pwc-archive/papers-with-abstracts huggingface dataset:
Satellite Imagery and AI: A New Era in Ocean Conservation, from Research to Deployment and Impact
Authors: Patrick Beukema, Favyen Bastani, Piper Wolters, Henry Herzog, Joe Ferdinando
Date: 2023-12-06 00:00:00

Illegal, unreported, and unregulated (IUU) fishing poses a global threat to ocean habitats. Publicly available satellite data offered by NASA and the European Space Agency (ESA) provide an opportunity to actively monitor this activity. Effectively leveraging satellite data for maritime conservation requires highly reliable machine learning models operating globally with minimal latency. This paper introduces three specialized computer vision models designed for synthetic aperture radar (Sentinel-1), optical imagery (Sentinel-2), and nighttime lights (Suomi-NPP/NOAA-20). It also presents best practices for developing and delivering real-time computer vision services for conservation. These models have been deployed in Skylight, a real time maritime monitoring platform, which is provided at no cost to users worldwide.
Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields
Authors: Shijie Zhou, Haoran Chang, Sicheng Jiang, Zhiwen Fan, Zehao Zhu, Dejia Xu, Pradyumna Chari, Suya You, Zhangyang Wang, Achuta Kadambi
Date: 2023-12-06 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3DGS, 3D scene Editing, Natural Language Queries, NeRF, Novel View Synthesis, Semantic Segmentation

3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times, some work has emerged that aims to extend the functionality of NeRF beyond view synthesis, for semantically aware tasks such as editing and segmentation using 3D feature field distillation from 2D foundation models. However, these methods have two major limitations: (a) they are limited by the rendering speed of NeRF pipelines, and (b) implicitly represented feature fields suffer from continuity artifacts reducing feature quality. Recently, 3D Gaussian Splatting has shown state-of-the-art performance on real-time radiance field rendering. In this work, we go one step further: in addition to radiance field rendering, we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation. This translation is not straightforward: naively incorporating feature fields in the 3DGS framework encounters significant challenges, notably the disparities in spatial resolution and channel consistency between RGB images and feature maps. We propose architectural and training changes to efficiently avert this problem. Our proposed method is general, and our experiments showcase novel view semantic segmentation, language-guided editing and segment anything through learning feature fields from state-of-the-art 2D foundation models such as SAM and CLIP-LSeg. Across experiments, our distillation method is able to provide comparable or better results, while being significantly faster to both train and render. Additionally, to the best of our knowledge, we are the first method to enable point and bounding-box prompting for radiance field manipulation, by leveraging the SAM model. Project website at: https://feature-3dgs.github.io/
HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces
Authors: Haithem Turki, Vasu Agrawal, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Deva Ramanan, Michael Zollhöfer, Christian Richardt
Date: 2023-12-05 00:00:00
Proceeding: CVPR 2024 1
Tasks: Neural Rendering

Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. One reason is that they make use of volume rendering, thus requiring many samples (and model queries) per ray at render time. Although this representation is flexible and easy to optimize, most real-world objects can be modeled more efficiently with surfaces instead of volumes, requiring far fewer samples per ray. This observation has spurred considerable progress in surface representations such as signed distance functions, but these may struggle to model semi-opaque and thin structures. We propose a method, HybridNeRF, that leverages the strengths of both representations by rendering most objects as surfaces while modeling the (typically) small fraction of challenging regions volumetrically. We evaluate HybridNeRF against the challenging Eyeful Tower dataset along with other commonly used view synthesis datasets. When comparing to state-of-the-art baselines, including recent rasterization-based approaches, we improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2Kx2K).
ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet
Authors: Soon Yau Cheong, Armin Mustafa, Andrew Gilbert
Date: 2023-12-05 00:00:00
Tasks: Image Generation, Person Re-Identification, Virtual Try-on, Visual Prompting

This paper introduces ViscoNet, a novel one-branch-adapter architecture for concurrent spatial and visual conditioning. Our lightweight model requires trainable parameters and dataset size multiple orders of magnitude smaller than the current state-of-the-art IP-Adapter. However, our method successfully preserves the generative power of the frozen text-to-image (T2I) backbone. Notably, it excels in addressing mode collapse, a pervasive issue previously overlooked. Our novel architecture demonstrates outstanding capabilities in achieving a harmonious visual-text balance, unlocking unparalleled versatility in various human image generation tasks, including pose re-targeting, virtual try-on, stylization, person re-identification, and textile transfer.Demo and code are available from project page https://soon-yau.github.io/visconet/ .
Predicting Bone Degradation Using Vision Transformer and Synthetic Cellular Microstructures Dataset
Authors: Mohammad Saber Hashemi, Azadeh Sheidaei
Date: 2023-12-05 00:00:00

