GenAI4ELab/papercli-papers huggingface dataset:
Realigning Confidence with Temporal Saliency Information for Point-Level Weakly-Supervised Temporal Action Localization
Authors: Ziying Xia; Jian Cheng; Siyu Liu; Yongxiang Hu; Shiguang Wang; Yijie Zhang; Liwan Dang
Venue: CVPR 2024 (main)

Abstract: Point-level weakly-supervised temporal action localization (P-TAL) aims to localize action instances in untrimmed videos through the use of single-point annotations in each instance. Existing methods predict the class activation sequences without any boundary information and the unreliable sequences result in a significant misalignment between the quality of proposals and their corresponding confidence. In this paper we surprisingly observe the most salient frame tend to appear in the central region of the each instance and is easily annotated by humans. Guided by the temporal saliency information we present a novel proposal-level plug-in framework to relearn the aligned confidence of proposals generated by the base locators. The proposed approach consists of Center Score Learning (CSL) and Alignment-based Boundary Adaptation (ABA). In CSL we design a novel center label generated by the point annotations for predicting aligned center scores. During inference we first fuse the center scores with the predicted action probabilities to obtain the aligned confidence. ABA utilizes the both aligned confidence and IoU information to enhance localization completeness. Extensive experiments demonstrate the generalization and effectiveness of the proposed framework showcasing state-of-the-art or competitive performances across three benchmarks. Our code is available at https://github.com/zyxia1009/CVPR2024-TSPNet.
ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion
Authors: Jiayu Yang; Ziang Cheng; Yunfei Duan; Pan Ji; Hongdong Li
Venue: CVPR 2024 (main)

Abstract: Given a single image of a 3D object this paper proposes a novel method (named ConsistNet) that can generate multiple images of the same object as if they are captured from different viewpoints while the 3D (multi-view) consistencies among those multiple generated images are effectively exploited. Central to our method is a lightweight multi-view consistency block that enables information exchange across multiple single-view diffusion processes based on the underlying multi-view geometry principles. ConsistNet is an extension to the standard latent diffusion model and it consists of two submodules: (a) a view aggregation module that unprojects multi-view features into global 3D volumes and infers consistency and (b) a ray aggregation module that samples and aggregates 3D consistent features back to each view to enforce consistency. Our approach departs from previous methods in multi-view image generation in that it can be easily dropped in pre-trained LDMs without requiring explicit pixel correspondences or depth prediction. Experiments show that our method effectively learns 3D consistency over a frozen Zero123-XL backbone and can generate 16 surrounding views of the object within 11 seconds on a single A100 GPU.
GenN2N: Generative NeRF2NeRF Translation
Authors: Xiangyue Liu; Han Xue; Kunming Luo; Ping Tan; Li Yi
Venue: CVPR 2024 (main)

Abstract: We present GenN2N a unified NeRF-to-NeRF translation framework for various NeRF translation tasks such as text-driven NeRF editing colorization super-resolution inpainting etc. Unlike previous methods designed for individual translation tasks with task-specific schemes GenN2N achieves all these NeRF editing tasks by employing a plug-and-play image-to-image translator to perform editing in the 2D domain and lifting 2D edits into the 3D NeRF space. Since the 3D consistency of 2D edits may not be assured we propose to model the distribution of the underlying 3D edits through a generative model that can cover all possible edited NeRFs. To model the distribution of 3D edited NeRFs from 2D edited images we carefully design a VAE-GAN that encodes images while decoding NeRFs. The latent space is trained to align with a Gaussian distribution and the NeRFs are supervised through an adversarial loss on its renderings. To ensure the latent code does not depend on 2D viewpoints but truly reflects the 3D edits we also regularize the latent code through a contrastive learning scheme. Extensive experiments on various editing tasks show GenN2N as a universal framework performs as well or better than task-specific specialists while possessing flexible generative power. More results on our project page: https://xiangyueliu.github.io/GenN2N/.
Theoretically Achieving Continuous Representation of Oriented Bounding Boxes
Authors: Zikai Xiao; Guoye Yang; Xue Yang; Taijiang Mu; Junchi Yan; Shimin Hu
Venue: CVPR 2024 (main)

