pwc-archive/papers-with-abstracts huggingface dataset:
Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer Examples
Authors: Yuyang Yu, Bangzhen Liu, Chenxi Zheng, Xuemiao Xu, Huaidong Zhang, Shengfeng He
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Few-Shot Learning

    In this paper we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end we implement two optimization strategies. The first prompt-free conditional learning utilizes a prompt-free encoder derived from a pre-trained Stable Diffusion model. This strategy is designed to adapt new conditions to the diffusion process by minimizing the textual-visual correlation thereby ensuring a more precise alignment between the generated content and the specified conditions. The second strategy entails condition-specific negative rectification which addresses the inconsistencies typically brought about by Classifier-free guidance in few-shot training contexts. Our extensive experiments across a variety of condition modalities demonstrate the effectiveness and efficiency of our framework yielding results comparable to those obtained with datasets a thousand times larger. Our codes are available at https://github.com/Yuyan9Yu/BeyondTextConstraint.
Bi-SSC: Geometric-Semantic Bidirectional Fusion for Camera-based 3D Semantic Scene Completion
Authors: Yujie Xue, Ruihui Li, Fan Wu, Zhuo Tang, Kenli Li, Mingxing Duan
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Semantic Scene Completion

    Camera-based Semantic Scene Completion (SSC) is to infer the full geometry of objects and scenes from only 2D images. The task is particularly challenging for those invisible areas due to the inherent occlusions and lighting ambiguity. Existing works ignore the information missing or ambiguous in those shaded and occluded areas resulting in distorted geometric prediction. To address this issue we propose a novel method Bi-SSC bidirectional geometric semantic fusion for camera-based 3D semantic scene completion. The key insight is to use the neighboring structure of objects in the image and the spatial differences from different perspectives to compensate for the lack of information in occluded areas. Specifically we introduce a spatial sensory fusion module with multiple association attention to improve semantic correlation in geometric distributions. This module works within single view and across stereo views to achieve global spatial consistency. Experimental results demonstrate that Bi-SSC outperforms state-of-the-art camera-based methods on SemanticKITTI particularly excelling in those invisible and shaded areas.
LoS: Local Structure-Guided Stereo Matching
Authors: Kunhong Li, Longguang Wang, Ye Zhang, Kaiwen Xue, Shunbo Zhou, Yulan Guo
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Stereo Matching

    Estimating disparities in challenging areas is difficult and limits the performance of stereo matching models. In this paper we exploit local structure information (LSI) to enhance stereo matching. Specifically our LSI comprises a series of key elements including the slant plane (parameterised by disparity gradients) disparity offset details and neighbouring relations. This LSI empowers our method to effectively handle intricate structures including object boundaries and curved surfaces. We bootstrap the LSI from monocular depth and subsequently iteratively refine it to better capture the underlying scene geometry constraints. Building upon the LSI we introduce the Local Structure-Guided Propagation (LSGP) which enhances the disparity initialization optimization and refinement processes. By combining LSGP with a Gated Recurrent Unit (GRU) we present our novel stereo matching method referred to as Local Structure-guided stereo matching (LoS). Remarkably LoS achieves top-ranking results on four widely recognized public benchmark datasets (ETH3D Middlebury KITTI 15 & 12) demonstrating the superior capabilities of our proposed model.
LiSA: LiDAR Localization with Semantic Awareness
Authors: Bochun Yang, Zijun Li, Wen Li, Zhipeng Cai, Chenglu Wen, Yu Zang, Matthias Muller, Cheng Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Knowledge Distillation, Semantic Segmentation, Visual Localization

