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Found 66 papers, 7 papers with code
Ice Hockey Puck Localization Using Contextual Cues
For evaluation, in addition to standard average precision, we propose Rink Space Localization Error (RSLE), a scale-invariant homography-based metric for removing perspective bias from rink space evaluation.
Gen4D: Synthesizing Humans and Scenes in the Wild
Lack of input data for in-the-wild activities often results in low performance across various computer vision tasks.
FoodTrack: Estimating Handheld Food Portions with Egocentric Video
Accurately tracking food consumption is crucial for nutrition and health monitoring.
6D Pose Estimation on Spoons and Hands
Accurate dietary monitoring is essential for promoting healthier eating habits.
Dietary Intake Estimation via Continuous 3D Reconstruction of Food
Monitoring dietary habits is crucial for preventing health risks associated with overeating and undereating, including obesity, diabetes, and cardiovascular diseases.
SAMJAM: Zero-Shot Video Scene Graph Generation for Egocentric Kitchen Videos
Then, we employ a matching algorithm to map each object in the scene graph with a SAM2-generated or SAM2-propagated mask, producing a temporally-consistent scene graph in dynamic environments.
Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms
Developing computer vision-based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production.
Deep Learning for Time Series Forecasting: A Survey
However, existing surveys have not provided a unified summary of the wide range of model architectures in this field, nor have they given detailed summaries of works in feature extraction and datasets.
Asymmetric Decision-Making in Online Knowledge Distillation:Unifying Consensus and Divergence
Building on these findings, we propose Asymmetric Decision-Making (ADM) to enhance feature consensus learning for student models while continuously promoting feature diversity in teacher models.
LEDiT: Your Length-Extrapolatable Diffusion Transformer without Positional Encoding
Diffusion transformers(DiTs) struggle to generate images at resolutions higher than their training resolutions.
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks
In Retrieval-Augmented Generation (RAG) and agent-based frameworks, the "Chain of Models" approach is widely used, where multiple specialized models work sequentially on distinct sub-tasks.
Semantic Communication with Entropy-and-Channel-Adaptive Rate Control over Multi-User MIMO Fading Channels
Although significant improvements in transmission efficiency have been achieved, existing semantic communication (SemCom) methods typically use a fixed transmission rate for varying channel conditions and transmission contents, leading to performance degradation under harsh channel conditions.
FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting
In the real world, objects reveal internal textures when sliced or cut, yet this behavior is not well-studied in 3D generation tasks today.
Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts
This method allows for the automatic understanding of hidden features and supports a broader range of analysis without the need to train specific vectors.
SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detection
Image-based Pose-Agnostic 3D Anomaly Detection is an important task that has emerged in industrial quality control.
MGSO: Monocular Real-time Photometric SLAM with Efficient 3D Gaussian Splatting
As a result, experiments show that our system generates reconstructions with a balance of quality, memory efficiency, and speed that outperforms the state-of-the-art.
MetaFood3D: 3D Food Dataset with Nutrition Values
To bridge the gap between general 3D vision and food computing research, we introduce MetaFood3D.
Towards Real-Time Gaussian Splatting: Accelerating 3DGS through Photometric SLAM
Initial applications of 3D Gaussian Splatting (3DGS) in Visual Simultaneous Localization and Mapping (VSLAM) demonstrate the generation of high-quality volumetric reconstructions from monocular video streams.
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Participants were tasked with reconstructing 3D models for 20 selected food items of varying difficulty levels: easy, medium, and hard.
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach
In contrast, traditional methods based on optical flow do not require training data, however, they often fail to capture object-level information, leading to over-segmentation or under-segmentation.
Understanding the Limitations of Diffusion Concept Algebra Through Food
Image generation techniques, particularly latent diffusion models, have exploded in popularity in recent years.
Comparative Analysis of Open-Source Language Models in Summarizing Medical Text Data
Unstructured text in medical notes and dialogues contains rich information.
Multi Player Tracking in Ice Hockey with Homographic Projections
Multi Object Tracking (MOT) in ice hockey pursues the combined task of localizing and associating players across a given sequence to maintain their identities.
Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types
Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data.
PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics
In the high-stakes world of baseball, every nuance of a pitcher's mechanics holds the key to maximizing performance and minimizing runs.
NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images
Many aging individuals encounter challenges in effectively tracking their dietary intake, exacerbating their susceptibility to nutrition-related health complications.
How Much You Ate? Food Portion Estimation on Spoons
Monitoring dietary intake is a crucial aspect of promoting healthy living.
In The Wild Ellipse Parameter Estimation for Circular Dining Plates and Bowls
Ellipse estimation is an important topic in food image processing because it can be leveraged to parameterize plates and bowls, which in turn can be used to estimate camera view angles and food portion sizes.
Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion
Detecting and segmenting moving objects from a moving monocular camera is challenging in the presence of unknown camera motion, diverse object motions and complex scene structures.
AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment
In this paper, we present AnimateZoo, a zero-shot diffusion-based video generator to address this challenging cross-species animation issue, aiming to accurately produce animal animations while preserving the background.