Abstract: Considerable efforts have been devoted to Oriented Object Detection (OOD). However one lasting issue regarding the discontinuity in Oriented Bounding Box (OBB) representation remains unresolved which is an inherent bottleneck for extant OOD methods. This paper endeavors to completely solve this issue in a theoretically guaranteed manner and puts an end to the ad-hoc efforts in this direction. Prior studies typically can only address one of the two cases of discontinuity: rotation and aspect ratio and often inadvertently introduce decoding discontinuity e.g. Decoding Incompleteness (DI) and Decoding Ambiguity (DA) as discussed in literature. Specifically we propose a novel representation method called Continuous OBB (COBB) which can be readily integrated into existing detectors e.g. Faster-RCNN as a plugin. It can theoretically ensure continuity in bounding box regression which to our best knowledge has not been achieved in literature for rectangle-based object representation. For fairness and transparency of experiments we have developed a modularized benchmark based on the open-source deep learning framework Jittor's detection toolbox JDet for OOD evaluation. On the popular DOTA dataset by integrating Faster-RCNN as the same baseline model our new method outperforms the peer method Gliding Vertex by 1.13% mAP50 (relative improvement 1.54%) and 2.46% mAP75 (relative improvement 5.91%) without any tricks.
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution
Authors: Zakariya Chaouai; Mohamed Tamaazousti
Venue: CVPR 2024 (main)

Abstract: Most of the recent literature on image Super-Resolution (SR) can be classified into two main approaches. The first one involves learning a corruption model tailored to a specific dataset aiming to mimic the noise and corruption in low-resolution images such as sensor noise. However this approach is data-specific tends to lack adaptability and its accuracy diminishes when faced with unseen types of image corruptions. A second and more recent approach referred to as Robust Super-Resolution (RSR) proposes to improve real-world SR by harnessing the generalization capabilities of a model by making it robust to adversarial attacks. To delve further into this second approach our paper explores the universality of various methods for enhancing the robustness of deep learning SR models. In other words we inquire: \enquote Which robustness method exhibits the highest degree of adaptability when dealing with a wide range of adversarial attacks ? . Our extensive experimentation on both synthetic and real-world images empirically demonstrates that median randomized smoothing (MRS) is more general in terms of robustness compared to adversarial learning techniques which tend to focus on specific types of attacks. Furthermore as expected we also illustrate that the proposed universal robust method enables the SR model to handle standard corruptions more effectively such as blur and Gaussian noise and notably corruptions naturally present in real-world images. These results support the significance of shifting the paradigm in the development of real-world SR methods towards RSR especially via MRS.
One-dimensional Adapter to Rule Them All: Concepts Diffusion Models and Erasing Applications
Authors: Mengyao Lyu; Yuhong Yang; Haiwen Hong; Hui Chen; Xuan Jin; Yuan He; Hui Xue; Jungong Han; Guiguang Ding
Venue: CVPR 2024 (main)

Abstract: The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing methods in academia are all based on full parameter or specification-based fine-tuning from which we observe the following issues: 1) Generation alteration towards erosion: Parameter drift during target elimination causes alterations and potential deformations across all generations even eroding other concepts at varying degrees which is more evident with multi-concept erased; 2) Transfer inability & deployment inefficiency: Previous model-specific erasure impedes the flexible combination of concepts and the training-free transfer towards other models resulting in linear cost growth as the deployment scenarios increase. To achieve non-invasive precise customizable and transferable elimination we ground our erasing framework on one-dimensional adapters to erase multiple concepts from most DMs at once across versatile erasing applications. The concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to learn targeted erasing and meantime the alteration and erosion phenomenon is effectively mitigated via a novel Latent Anchoring fine-tuning strategy. Once obtained SPMs can be flexibly combined and plug-and-play for other DMs without specific re-tuning enabling timely and efficient adaptation to diverse scenarios. During generation our Facilitated Transport mechanism dynamically regulates the permeability of each SPM to respond to different input prompts further minimizing the impact on other concepts. Quantitative and qualitative results across  40 concepts 7 DMs and 4 erasing applications have demonstrated the superior erasing of SPM. Our code and pre-tuned SPMs are available on the project page https://lyumengyao.github.io/projects/spm.
Learning Large-Factor EM Image Super-Resolution with Generative Priors
Authors: Jiateng Shou; Zeyu Xiao; Shiyu Deng; Wei Huang; Peiyao Shi; Ruobing Zhang; Zhiwei Xiong; Feng Wu
Venue: CVPR 2024 (main)