    LiDAR localization is a fundamental task in robotics and computer vision which estimates the pose of a LiDAR point cloud within a global map. Scene Coordinate Regression (SCR) has demonstrated state-of-the-art performance in this task. In SCR a scene is represented as a neural network which outputs the world coordinates for each point in the input point cloud. However SCR treats all points equally during localization ignoring the fact that not all objects are beneficial for localization. For example dynamic objects and repeating structures often negatively impact SCR. To address this problem we introduce LiSA the first method that incorporates semantic awareness into SCR to boost the localization robustness and accuracy. To avoid extra computation or network parameters during inference we distill the knowledge from a segmentation model to the original SCR network. Experiments show the superior performance of LiSA on standard LiDAR localization benchmarks compared to state-of-the-art methods. Applying knowledge distillation not only preserves high efficiency but also achieves higher localization accuracy than introducing extra semantic segmentation modules. We also analyze the benefit of semantic information for LiDAR localization. Our code is released at https://github.com/Ybchun/LiSA.
Learned Trajectory Embedding for Subspace Clustering
Authors: Yaroslava Lochman, Carl Olsson, Christopher Zach
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Clustering, Motion Segmentation, parameter estimation

    Clustering multiple motions from observed point trajectories is a fundamental task in understanding dynamic scenes. Most motion models require multiple tracks to estimate their parameters hence identifying clusters when multiple motions are observed is a very challenging task. This is even aggravated for high-dimensional motion models. The starting point of our work is that this high-dimensionality of motion model can actually be leveraged to our advantage as sufficiently long trajectories identify the underlying motion uniquely in practice. Consequently we propose to learn a mapping from trajectories to embedding vectors that represent the generating motion. The obtained trajectory embeddings are useful for clustering multiple observed motions but are also trained to contain sufficient information to recover the parameters of the underlying motion by utilizing a geometric loss. We therefore are able to use only weak supervision from given motion segmentation to train this mapping. The entire algorithm consisting of trajectory embedding clustering and motion parameter estimation is highly efficient. We conduct experiments on the Hopkins155 Hopkins12 and KT3DMoSeg datasets and show state-of-the-art performance of our proposed method for trajectory-based motion segmentation on full sequences and its competitiveness on the occluded sequences. Project page: https://ylochman.github.io/trajectory-embedding.
NightCC: Nighttime Color Constancy via Adaptive Channel Masking
Authors: Shuwei Li, Robby T. Tan
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Color Constancy, Diversity, Domain Adaptation, Unsupervised Domain Adaptation

    Nighttime conditions pose a significant challenge to color constancy due to the diversity of lighting conditions and the presence of substantial low-light noise. Existing color constancy methods struggle with nighttime scenes frequently leading to imprecise light color estimations. To tackle nighttime color constancy we propose a novel unsupervised domain adaptation approach that utilizes labeled daytime data to facilitate learning on unlabeled nighttime images. To specifically address the unique lighting conditions of nighttime and ensure the robustness of pseudo labels we propose adaptive channel masking and light uncertainty. By selectively masking channels that are less sensitive to lighting conditions adaptive channel masking directs the model to progressively focus on features less affected by variations in light colors and noise. Additionally our model leverages light uncertainty to provide a pixel-wise uncertainty estimation regarding light color prediction which helps avoid learning from incorrect labels. Our model demonstrates a significant improvement in accuracy achieving 21.5% lower Mean Angular Error (MAE) compared to the state-of-the-art method on our nighttime dataset.
VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding
Authors: Syed Talal Wasim, Muzammal Naseer, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Spatio-Temporal Video Grounding, Video Grounding, Video Understanding

    Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and predefined vocabularies our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions our model surpasses the recent best-performing models by 4.88 m_vIoU and 1.83 accuracy demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.
FedHCA2: Towards Hetero-Client Federated Multi-Task Learning
Authors: Yuxiang Lu, Suizhi Huang, Yuwen Yang, Shalayiding Sirejiding, Yue Ding, Hongtao Lu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Decoder, Federated Learning, Multi-Task Learning

    Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks assuming model congruity that identical model architecture is deployed in each client. To relax this assumption and thus extend real-world applicability we introduce a novel problem setting Hetero-Client Federated Multi-Task Learning (HC-FMTL) to accommodate diverse task setups. The main challenge of HC-FMTL is the model incongruity issue that invalidates conventional aggregation methods. It also escalates the difficulties in model aggregation to deal with data and task heterogeneity inherent in FMTL. To address these challenges we propose the FedHCA^2 framework which allows for federated training of personalized models by modeling relationships among heterogeneous clients. Drawing on our theoretical insights into the difference between multi-task and federated optimization we propose the Hyper Conflict-Averse Aggregation scheme to mitigate conflicts during encoder updates. Additionally inspired by task interaction in MTL the Hyper Cross Attention Aggregation scheme uses layer-wise cross attention to enhance decoder interactions while alleviating model incongruity. Moreover we employ learnable Hyper Aggregation Weights for each client to customize personalized parameter updates. Extensive experiments demonstrate the superior performance of FedHCA^2 in various HC-FMTL scenarios compared to representative methods. Code is available at https://github.com/innovator-zero/FedHCA2.
VMINer: Versatile Multi-view Inverse Rendering with Near- and Far-field Light Sources
Authors: Fan Fei, Jiajun Tang, Ping Tan, Boxin Shi
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Inverse Rendering

    This paper introduces a versatile multi-view inverse rendering framework with near- and far-field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse rendering our framework adopts a lightweight yet inclusive lighting model for different near- and far-field lights thus is able to make use of input images under varied lighting conditions available during capture. It leverages observations under each lighting to disentangle the intrinsic geometry and material from the external lighting using both neural radiance field rendering and physically-based surface rendering on the 3D implicit fields. After training the reconstructed scene is extracted to a textured triangle mesh for seamless integration into industrial rendering software for various applications. Quantitatively and qualitatively tested on synthetic and real-world scenes our method shows superiority to state-of-the-art multi-view inverse rendering methods in both speed and quality.
FISBe: A Real-World Benchmark Dataset for Instance Segmentation of Long-Range Thin Filamentous Structures
Authors: Lisa Mais, Peter Hirsch, Claire Managan, Ramya Kandarpa, Josef Lorenz Rumberger, Annika Reinke, Lena Maier-Hein, Gudrun Ihrke, Dagmar Kainmueller
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Benchmarking, Instance Segmentation, scientific discovery, Segmentation, Semantic Segmentation

    Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging thin filamentous and widely branching morphologies multiple neurons are tightly inter-weaved and partial volume effects uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing to date methods are typically benchmarked on synthetic datasets. To address this gap we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies and facilitate scientific discovery in basic neuroscience.
Batch Normalization Alleviates the Spectral Bias in Coordinate Networks
Authors: Zhicheng Cai, Hao Zhu, Qiu Shen, Xinran Wang, Xun Cao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Compression, Novel View Synthesis

    Representing signals using coordinate networks dominates the area of inverse problems recently and is widely applied in various scientific computing tasks. Still there exists an issue of spectral bias in coordinate networks limiting the capacity to learn high-frequency components. This problem is caused by the pathological distribution of the neural tangent kernel's (NTK's) eigenvalues of coordinate networks. We find that this pathological distribution could be improved using the classical batch normalization (BN) which is a common deep learning technique but rarely used in coordinate networks. BN greatly reduces the maximum and variance of NTK's eigenvalues while slightly modifies the mean value considering the max eigenvalue is much larger than the most this variance change results in a shift of eigenvalues' distribution from a lower one to a higher one therefore the spectral bias could be alleviated (see Fig. 1). This observation is substantiated by the significant improvements of applying BN-based coordinate networks to various tasks including the image compression computed tomography reconstruction shape representation magnetic resonance imaging and novel view synthesis.
MMA: Multi-Modal Adapter for Vision-Language Models
Authors: Lingxiao Yang, Ru-Yuan Zhang, Yanchen Wang, Xiaohua Xie
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Domain Generalization, General Knowledge, Transfer Learning