Bone degradation, especially for astronauts in microgravity conditions, is crucial for space exploration missions since the lower applied external forces accelerate the diminution in bone stiffness and strength substantially. Although existing computational models help us understand this phenomenon and possibly restrict its effect in the future, they are time-consuming to simulate the changes in the bones, not just the bone microstructures, of each individual in detail. In this study, a robust yet fast computational method to predict and visualize bone degradation has been developed. Our deep-learning method, TransVNet, can take in different 3D voxelized images and predict their evolution throughout months utilizing a hybrid 3D-CNN-VisionTransformer autoencoder architecture. Because of limited available experimental data and challenges of obtaining new samples, a digital twin dataset of diverse and initial bone-like microstructures was generated to train our TransVNet on the evolution of the 3D images through a previously developed degradation model for microgravity.
AI-SAM: Automatic and Interactive Segment Anything Model
Authors: Yimu Pan, Sitao Zhang, Alison D. Gernand, Jeffery A. Goldstein, James Z. Wang
Date: 2023-12-05 00:00:00
Tasks: Medical Image Segmentation, Semantic Segmentation

Semantic segmentation is a core task in computer vision. Existing methods are generally divided into two categories: automatic and interactive. Interactive approaches, exemplified by the Segment Anything Model (SAM), have shown promise as pre-trained models. However, current adaptation strategies for these models tend to lean towards either automatic or interactive approaches. Interactive methods depend on prompts user input to operate, while automatic ones bypass the interactive promptability entirely. Addressing these limitations, we introduce a novel paradigm and its first model: the Automatic and Interactive Segment Anything Model (AI-SAM). In this paradigm, we conduct a comprehensive analysis of prompt quality and introduce the pioneering Automatic and Interactive Prompter (AI-Prompter) that automatically generates initial point prompts while accepting additional user inputs. Our experimental results demonstrate AI-SAM's effectiveness in the automatic setting, achieving state-of-the-art performance. Significantly, it offers the flexibility to incorporate additional user prompts, thereby further enhancing its performance. The project page is available at https://github.com/ymp5078/AI-SAM.
The Automated Bias Triangle Feature Extraction Framework
Authors: Madeleine Kotzagiannidis, Jonas Schuff, Nathan Korda
Date: 2023-12-05 00:00:00

Bias triangles represent features in stability diagrams of Quantum Dot (QD) devices, whose occurrence and property analysis are crucial indicators for spin physics. Nevertheless, challenges associated with quality and availability of data as well as the subtlety of physical phenomena of interest have hindered an automatic and bespoke analysis framework, often still relying (in part) on human labelling and verification. We introduce a feature extraction framework for bias triangles, built from unsupervised, segmentation-based computer vision methods, which facilitates the direct identification and quantification of physical properties of the former. Thereby, the need for human input or large training datasets to inform supervised learning approaches is circumvented, while additionally enabling the automation of pixelwise shape and feature labeling. In particular, we demonstrate that Pauli Spin Blockade (PSB) detection can be conducted effectively, efficiently and without any training data as a direct result of this approach.
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
Authors: Sean I. Young, Yaël Balbastre, Bruce Fischl, Polina Golland, Juan Eugenio Iglesias
Date: 2023-12-05 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Reconstruction, Depth Estimation, Motion Estimation

In magnetic resonance imaging (MRI), slice-to-volume reconstruction (SVR) refers to computational reconstruction of an unknown 3D magnetic resonance volume from stacks of 2D slices corrupted by motion. While promising, current SVR methods require multiple slice stacks for accurate 3D reconstruction, leading to long scans and limiting their use in time-sensitive applications such as fetal fMRI. Here, we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion. Inspired by the recent success of single-view depth estimation methods, we formulate SVR as a single-stack motion estimation task and train a fully convolutional network to predict a motion stack for a given slice stack, producing a 3D reconstruction as a byproduct of the predicted motion. Extensive experiments on the SVR of adult and fetal brains demonstrate that our fully convolutional method is twice as accurate as previous SVR methods. Our code is available at github.com/seannz/svr.
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
Authors: Xiaohu Lu, Hayder Radha
Date: 2023-12-05 00:00:00
Tasks: 3D Object Detection, Adversarial Attack, Adversarial Robustness, Object, object-detection, Object Detection