Abstract: As the mainstream technique for capturing images of biological specimens at nanometer resolution electron microscopy (EM) is extremely time-consuming for scanning wide field-of-view (FOV) specimens. In this paper we investigate a challenging task of large-factor EM image super-resolution (EMSR) which holds great promise for reducing scanning time relaxing acquisition conditions and expanding imaging FOV. By exploiting the repetitive structures and volumetric coherence of EM images we propose the first generative learning-based framework for large-factor EMSR. Specifically motivated by the predictability of repetitive structures and textures in EM images we first learn a discrete codebook in the latent space to represent high-resolution (HR) cell-specific priors and a latent vector indexer to map low-resolution (LR) EM images to their corresponding latent vectors in a generative manner. By incorporating the generative cell-specific priors from HR EM images through a multi-scale prior fusion module we then deploy multi-image feature alignment and fusion to further exploit the inter-section coherence in the volumetric EM data. Extensive experiments demonstrate that our proposed framework outperforms advanced single-image and video super-resolution methods for 8x and 16x EMSR (i.e. with 64 times and 256 times less data acquired respectively) achieving superior visual reconstruction quality and downstream segmentation accuracy on benchmark EM datasets. Code is available at https://github.com/jtshou/GPEMSR.
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
Authors: Nastaran Saadati; Minh Pham; Nasla Saleem; Joshua R. Waite; Aditya Balu; Zhanong Jiang; Chinmay Hegde; Soumik Sarkar
Venue: CVPR 2024 (main)

Abstract: Recent advances in decentralized deep learning algorithms have demonstrated cutting-edge performance on various tasks with large pre-trained models. However a pivotal prerequisite for achieving this level of competitiveness is the significant communication and computation overheads when updating these models which prohibits the applications of them to real-world scenarios. To address this issue drawing inspiration from advanced model merging techniques without requiring additional training we introduce the Decentralized Iterative Merging-And-Training (DIMAT) paradigm--a novel decentralized deep learning framework. Within DIMAT each agent is trained on their local data and periodically merged with their neighboring agents using advanced model merging techniques like activation matching until convergence is achieved. DIMAT provably converges with the best available rate for nonconvex functions with various first-order methods while yielding tighter error bounds compared to the popular existing approaches. We conduct a comprehensive empirical analysis to validate DIMAT's superiority over baselines across diverse computer vision tasks sourced from multiple datasets. Empirical results validate our theoretical claims by showing that DIMAT attains faster and higher initial gain in accuracy with independent and identically distributed (IID) and non-IID data incurring lower communication overhead. This DIMAT paradigm presents a new opportunity for the future decentralized learning enhancing its adaptability to real-world with sparse and light-weight communication and computation.
MMA: Multi-Modal Adapter for Vision-Language Models
Authors: Lingxiao Yang; Ru-Yuan Zhang; Yanchen Wang; Xiaohua Xie
Venue: CVPR 2024 (main)

Abstract: 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
Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
Authors: Joren Brunekreef; Eric Marcus; Ray Sheombarsing; Jan-Jakob Sonke; Jonas Teuwen
Venue: CVPR 2024 (main)

Abstract: Image segmentation algorithms can be understood as a collection of pixel classifiers for which the outcomes of nearby pixels are correlated. Classifier models can be calibrated using Inductive Conformal Prediction but this requires holding back a sufficiently large calibration dataset for computing the distribution of non-conformity scores of the model's predictions. If one only requires only marginal calibration on the image level this calibration set consists of all individual pixels in the images available for calibration. However if the goal is to attain proper calibration for each individual pixel classifier the calibration set consists of individual images. In a scenario where data are scarce (such as the medical domain) it may not always be possible to set aside sufficiently many images for this pixel-level calibration.The method we propose dubbed "Kandinsky calibration" makes use of the spatial structure present in the distribution of natural images to simultaneously calibrate the classifiers of "similar" pixels. This can be seen as an intermediate approach between marginal (imagewise) and conditional (pixelwise) calibration where non-conformity scores are aggregated over similar image regions thereby making more efficient use of the images available for calibration. We run experiments on segmentation algorithms trained and calibrated on subsets of the public MS-COCO and Medical Decathlon datasets demonstrating that Kandinsky calibration method can significantly improve the coverage. When compared to both pixelwise and imagewise calibration on little data the Kandinsky method achieves much lower coverage errors indicating the data efficiency of the Kandinsky calibration.
Diversity-aware Channel Pruning for StyleGAN Compression
Authors: Jiwoo Chung; Sangeek Hyun; Sang-Heon Shim; Jae-Pil Heo
Venue: CVPR 2024 (main)

