pwc-archive/papers-with-abstracts huggingface dataset:
Real-World Mobile Image Denoising Dataset with Efficient Baselines
Authors: Roman Flepp, Andrey Ignatov, Radu Timofte, Luc van Gool
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Denoising, Image Denoising

    The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware the mobile camera sensor area cannot be increased significantly due to physical constraints leading to a pixel size of 0.6--2.0 \mum which results in strong image noise even in moderate lighting conditions. In the era of deep learning one can train a CNN model to perform robust image denoising. However there is still a lack of a substantially diverse dataset for this task. To address this problem we introduce a novel Mobile Image Denoising Dataset (MIDD) comprising over 400000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors showing its high generalizability. The datasets code and models can be found on the official project website.
Data Poisoning based Backdoor Attacks to Contrastive Learning
Authors: Jinghuai Zhang, Hongbin Liu, Jinyuan Jia, Neil Zhenqiang Gong
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Data Poisoning

    Contrastive learning (CL) pre-trains general-purpose encoders using an unlabeled pre-training dataset which consists of images or image-text pairs. CL is vulnerable to data poisoning based backdoor attacks (DPBAs) in which an attacker injects poisoned inputs into the pre-training dataset so the encoder is backdoored. However existing DPBAs achieve limited effectiveness. In this work we take the first step to analyze the limitations of existing backdoor attacks and propose new DPBAs called CorruptEncoder to CL. CorruptEncoder introduces a new attack strategy to create poisoned inputs and uses a theory-guided method to maximize attack effectiveness. Our experiments show that CorruptEncoder substantially outperforms existing DPBAs. In particular CorruptEncoder is the first DPBA that achieves more than 90% attack success rates with only a few (3) reference images and a small poisoning ratio (0.5%). Moreover we also propose a defense called localized cropping to defend against DPBAs. Our results show that our defense can reduce the effectiveness of DPBAs but it sacrifices the utility of the encoder highlighting the need for new defenses.
Time- Memory- and Parameter-Efficient Visual Adaptation
Authors: Otniel-Bogdan Mercea, Alexey Gritsenko, Cordelia Schmid, Anurag Arnab
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: GPU, Video Classification

    As foundation models become more popular there is a growing need to efficiently finetune them for downstream tasks. Although numerous adaptation methods have been proposed they are designed to be efficient only in terms of how many parameters are trained. They however typically still require backpropagating gradients throughout the model meaning that their training-time and -memory cost does not reduce as significantly. We propose an adaptation method which does not backpropagate gradients through the backbone. We achieve this by designing a lightweight network in parallel that operates on features from the frozen pretrained backbone. As a result our method is efficient not only in terms of parameters but also in training-time and memory usage. Our approach achieves state-of-the-art accuracy-parameter trade-offs on the popular VTAB benchmark and we further show how we outperform prior works with respect to training-time and -memory usage too. We further demonstrate the training efficiency and scalability of our method by adapting a vision transformer backbone of 4 billion parameters for the computationally demanding task of video classification without any intricate model parallelism. Here we outperform a prior adaptor-based method which could only scale to a 1 billion parameter backbone or fully-finetuning a smaller backbone with the same GPU and less training time.
IIRP-Net: Iterative Inference Residual Pyramid Network for Enhanced Image Registration
Authors: Tai Ma, Suwei Zhang, Jiafeng Li, Ying Wen
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Registration

    Deep learning-based image registration (DLIR) methods have achieved remarkable success in deformable image registration. We observe that iterative inference can exploit the well-trained registration network to the fullest extent. In this work we propose a novel Iterative Inference Residual Pyramid Network (IIRP-Net) to enhance registration performance without any additional training costs. In IIRP-Net we construct a streamlined pyramid registration network consisting of a feature extractor and residual flow estimators (RP-Net) to achieve generalized capabilities in feature extraction and registration. Then in the inference phase IIRP-Net employs an iterative inference strategy to enhance RP-Net by iteratively reutilizing residual flow estimators from coarse to fine. The number of iterations is adaptively determined by the proposed IterStop mechanism. We conduct extensive experiments on the FLARE and Mindboggle datasets and the results verify the effectiveness of the proposed method outperforming state-of-the-art deformable image registration methods. Our code is available at https://github.com/Torbjorn1997/IIRP-Net.
E-GPS: Explainable Geometry Problem Solving via Top-Down Solver and Bottom-Up Generator
Authors: Wenjun Wu, Lingling Zhang, Jun Liu, Xi Tang, Yaxian Wang, Shaowei Wang, Qianying Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Geometry Problem Solving