Domain-Guided Masked Autoencoders for Unique Player Identification
We further introduce a new spatio-temporal network leveraging our novel d-MAE for unique player identification.
Distribution and Depth-Aware Transformers for 3D Human Mesh Recovery
Precise Human Mesh Recovery (HMR) with in-the-wild data is a formidable challenge and is often hindered by depth ambiguities and reduced precision.
Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement
One of the main motivations of MTL is to develop neural networks capable of inferring multiple tasks simultaneously.
NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation
As the largest open-source synthetic food dataset, NV-Synth highlights the value of physics-based simulations for enabling scalable and controllable generation of diverse photorealistic meal images to overcome data limitations and drive advancements in automated dietary assessment using computer vision.
FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation
To address these limitations, we introduce FoodFusion, a Latent Diffusion model engineered specifically for the faithful synthesis of realistic food images from textual descriptions.
Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion
In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022.
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models
Extensive experiments demonstrate that our approach can address object duplication and heavy computation issues, achieving state-of-the-art performance on higher-resolution image synthesis tasks.
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks.
NutritionVerse-Real: An Open Access Manually Collected 2D Food Scene Dataset for Dietary Intake Estimation
Dietary intake estimation plays a crucial role in understanding the nutritional habits of individuals and populations, aiding in the prevention and management of diet-related health issues.
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training.
NAS-NeRF: Generative Neural Architecture Search for Neural Radiance Fields
Thus, there is a need to dynamically optimize the neural network component of NeRFs to achieve a balance between computational complexity and specific targets for synthesis quality.
NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches
Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images, but the lack of comprehensive datasets with diverse viewpoints, modalities and food annotations hinders the accuracy and realism of such methods.
Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos
To address these issues, we propose a robust keyframe identification module that extracts frames containing essential high-level information about the jersey number.
Mitigating Motion Blur for Robust 3D Baseball Player Pose Modeling for Pitch Analysis
The findings highlight the effectiveness of our method in mitigating the challenges posed by motion blur, thereby enhancing the overall quality of pose estimation.
The Model Inversion Eavesdropping Attack in Semantic Communication Systems
In recent years, semantic communication has been a popular research topic for its superiority in communication efficiency.
Transferring Knowledge for Food Image Segmentation using Transformers and Convolutions
To address these issues, two models are trained and compared, one based on convolutional neural networks and the other on Bidirectional Encoder representation for Image Transformers (BEiT).
Deep Joint Source-Channel Coding for Wireless Image Transmission with Entropy-Aware Adaptive Rate Control
Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources.
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge
As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments.
NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation
Unlike existing datasets, a collection of 3D models with nutritional information allow for view synthesis to create an infinite number of 2D images for any given viewpoint/camera angle along with the associated nutritional information.
NutritionVerse-Thin: An Optimized Strategy for Enabling Improved Rendering of 3D Thin Food Models
With the growth in capabilities of generative models, there has been growing interest in using photo-realistic renders of common 3D food items to improve downstream tasks such as food printing, nutrition prediction, or management of food wastage.
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping
To address these challenges, this paper proposes ShapeShift, a superquadric-based framework for object pose estimation that predicts the object's pose relative to a primitive shape which is fitted to the object.
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
ANL introduces the additional negative pseudo-label for all unlabeled data to leverage low-confidence examples.
MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy
We also demonstrate that our MC score is a more reliability indicator for outputs during inference time compared to the model generated confidence scores that are often over-confident.
MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis
Autonomous bin picking poses significant challenges to vision-driven robotic systems given the complexity of the problem, ranging from various sensor modalities, to highly entangled object layouts, to diverse item properties and gripper types.
Demo: low-power communications based on RIS and AI for 6G
By jointly designing the hardware and software, this prototype can realize real-time 4K video transmission with much reduced power consumption.
MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis
We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance in a manner that is more appropriate for robotic grasp applications compared to existing general-purpose performance metrics.
FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Framework for Heterogeneous Edge Devices
Our numerical results demonstrate that FTPipeHD is 6. 8x faster in training than the state of the art method when the computing capacity of the best device is 10x greater than the worst one.
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification
As a prevailing task in video surveillance and forensics field, person re-identification (re-ID) aims to match person images captured from non-overlapped cameras.
TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search
Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description.
Ranked #10 on
Text based Person Retrieval
on CUHK-PEDES
Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification
Due to the inaccurate person detections and pose changes, pedestrian misalignment significantly increases the difficulty of feature extraction and matching.
Quantization in Relative Gradient Angle Domain For Building Polygon Estimation
In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs.
Plant Stem Segmentation Using Fast Ground Truth Generation
Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting.
A Voice Interactive Multilingual Student Support System using IBM Watson
The chatbot could also be adapted for use in other application areas such as student info-centers, government kiosks, and mental health support systems.
Estimating Phenotypic Traits From UAV Based RGB Imagery
Use of imagery is becoming popular for phenotyping.
Locating Objects Without Bounding Boxes
In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects.
Ranked #1 on
Object Localization
on Mall