Abstract: StyleGAN has shown remarkable performance in unconditional image generation. However its high computational cost poses a significant challenge for practical applications. Although recent efforts have been made to compress StyleGAN while preserving its performance existing compressed models still lag behind the original model particularly in terms of sample diversity. To overcome this we propose a novel channel pruning method that leverages varying sensitivities of channels to latent vectors which is a key factor in sample diversity. Specifically by assessing channel importance based on their sensitivities to latent vector perturbations our method enhances the diversity of samples in the compressed model. Since our method solely focuses on the channel pruning stage it has complementary benefits with prior training schemes without additional training cost. Extensive experiments demonstrate that our method significantly enhances sample diversity across various datasets. Moreover in terms of FID scores our method not only surpasses state-of-the-art by a large margin but also achieves comparable scores with only half training iterations.
BioCLIP: A Vision Foundation Model for the Tree of Life
Authors: Samuel Stevens; Jiaman Wu; Matthew J Thompson; Elizabeth G Campolongo; Chan Hee Song; David Edward Carlyn; Li Dong; Wasila M Dahdul; Charles Stewart; Tanya Berger-Wolf; Wei-Lun Chao; Yu Su
Venue: CVPR 2024 (main)

Abstract: Images of the natural world collected by a variety of cameras from drones to individual phones are increasingly abundant sources of biological information. There is an explosion of computational methods and tools particularly computer vision for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions contexts and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this we curate and release TreeOfLife-10M the largest and most diverse ML-ready dataset of biology images. We then develop BioCLIP a foundation model for the tree of life leveraging the unique properties of biology captured by TreeOfLife-10M namely the abundance and variety of images of plants animals and fungi together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks and find that BioCLIP consistently and substantially outperforms existing baselines (by 17% to 20% absolute). Intrinsic evaluation reveals that BioCLIP has learned a hierarchical representation conforming to the tree of life shedding light on its strong generalizability. All data code and models will be publicly released upon acceptance.
From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models
Authors: Rongjie Li; Songyang Zhang; Dahua Lin; Kai Chen; Xuming He
Venue: CVPR 2024 (main)

Abstract: Scene graph generation (SGG) aims to parse a visual scene into an intermediate graph representation for downstream reasoning tasks. Despite recent advancements existing methods struggle to generate scene graphs with novel visual relation concepts. To address this challenge we introduce a new open-vocabulary SGG framework based on sequence generation. Our framework leverages vision-language pre-trained models (VLM) by incorporating an image-to-graph generation paradigm. Specifically we generate scene graph sequences via image-to-text generation with VLM and then construct scene graphs from these sequences. By doing so we harness the strong capabilities of VLM for open-vocabulary SGG and seamlessly integrate explicit relational modeling for enhancing the VL tasks. Experimental results demonstrate that our design not only achieves superior performance with an open vocabulary but also enhances downstream vision-language task performance through explicit relation modeling knowledge.
Deep Imbalanced Regression via Hierarchical Classification Adjustment
Authors: Haipeng Xiong; Angela Yao
Venue: CVPR 2024 (main)

Abstract: Regression tasks in computer vision such as age estimation or counting are often formulated into classification by quantizing the target space into classes. Yet real-world data is often imbalanced -- the majority of training samples lie in a head range of target values while a minority of samples span a usually larger tail range. By selecting the class quantization one can adjust imbalanced regression targets into balanced classification outputs though there are trade-offs in balancing classification accuracy and quantization error. To improve regression performance over the entire range of data we propose to construct hierarchical classifiers for solving imbalanced regression tasks. The fine-grained classifiers limit the quantization error while being modulated by the coarse predictions to ensure high accuracy. Standard hierarchical classification approaches when applied to the regression problem fail to ensure that predicted ranges remain consistent across the hierarchy. As such we propose a range-preserving distillation process that effectively learns a single classifier from the set of hierarchical classifiers. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks: age estimation crowd counting and depth estimation. Code is available at https://github.com/xhp-hust-2018-2011/HCA.