The adversarial robustness of a model is its ability to resist adversarial attacks in the form of small perturbations to input data. Universal adversarial attack methods such as Fast Sign Gradient Method (FSGM) and Projected Gradient Descend (PGD) are popular for LiDAR object detection, but they are often deficient compared to task-specific adversarial attacks. Additionally, these universal methods typically require unrestricted access to the model's information, which is difficult to obtain in real-world applications. To address these limitations, we present a black-box Scaling Adversarial Robustness (ScAR) method for LiDAR object detection. By analyzing the statistical characteristics of 3D object detection datasets such as KITTI, Waymo, and nuScenes, we have found that the model's prediction is sensitive to scaling of 3D instances. We propose three black-box scaling adversarial attack methods based on the available information: model-aware attack, distribution-aware attack, and blind attack. We also introduce a strategy for generating scaling adversarial examples to improve the model's robustness against these three scaling adversarial attacks. Comparison with other methods on public datasets under different 3D object detection architectures demonstrates the effectiveness of our proposed method. Our code is available at https://github.com/xiaohulugo/ScAR-IROS2023.
LooseControl: Lifting ControlNet for Generalized Depth Conditioning
Authors: Shariq Farooq Bhat, Niloy J. Mitra, Peter Wonka
Date: 2023-12-05 00:00:00
Tasks: Attribute, Image Generation

We present LooseControl to allow generalized depth conditioning for diffusion-based image generation. ControlNet, the SOTA for depth-conditioned image generation, produces remarkable results but relies on having access to detailed depth maps for guidance. Creating such exact depth maps, in many scenarios, is challenging. This paper introduces a generalized version of depth conditioning that enables many new content-creation workflows. Specifically, we allow (C1) scene boundary control for loosely specifying scenes with only boundary conditions, and (C2) 3D box control for specifying layout locations of the target objects rather than the exact shape and appearance of the objects. Using LooseControl, along with text guidance, users can create complex environments (e.g., rooms, street views, etc.) by specifying only scene boundaries and locations of primary objects. Further, we provide two editing mechanisms to refine the results: (E1) 3D box editing enables the user to refine images by changing, adding, or removing boxes while freezing the style of the image. This yields minimal changes apart from changes induced by the edited boxes. (E2) Attribute editing proposes possible editing directions to change one particular aspect of the scene, such as the overall object density or a particular object. Extensive tests and comparisons with baselines demonstrate the generality of our method. We believe that LooseControl can become an important design tool for easily creating complex environments and be extended to other forms of guidance channels. Code and more information are available at https://shariqfarooq123.github.io/loose-control/ .
Adaptive Multi-step Refinement Network for Robust Point Cloud Registration
Authors: Zhi Chen, Yufan Ren, Tong Zhang, Zheng Dang, Wenbing Tao, Sabine Süsstrunk, Mathieu Salzmann
Date: 2023-12-05 00:00:00
Tasks: Denoising, Point Cloud Registration

Point Cloud Registration (PCR) estimates the relative rigid transformation between two point clouds of the same scene. Despite significant progress with learning-based approaches, existing methods still face challenges when the overlapping region between the two point clouds is small. In this paper, we propose an adaptive multi-step refinement network that refines the registration quality at each step by leveraging the information from the preceding step. To achieve this, we introduce a training procedure and a refinement network. Firstly, to adapt the network to the current step, we utilize a generalized one-way attention mechanism, which prioritizes the last step's estimated overlapping region, and we condition the network on step indices. Secondly, instead of training the network to map either random transformations or a fixed pre-trained model's estimations to the ground truth, we train it on transformations with varying registration qualities, ranging from accurate to inaccurate, thereby enhancing the network's adaptiveness and robustness. Despite its conceptual simplicity, our method achieves state-of-the-art performance on both the 3DMatch/3DLoMatch and KITTI benchmarks. Notably, on 3DLoMatch, our method reaches 80.4% recall rate, with an absolute improvement of 1.2%.
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models
Authors: Yushi Hu, Otilia Stretcu, Chun-Ta Lu, Krishnamurthy Viswanathan, Kenji Hata, Enming Luo, Ranjay Krishna, Ariel Fuxman
Date: 2023-12-05 00:00:00
Proceeding: CVPR 2024 1
Tasks: Language Modeling, Language Modelling, Large Language Model, Meme Classification, Object Counting, Visual Question Answering (VQA)

Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However, generated programs are error-prone: they omit necessary steps, include spurious ones, and are unable to recover when the specialized models give incorrect outputs. Moreover, they require loading multiple models, incurring high latency and computation costs. We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs, which are then executed and verified to identify a correct one. It translates each correct program into a language description of the reasoning steps, which are then distilled into a VLM. Extensive experiments show that VPD improves the VLM's ability to count, understand spatial relations, and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs, achieving state-of-the-art performance across complex vision tasks, including MMBench, OK-VQA, A-OKVQA, TallyQA, POPE, and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally, experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data.
HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding
Authors: Trong-Thuan Nguyen, Pha Nguyen, Khoa Luu
Date: 2023-12-05 00:00:00
Proceeding: CVPR 2024 1
Tasks: Diversity, Graph Generation, Position, Relation, Scene Graph Generation, Video Understanding

Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a simple relationship model. These methods, however, struggle with a diversity of appearance, situation, position, interaction, and relation in videos. This limitation hinders the ability to fully comprehend the interplay within the complex visual dynamics of subjects. In this paper, we delve into interactivities understanding within visual content by deriving scene graph representations from dense interactivities among humans and objects. To achieve this goal, we first present a new dataset containing Appearance-Situation-Position-Interaction-Relation predicates, named ASPIRe, offering an extensive collection of videos marked by a wide range of interactivities. Then, we propose a new approach named Hierarchical Interlacement Graph (HIG), which leverages a unified layer and graph within a hierarchical structure to provide deep insights into scene changes across five distinct tasks. Our approach demonstrates superior performance to other methods through extensive experiments conducted in various scenarios.
DGInStyle: Domain-Generalizable Semantic Segmentation with Image Diffusion Models and Stylized Semantic Control
Authors: Yuru Jia, Lukas Hoyer, Shengyu Huang, Tianfu Wang, Luc van Gool, Konrad Schindler, Anton Obukhov
Date: 2023-12-05 00:00:00
Tasks: Autonomous Driving, Domain Generalization, Semantic Segmentation

Large, pretrained latent diffusion models (LDMs) have demonstrated an extraordinary ability to generate creative content, specialize to user data through few-shot fine-tuning, and condition their output on other modalities, such as semantic maps. However, are they usable as large-scale data generators, e.g., to improve tasks in the perception stack, like semantic segmentation? We investigate this question in the context of autonomous driving, and answer it with a resounding "yes". We propose an efficient data generation pipeline termed DGInStyle. First, we examine the problem of specializing a pretrained LDM to semantically-controlled generation within a narrow domain. Second, we propose a Style Swap technique to endow the rich generative prior with the learned semantic control. Third, we design a Multi-resolution Latent Fusion technique to overcome the bias of LDMs towards dominant objects. Using DGInStyle, we generate a diverse dataset of street scenes, train a domain-agnostic semantic segmentation model on it, and evaluate the model on multiple popular autonomous driving datasets. Our approach consistently increases the performance of several domain generalization methods compared to the previous state-of-the-art methods. Source code and dataset are available at https://dginstyle.github.io.
MagicStick: Controllable Video Editing via Control Handle Transformations
Authors: Yue Ma, Xiaodong Cun, Sen Liang, Jinbo Xing, Yingqing He, Chenyang Qi, Siran Chen, Qifeng Chen
Date: 2023-12-05 00:00:00
Tasks: Video Editing, Video Generation

Text-based video editing has recently attracted considerable interest in changing the style or replacing the objects with a similar structure. Beyond this, we demonstrate that properties such as shape, size, location, motion, etc., can also be edited in videos. Our key insight is that the keyframe transformations of the specific internal feature (e.g., edge maps of objects or human pose), can easily propagate to other frames to provide generation guidance. We thus propose MagicStick, a controllable video editing method that edits the video properties by utilizing the transformation on the extracted internal control signals. In detail, to keep the appearance, we inflate both the pretrained image diffusion model and ControlNet to the temporal dimension and train low-rank adaptions (LORA) layers to fit the specific scenes. Then, in editing, we perform an inversion and editing framework. Differently, finetuned ControlNet is introduced in both inversion and generation for attention guidance with the proposed attention remix between the spatial attention maps of inversion and editing. Yet succinct, our method is the first method to show the ability of video property editing from the pre-trained text-to-image model. We present experiments on numerous examples within our unified framework. We also compare with shape-aware text-based editing and handcrafted motion video generation, demonstrating our superior temporal consistency and editing capability than previous works. The code and models are available on https://github.com/mayuelala/MagicStick.