    Geometry Problem Solving has drawn growing attention recently due to its application prospects in intelligent education field. However existing methods are still inadequate to meet the needs of practical application suffering from the following limitations: 1) explainability is not ensured which is essential in real teaching scenarios; 2) the small scale and incomplete annotation of existing datasets make it hard for model to comprehend geometric knowledge. To tackle the above problems we propose a novel method called Explainable Geometry Problem Solving (E-GPS). E-GPS first parses the geometric diagram and problem text into unified formal language representations. Then the answer and explainable reasoning and solving steps are obtained by a Top-Down Problem Solver (TD-PS) which innovatively solves the problem from the target and focuses on what is needed. To alleviate the data issues a Bottom-Up Problem Generator (BU-PG) is devised to augment the data set with various well-annotated constructed geometry problems. It enables us to train an enhanced theorem predictor with a better grasp of theorem knowledge which further improves the efficiency of TD-PS. Extensive experiments demonstrate that E-GPS maintains comparable solving performances with fewer steps and provides outstanding explainability.
TransLoc4D: Transformer-based 4D Radar Place Recognition
Authors: Guohao Peng, Heshan Li, Yangyang Zhao, Jun Zhang, Zhenyu Wu, Pengyu Zheng, Danwei Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Place Recognition is crucial for unmanned vehicles in terms of localization and mapping. Recent years have witnessed numerous explorations in the field where 2D cameras and 3D LiDARs are mostly employed. Despite their admirable performance they may encounter challenges in adverse weather such as rain and fog. Hopefully 4D millimeter-wave Radar emerges as a promising alternative as its longer wavelength makes it virtually immune to interference from tiny particles of fog and rain. Therefore in this work we propose a novel 4D Radar place recognition model TransLoc4D based on sparse convolution and Transformer structures. Specifically a Minkloc4D backbone is first proposed to leverage the geometric intensity and velocity information from 4D Radar scans. While mainstream 3D LiDAR solutions merely capture geometric structures of point clouds Minkloc4D explores the intensity and velocity properties of 4D Radar scans and demonstrates their effectiveness. After feature extraction a Transformer layer is introduced to enhance local features where linear self-attention captures the long-range dependency of point cloud alleviating its sparsity and noise. To validate TransLoc4D we construct two datasets and set up benchmarks for 4D radar place recognition. Experiments show TransLoc4D is feasible and can robustly deal with dynamic and adverse environments.
Virtual Immunohistochemistry Staining for Histological Images Assisted by Weakly-supervised Learning
Authors: Jiahan Li, Jiuyang Dong, Shenjin Huang, Xi Li, Junjun Jiang, Xiaopeng Fan, Yongbing Zhang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Multiple Instance Learning, Virtual Staining, Weakly-supervised Learning

    Recently virtual staining technology has greatly promoted the advancement of histopathology. Despite the practical successes achieved the outstanding performance of most virtual staining methods relies on hard-to-obtain paired images in training. In this paper we propose a method for virtual immunohistochemistry (IHC) staining named confusion-GAN which does not require paired images and can achieve comparable performance to supervised algorithms. Specifically we propose a multi-branch discriminator which judges if the features of generated images can be embedded into the feature pool of target domain images to improve the visual quality of generated images. Meanwhile we also propose a novel patch-level pathology information extractor which is assisted by multiple instance learning to ensure pathological consistency during virtual staining. Extensive experiments were conducted on three types of IHC images including a high-resolution hepatocellular carcinoma immunohistochemical dataset proposed by us. The results demonstrated that our proposed confusion-GAN can generate highly realistic images that are capable of deceiving even experienced pathologists. Furthermore compared to using H&E images directly the downstream diagnosis achieved higher accuracy when using images generated by confusion-GAN. Our dataset and codes will be available at https://github.com/jiahanli2022/confusion-GAN.
Referring Image Editing: Object-level Image Editing via Referring Expressions
Authors: Chang Liu, Xiangtai Li, Henghui Ding
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Semantic Segmentation

