pwc-archive/papers-with-abstracts huggingface dataset:
Text2Loc: 3D Point Cloud Localization from Natural Language
Authors: Yan Xia, Letian Shi, Zifeng Ding, João F. Henriques, Daniel Cremers
Date: 2023-11-27 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Visual Place Recognition

We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network, Text2Loc, that fully interprets the semantic relationship between points and text. Text2Loc follows a coarse-to-fine localization pipeline: text-submap global place recognition, followed by fine localization. In global place recognition, relational dynamics among each textual hint are captured in a hierarchical transformer with max-pooling (HTM), whereas a balance between positive and negative pairs is maintained using text-submap contrastive learning. Moreover, we propose a novel matching-free fine localization method to further refine the location predictions, which completely removes the need for complicated text-instance matching and is lighter, faster, and more accurate than previous methods. Extensive experiments show that Text2Loc improves the localization accuracy by up to $2\times$ over the state-of-the-art on the KITTI360Pose dataset. Our project page is publicly available at \url{https://yan-xia.github.io/projects/text2loc/}.
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding
Authors: Thanh-Dat Truong, Utsav Prabhu, Bhiksha Raj, Jackson Cothren, Khoa Luu
Date: 2023-11-27 00:00:00
Proceeding: CVPR 2025 1
Tasks: Continual Learning, Continual Semantic Segmentation, Fairness, Scene Segmentation, Scene Understanding, Semantic Segmentation

Continual Learning in semantic scene segmentation aims to continually learn new unseen classes in dynamic environments while maintaining previously learned knowledge. Prior studies focused on modeling the catastrophic forgetting and background shift challenges in continual learning. However, fairness, another major challenge that causes unfair predictions leading to low performance among major and minor classes, still needs to be well addressed. In addition, prior methods have yet to model the unknown classes well, thus resulting in producing non-discriminative features among unknown classes. This paper presents a novel Fairness Learning via Contrastive Attention Approach to continual learning in semantic scene understanding. In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness. Then, we propose an attention-based visual grammar approach to effectively model the background shift problem and unknown classes, producing better feature representations for different unknown classes. Through our experiments, our proposed approach achieves State-of-the-Art (SOTA) performance on different continual learning benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC. It promotes the fairness of the continual semantic segmentation model.
From Pixels to Titles: Video Game Identification by Screenshots using Convolutional Neural Networks
Authors: Fabricio Breve
Date: 2023-11-27 00:00:00

This paper investigates video game identification through single screenshots, utilizing ten convolutional neural network (CNN) architectures (VGG16, ResNet50, ResNet152, MobileNet, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB2, EfficientNetB3, and EfficientNetV2S) and three transformers architectures (ViT-B16, ViT-L32, and SwinT) across 22 home console systems, spanning from Atari 2600 to PlayStation 5, totalling 8,796 games and 170,881 screenshots. Except for VGG16, all CNNs outperformed the transformers in this task. Using ImageNet pre-trained weights as initial weights, EfficientNetV2S achieves the highest average accuracy (77.44%) and the highest accuracy in 16 of the 22 systems. DenseNet201 is the best in four systems and EfficientNetB3 is the best in the remaining two systems. Employing alternative initial weights fine-tuned in an arcade screenshots dataset boosts accuracy for EfficientNet architectures, with the EfficientNetV2S reaching a peak accuracy of 77.63% and demonstrating reduced convergence epochs from 26.9 to 24.5 on average. Overall, the combination of optimal architecture and weights attains 78.79% accuracy, primarily led by EfficientNetV2S in 15 systems. These findings underscore the efficacy of CNNs in video game identification through screenshots.
Tell2Design: A Dataset for Language-Guided Floor Plan Generation
Authors: Sicong Leng, Yang Zhou, Mohammed Haroon Dupty, Wee Sun Lee, Sam Conrad Joyce, Wei Lu
Date: 2023-11-27 00:00:00
Tasks: Conditional Image Generation, Image Generation