    Pre-trained Vision-Language Models (VLMs) have served as excellent foundation models for transfer learning in diverse downstream tasks. However tuning VLMs for few-shot generalization tasks faces a discrimination -- generalization dilemma i.e. general knowledge should be preserved and task-specific knowledge should be fine-tuned. How to precisely identify these two types of representations remains a challenge. In this paper we propose a Multi-Modal Adapter (MMA) for VLMs to improve the alignment between representations from text and vision branches. MMA aggregates features from different branches into a shared feature space so that gradients can be communicated across branches. To determine how to incorporate MMA we systematically analyze the discriminability and generalizability of features across diverse datasets in both the vision and language branches and find that (1) higher layers contain discriminable dataset-specific knowledge while lower layers contain more generalizable knowledge and (2) language features are more discriminable than visual features and there are large semantic gaps between the features of the two modalities especially in the lower layers. Therefore we only incorporate MMA to a few higher layers of transformers to achieve an optimal balance between discrimination and generalization. We evaluate the effectiveness of our approach on three tasks: generalization to novel classes novel target datasets and domain generalization. Compared to many state-of-the-art methods our MMA achieves leading performance in all evaluations. Code is at https://github.com/ZjjConan/Multi-Modal-Adapter
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning
Authors: Menghao Zhang, Jingyu Wang, Qi Qi, Haifeng Sun, Zirui Zhuang, Pengfei Ren, Ruilong Ma, Jianxin Liao
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Anomaly Detection, Contrastive Learning, Representation Learning, Video Anomaly Detection

    ecent progress in video anomaly detection suggests that the features of appearance and motion play crucial roles in distinguishing abnormal patterns from normal ones. However we note that the effect of spatial scales of anomalies is ignored. The fact that many abnormal events occur in limited localized regions and severe background noise interferes with the learning of anomalous changes. Meanwhile most existing methods are limited by coarse-grained modeling approaches which are inadequate for learning highly discriminative features to discriminate subtle differences between small-scale anomalies and normal patterns. To this end this paper address multi-scale video anomaly detection by multi-grained spatio-temporal representation learning. We utilize video continuity to design three proxy tasks to perform feature learning at both coarse-grained and fine-grained levels i.e. continuity judgment discontinuity localization and missing frame estimation. In particular we formulate missing frame estimation as a contrastive learning task in feature space instead of a reconstruction task in RGB space to learn highly discriminative features. Experiments show that our proposed method outperforms state-of-the-art methods on four datasets especially in scenes with small-scale anomalies.
Logarithmic Lenses: Exploring Log RGB Data for Image Classification
Authors: Bruce A. Maxwell, Sumegha Singhania, Avnish Patel, Rahul Kumar, Heather Fryling, Sihan Li, Haonan Sun, Ping He, Zewen Li
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Data Augmentation, image-classification, Image Classification

    The design of deep network architectures and training methods in computer vision has been well-explored. However in almost all cases the images have been used as provided with little exploration of pre-processing steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors; differences and sums correspond to ratios and products. Features in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.
TurboSL: Dense Accurate and Fast 3D by Neural Inverse Structured Light
Authors: Parsa Mirdehghan, Maxx Wu, Wenzheng Chen, David B. Lindell, Kiriakos N. Kutulakos
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Inverse Rendering

    We show how to turn a noisy and fragile active triangulation technique--three-pattern structured light with a grayscale camera--into a fast and powerful tool for 3D capture: able to output sub-pixel accurate disparities at megapixel resolution along with reflectance normals and a no-reference estimate of its own pixelwise 3D error. To achieve this we formulate structured-light decoding as a neural inverse rendering problem. We show that despite having just three or four input images--all from the same viewpoint--this problem can be tractably solved by TurboSL an algorithm that combines (1) a precise image formation model (2) a signed distance field scene representation and (3) projection pattern sequences optimized for accuracy instead of precision. We use TurboSL to reconstruct a variety of complex scenes from images captured at up to 60 fps with a camera and a common projector. Our experiments highlight TurboSL's potential for dense and highly-accurate 3D acquisition from data captured in fractions of a second.