    Significant advancements have been made in image editing with the recent advance of the Diffusion model. However most of the current methods primarily focus on global or subject-level modifications and often face limitations when it comes to editing specific objects when there are other objects coexisting in the scene given solely textual prompts. In response to this challenge we introduce an object-level generative task called Referring Image Editing (RIE) which enables the identification and editing of specific source objects in an image using text prompts. To tackle this task effectively we propose a tailored framework called ReferDiffusion. It aims to disentangle input prompts into multiple embeddings and employs a mixed-supervised multi-stage training strategy. To facilitate further research in this domain we introduce the RefCOCO-Edit dataset comprising images editing prompts source object segmentation masks and reference edited images for training and evaluation. Our extensive experiments demonstrate the effectiveness of our approach in identifying and editing target objects while conventional general image editing and region-based image editing methods have difficulties in this challenging task.
Learning to Predict Activity Progress by Self-Supervised Video Alignment
Authors: Gerard Donahue, Ehsan Elhamifar
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Representation Learning, Video Alignment

    In this paper we tackle the problem of self-supervised video alignment and activity progress prediction using in-the-wild videos. Our proposed self-supervised representation learning method carefully addresses different action orderings redundant actions and background frames to generate improved video representations compared to previous methods. Our model generalizes temporal cycle-consistency learning to allow for more flexibility in determining cycle-consistent neighbors. More specifically to handle repeated actions we propose a multi-neighbor cycle consistency and a multi-cycle-back regression loss by finding multiple soft nearest neighbors using a Gaussian Mixture Model. To handle background and redundant frames we introduce a context-dependent drop function in our framework discouraging the alignment of droppable frames. On the other hand to learn from videos of multiple activities jointly we propose a multi-head crosstask network allowing us to embed a video and estimate progress without knowing its activity label. Experiments on multiple datasets show that our method outperforms the state-of-the-art for video alignment and progress prediction.
DiffusionTrack: Point Set Diffusion Model for Visual Object Tracking
Authors: Fei Xie, Zhongdao Wang, Chao Ma
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Denoising, Object Tracking, Visual Object Tracking, Visual Tracking

    Existing Siamese or transformer trackers commonly pose visual object tracking as a one-shot detection problem i.e. locating the target object in a single forward evaluation scheme. Despite the demonstrated success these trackers may easily drift towards distractors with similar appearance due to the single forward evaluation scheme lacking self-correction. To address this issue we cast visual tracking as a point set based denoising diffusion process and propose a novel generative learning based tracker dubbed DiffusionTrack. Our DiffusionTrack possesses two appealing properties: 1) It follows a novel noise-to-target tracking paradigm that leverages multiple denoising diffusion steps to localize the target in a dynamic searching manner per frame. 2) It models the diffusion process using a point set representation which can better handle appearance variations for more precise localization. One side benefit is that DiffusionTrack greatly simplifies the post-processing e.g. removing window penalty scheme. Without bells and whistles our DiffusionTrack achieves leading performance over the state-of-the-art trackers and runs in real-time. The code is in https://github.com/VISION-SJTU/DiffusionTrack.
HOI-M^3: Capture Multiple Humans and Objects Interaction within Contextual Environment
Authors: Juze Zhang, Jingyan Zhang, Zining Song, Zhanhe Shi, Chengfeng Zhao, Ye Shi, Jingyi Yu, Lan Xu, Jingya Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Human-Object Interaction Detection, Object

    Humans naturally interact with both others and the surrounding multiple objects engaging in various social activities. However recent advances in modeling human-object interactions mostly focus on perceiving isolated individuals and objects due to fundamental data scarcity. In this paper we introduce HOI-M^3 a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects. Notably it provides accurate 3D tracking for both humans and objects from dense RGB and object-mounted IMU inputs covering 199 sequences and 181M frames of diverse humans and objects under rich activities. With the unique HOI-M^3 dataset we introduce two novel data-driven tasks with companion strong baselines: monocular capture and unstructured generation of multiple human-object interactions. Extensive experiments demonstrate that our dataset is challenging and worthy of further research about multiple human-object interactions and behavior analysis. Our HOI-M^3 dataset corresponding codes and pre-trained models will be disseminated to the community for future research.
FADES: Fair Disentanglement with Sensitive Relevance
Authors: Taeuk Jang, Xiaoqian Wang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: counterfactual, Disentanglement, Fairness

    Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches such as employing correlation-aware causal graphs or proxies for mutual information these methods are less practical and not applicable in general. In this work we propose FAir DisEntanglement with Sensitive relevance (FADES) a novel approach that leverages conditional mutual information from the information theory perspective to address these challenges. We employ sensitive relevant code to direct correlated information between target labels and sensitive attributes by imposing conditional independence allowing better separation of the features of interest in the latent space. Utilizing an intuitive disentangling approach FADES consistently achieves superior performance and fairness both quantitatively and qualitatively with its straightforward structure. Specifically the proposed method outperforms existing works in downstream classification and counterfactual generations on various benchmarks.
Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning
Authors: Pehuen Moure, Longbiao Cheng, Joachim Ott, Zuowen Wang, Shih-Chii Liu
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Out-of-Distribution Generalization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

    In order for reinforcement learning (RL) agents to be deployed in real-world environments they must be able to generalize to unseen environments. However RL struggles with out-of-distribution generalization often due to over-fitting the particulars of the training environment. Although regularization techniques from supervised learning can be applied to avoid over-fitting the differences between supervised learning and RL limit their application. To address this we propose the Signal-to-Noise Ratio regulated Parameter Uncertainty Network (SNR PUN) for RL. We introduce SNR as a new measure of regularizing the parameter uncertainty of a network and provide a formal analysis explaining why SNR regularization works well for RL. We demonstrate the effectiveness of our proposed method to generalize in several simulated environments; and in a physical system showing the possibility of using SNR PUN for applying RL to real-world applications.
V?: Guided Visual Search as a Core Mechanism in Multimodal LLMs
Authors: Penghao Wu, Saining Xie
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Visual Grounding, World Knowledge

    When we look around and perform complex tasks how we see and selectively process what we see is crucial. However the lack of this visual search mechanism in current multimodal LLMs (MLLMs) hinders their ability to focus on important visual details especially when handling high-resolution and visually crowded images. To address this we introduce V* an LLM-guided visual search mechanism that employs the world knowledge in LLMs for efficient visual querying. When combined with an MLLM this mechanism enhances collaborative reasoning contextual understanding and precise visual grounding. This integration results in a new MLLM meta-architecture named Show sEArch and TelL (SEAL). We further create V*Bench a benchmark specifically designed to evaluate MLLMs in their ability to process high-resolution images and focus on visual details. Our study highlights the necessity of incorporating visual search capabilities into multimodal systems. The code is available at https://github.com/penghao-wu/vstar
BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks
Authors: Shangqian Gao, yanfu Zhang, Feihu Huang, Heng Huang
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1

    Most existing dynamic or runtime channel pruning methods have to store all weights to achieve efficient inference which brings extra storage costs. Static pruning methods can reduce storage costs directly but their performance is limited by using a fixed sub-network to approximate the original model. Most existing pruning works suffer from these drawbacks because they were designed to only conduct either static or dynamic pruning. In this paper we propose a novel method to solve both efficiency and storage challenges via simultaneously conducting dynamic and static channel pruning for convolutional neural networks. We propose a new bi-level optimization based model to naturally integrate the static and dynamic channel pruning. By doing so our method enjoys benefits from both sides and the disadvantages of dynamic and static pruning are reduced. After pruning we permanently remove redundant parameters and then finetune the model with dynamic flexibility. Experimental results on CIFAR-10 and ImageNet datasets suggest that our method can achieve state-of-the-art performance compared to existing dynamic and static channel pruning methods.
Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion.
Authors: Weijian Ma, Shuaiqi Chen, Yunzhong Lou, Xueyang Li, Xiangdong Zhou
Date: 2024-01-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D geometry, CAD Reconstruction

    Reconstructing CAD construction sequences from raw 3D geometry serves as an interface between real-world objects and digital designs. In this paper we propose CAD-Diffuser a multimodal diffusion scheme aiming at integrating top-down design paradigm into generative reconstruction. In particular we unify CAD point clouds and CAD construction sequences at the token level guiding our proposed multimodal diffusion strategy to understand and link between the geometry and the design intent concentrated in construction sequences. Leveraging the strong decoding abilities of language models the forward process is modeled as a random walk between the original token and the [MASK] token while the reverse process naturally fits the masked token modeling scheme. A volume-based noise schedule is designed to encourage outline-first generation decomposing the top-down design methodology into a machine-understandable procedure. For tokenizing CAD data of multiple modalities we introduce a tokenizer with a self-supervised face segmentation task to compress local and global geometric information for CAD point clouds and the CAD construction sequence is transformed into a primitive token string. Experimental results show that our CAD-Diffuser can perceive geometric details and the results are more likely to be reused by human designers.