from both implicit body dynamics and explicit text prompts. We first warm up two parallel body-to-hand and text-to-hand diffusion models and then utilize the cross-attention transformer for motion blending. Extensive experiments and cross-validations demonstrate the effectiveness of our approach and dataset for generating convincing two-hand motions from the hybrid body-and-textual conditions. Our dataset and code will be disseminated to the community for future research which can be found at https://github.com/Godheritage/BOTH2Hands.