We consider the task of generating designs directly from natural language descriptions, and consider floor plan generation as the initial research area. Language conditional generative models have recently been very successful in generating high-quality artistic images. However, designs must satisfy different constraints that are not present in generating artistic images, particularly spatial and relational constraints. We make multiple contributions to initiate research on this task. First, we introduce a novel dataset, \textit{Tell2Design} (T2D), which contains more than $80k$ floor plan designs associated with natural language instructions. Second, we propose a Sequence-to-Sequence model that can serve as a strong baseline for future research. Third, we benchmark this task with several text-conditional image generation models. We conclude by conducting human evaluations on the generated samples and providing an analysis of human performance. We hope our contributions will propel the research on language-guided design generation forward.
Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
Authors: Zhongyi Shui, Yunlong Zhang, Kai Yao, Chenglu Zhu, Sunyi Zheng, Jingxiong Li, Honglin Li, Yuxuan Sun, Ruizhe Guo, Lin Yang
Date: 2023-11-27 00:00:00
Tasks: Image Segmentation, Instance Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Nucleus instance segmentation in histology images is crucial for a broad spectrum of clinical applications. Current dominant algorithms rely on regression of nuclear proxy maps. Distinguishing nucleus instances from the estimated maps requires carefully curated post-processing, which is error-prone and parameter-sensitive. Recently, the Segment Anything Model (SAM) has earned huge attention in medical image segmentation, owing to its impressive generalization ability and promptable property. Nevertheless, its potential on nucleus instance segmentation remains largely underexplored. In this paper, we present a novel prompt-driven framework that consists of a nucleus prompter and SAM for automatic nucleus instance segmentation. Specifically, the prompter learns to generate a unique point prompt for each nucleus while the SAM is fine-tuned to output the corresponding mask for the prompted nucleus. Furthermore, we propose the inclusion of adjacent nuclei as negative prompts to enhance the model's capability to identify overlapping nuclei. Without complicated post-processing, our proposed method sets a new state-of-the-art performance on three challenging benchmarks. Code is available at \url{github.com/windygoo/PromptNucSeg}
Optimal Transport Aggregation for Visual Place Recognition
Authors: Sergio Izquierdo, Javier Civera
Date: 2023-11-27 00:00:00
Proceeding: CVPR 2024 1
Tasks: Re-Ranking, Visual Place Recognition

The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places, relying solely on visual cues. State-of-the-art pipelines focus on the aggregation of features extracted from a deep backbone, in order to form a global descriptor for each image. In this context, we introduce SALAD (Sinkhorn Algorithm for Locally Aggregated Descriptors), which reformulates NetVLAD's soft-assignment of local features to clusters as an optimal transport problem. In SALAD, we consider both feature-to-cluster and cluster-to-feature relations and we also introduce a 'dustbin' cluster, designed to selectively discard features deemed non-informative, enhancing the overall descriptor quality. Additionally, we leverage and fine-tune DINOv2 as a backbone, which provides enhanced description power for the local features, and dramatically reduces the required training time. As a result, our single-stage method not only surpasses single-stage baselines in public VPR datasets, but also surpasses two-stage methods that add a re-ranking with significantly higher cost. Code and models are available at https://github.com/serizba/salad.
ADM-Loc: Actionness Distribution Modeling for Point-supervised Temporal Action Localization
Authors: Elahe Vahdani, YingLi Tian
Date: 2023-11-27 00:00:00
Tasks: Action Classification, Action Detection, Action Localization, Classification, Temporal Action Localization

This paper addresses the challenge of point-supervised temporal action detection, in which only one frame per action instance is annotated in the training set. Self-training aims to provide supplementary supervision for the training process by generating pseudo-labels (action proposals) from a base model. However, most current methods generate action proposals by applying manually designed thresholds to action classification probabilities and treating adjacent snippets as independent entities. As a result, these methods struggle to generate complete action proposals, exhibit sensitivity to fluctuations in action classification scores, and generate redundant and overlapping action proposals. This paper proposes a novel framework termed ADM-Loc, which stands for Actionness Distribution Modeling for point-supervised action Localization. ADM-Loc generates action proposals by fitting a composite distribution, comprising both Gaussian and uniform distributions, to the action classification signals. This fitting process is tailored to each action class present in the video and is applied separately for each action instance, ensuring the distinctiveness of their distributions. ADM-Loc significantly enhances the alignment between the generated action proposals and ground-truth action instances and offers high-quality pseudo-labels for self-training. Moreover, to model action boundary snippets, it enforces consistency in action classification scores during training by employing Gaussian kernels, supervised with the proposed loss functions. ADM-Loc outperforms the state-of-the-art point-supervised methods on THUMOS14 and ActivityNet-v1.2 datasets.
Computer Vision for Carriers: PATRIOT
Authors: Ari Goodman, Gurpreet Singh, James Hing, Ryan O'Shea
Date: 2023-11-27 00:00:00

Deck tracking performed on carriers currently involves a team of sailors manually identifying aircraft and updating a digital user interface called the Ouija Board. Improvements to the deck tracking process would result in increased Sortie Generation Rates, and therefore applying automation is seen as a critical method to improve deck tracking. However, the requirements on a carrier ship do not allow for the installation of hardware-based location sensing technologies like Global Positioning System (GPS) sensors. PATRIOT (Panoramic Asset Tracking of Real-Time Information for the Ouija Tabletop) is a research effort and proposed solution to performing deck tracking with passive sensing and without the need for GPS sensors. PATRIOT is a prototype system which takes existing camera feeds, calculates aircraft poses, and updates a virtual Ouija board interface with the current status of the assets. PATRIOT would allow for faster, more accurate, and less laborious asset tracking for aircraft, people, and support equipment. PATRIOT is anticipated to benefit the warfighter by reducing cognitive workload, reducing manning requirements, collecting data to improve logistics, and enabling an automation gateway for future efforts to improve efficiency and safety. The authors have developed and tested algorithms to perform pose estimations of assets in real-time including OpenPifPaf, High-Resolution Network (HRNet), HigherHRNet (HHRNet), Faster R-CNN, and in-house developed encoder-decoder network. The software was tested with synthetic and real-world data and was able to accurately extract the pose of assets. Fusion, tracking, and real-world generality are planned to be improved to ensure a successful transition to the fleet.
LIFT OFF: LoRaWAN Installation and Fiducial Tracking Operations for the Flightline of the Future
Authors: Ari Goodman, Ryan O'Shea
Date: 2023-11-27 00:00:00

Real-time situational awareness for the location of assets is critical to ensure missions are completed efficiently and requirements are satisfied. In many commercial settings, the application of global positioning system (GPS) sensors is appropriate to achieve timely knowledge of the position of people and equipment. However, GPS sensors are not appropriate for all situations due to flight clearance and operations security concerns. LIFT OFF: LoRaWAN Installation and Fiducial Tracking Operations for the Flightline of the Future proposes a hybrid framework solution to achieve real-time situational awareness for people, support equipment, and aircraft positions regardless of the environment. This framework included a machine-vision component, which involved setting up cameras to detect AprilTag decals that were installed on the sides of aircraft. The framework included a geolocation sensor component, which involved installing GPS sensors on support equipment and helmets. The framework also included creating a long-range wide area network (LoRaWAN) to transfer data and developing a user interface to display the data. The framework was tested at Naval Air Station Oceana Flightline, the United States Naval Test Pilot School, and at Naval Air Warfare Center Aircraft Division Lakehurst. LIFT OFF successfully provided a real-time updating map of all tracked assets using GPS sensors for people and support equipment and with visual fiducials for aircraft. The trajectories of the assets were recorded for logistical analysis and playback. Future follow-on work is anticipated to apply the technology to other environments including carriers and amphibious assault ships in addition to the flightline.
Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models
Authors: Claudio Rota, Marco Buzzelli, Joost Van de Weijer
Date: 2023-11-27 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Video Super-Resolution

In this paper, we address the problem of enhancing perceptual quality in video super-resolution (VSR) using Diffusion Models (DMs) while ensuring temporal consistency among frames. We present StableVSR, a VSR method based on DMs that can significantly enhance the perceptual quality of upscaled videos by synthesizing realistic and temporally-consistent details. We introduce the Temporal Conditioning Module (TCM) into a pre-trained DM for single image super-resolution to turn it into a VSR method. TCM uses the novel Temporal Texture Guidance, which provides it with spatially-aligned and detail-rich texture information synthesized in adjacent frames. This guides the generative process of the current frame toward high-quality and temporally-consistent results. In addition, we introduce the novel Frame-wise Bidirectional Sampling strategy to encourage the use of information from past to future and vice-versa. This strategy improves the perceptual quality of the results and the temporal consistency across frames. We demonstrate the effectiveness of StableVSR in enhancing the perceptual quality of upscaled videos while achieving better temporal consistency compared to existing state-of-the-art methods for VSR. The project page is available at https://github.com/claudiom4sir/StableVSR.
MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization
Authors: Can Sun, Hao Zheng, Zhigang Hu, Liu Yang, Meiguang Zheng, Bo Xu
Date: 2023-11-27 00:00:00
Tasks: Domain Generalization, Meta-Learning

The single domain generalization(SDG) based on meta-learning has emerged as an effective technique for solving the domain-shift problem. However, the inadequate match of data distribution between source and augmented domains and difficult separation of domain-invariant features from domain-related features make SDG model hard to achieve great generalization. Therefore, a novel meta-learning method based on domain enhancement and feature alignment (MetaDefa) is proposed to improve the model generalization performance. First, the background substitution and visual corruptions techniques are used to generate diverse and effective augmented domains. Then, the multi-channel feature alignment module based on class activation maps and class agnostic activation maps is designed to effectively extract adequate transferability knowledge. In this module, domain-invariant features can be fully explored by focusing on similar target regions between source and augmented domains feature space and suppressing the feature representation of non-similar target regions. Extensive experiments on two publicly available datasets show that MetaDefa has significant generalization performance advantages in unknown multiple target domains.
Data Generation for Post-OCR correction of Cyrillic handwriting
Authors: Evgenii Davydkin, Aleksandr Markelov, Egor Iuldashev, Anton Dudkin, Ivan Krivorotov
Date: 2023-11-27 00:00:00
Tasks: Handwriting generation, Handwritten Text Recognition, HTR, Optical Character Recognition, Optical Character Recognition (OCR), Text Generation

This paper introduces a novel approach to post-Optical Character Recognition Correction (POC) for handwritten Cyrillic text, addressing a significant gap in current research methodologies. This gap is due to the lack of large text corporas that provide OCR errors for further training of language-based POC models, which are demanding in terms of corpora size. Our study primarily focuses on the development and application of a synthetic handwriting generation engine based on B\'ezier curves. Such an engine generates highly realistic handwritten text in any amounts, which we utilize to create a substantial dataset by transforming Russian text corpora sourced from the internet. We apply a Handwritten Text Recognition (HTR) model to this dataset to identify OCR errors, forming the basis for our POC model training. The correction model is trained on a 90-symbol input context, utilizing a pre-trained T5 architecture with a seq2seq correction task. We evaluate our approach on HWR200 and School_notebooks_RU datasets as they provide significant challenges in the HTR domain. Furthermore, POC can be used to highlight errors for teachers, evaluating student performance. This can be done simply by comparing sentences before and after correction, displaying differences in text. Our primary contribution lies in the innovative use of B\'ezier curves for Cyrillic text generation and subsequent error correction using a specialized POC model. We validate our approach by presenting Word Accuracy Rate (WAR) and Character Accuracy Rate (CAR) results, both with and without post-OCR correction, using real open corporas of handwritten Cyrillic text. These results, coupled with our methodology, are designed to be reproducible, paving the way for further advancements in the field of OCR and handwritten text analysis. Paper contributions can be found in https://github.com/dbrainio/CyrillicHandwritingPOC
Stability-Informed Initialization of Neural Ordinary Differential Equations
Authors: Theodor Westny, Arman Mohammadi, Daniel Jung, Erik Frisk
Date: 2023-11-27 00:00:00
Tasks: Numerical Integration

This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques. It is shown how the choice of integration technique implicitly regularizes the learned model, and how the solver's corresponding stability region affects training and prediction performance. From this analysis, a stability-informed parameter initialization technique is introduced. The effectiveness of the initialization method is displayed across several learning benchmarks and industrial applications.
EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension
Authors: Jiaxuan Li, Duc Minh Vo, Akihiro Sugimoto, Hideki Nakayama
Date: 2023-11-27 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Captioning, Object, Retrieval

Large language models (LLMs)-based image captioning has the capability of describing objects not explicitly observed in training data; yet novel objects occur frequently, necessitating the requirement of sustaining up-to-date object knowledge for open-world comprehension. Instead of relying on large amounts of data and/or scaling up network parameters, we introduce a highly effective retrieval-augmented image captioning method that prompts LLMs with object names retrieved from External Visual--name memory (EVCap). We build ever-changing object knowledge memory using objects' visuals and names, enabling us to (i) update the memory at a minimal cost and (ii) effortlessly augment LLMs with retrieved object names by utilizing a lightweight and fast-to-train model. Our model, which was trained only on the COCO dataset, can adapt to out-of-domain without requiring additional fine-tuning or re-training. Our experiments conducted on benchmarks and synthetic commonsense-violating data show that EVCap, with only 3.97M trainable parameters, exhibits superior performance compared to other methods based on frozen pre-trained LLMs. Its performance is also competitive to specialist SOTAs that require extensive training.
InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint
Authors: Zhenzhi Wang, Jingbo Wang, Yixuan Li, Dahua Lin, Bo Dai
Date: 2023-11-27 00:00:00
Tasks: Language Modelling, Large Language Model, Motion Generation, Motion Synthesis

Text-conditioned motion synthesis has made remarkable progress with the emergence of diffusion models. However, the majority of these motion diffusion models are primarily designed for a single character and overlook multi-human interactions. In our approach, we strive to explore this problem by synthesizing human motion with interactions for a group of characters of any size in a zero-shot manner. The key aspect of our approach is the adaptation of human-wise interactions as pairs of human joints that can be either in contact or separated by a desired distance. In contrast to existing methods that necessitate training motion generation models on multi-human motion datasets with a fixed number of characters, our approach inherently possesses the flexibility to model human interactions involving an arbitrary number of individuals, thereby transcending the limitations imposed by the training data. We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. It consists of a motion controller and an inverse kinematics guidance module that realistically and accurately aligns the joints of synthesized characters to the desired location. Furthermore, we demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model (LLM). Experimental results highlight the capability of our framework to generate interactions with multiple human characters and its potential to work with off-the-shelf physics-based character simulators. Code is available at https://github.com/zhenzhiwang/intercontrol