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Found 27 papers, 9 papers with code
Insight Gained from Migrating a Machine Learning Model to Intelligence Processing Units
The discoveries in this paper show that Intelligence Processing Units (IPUs) offer a viable accelerator alternative to GPUs for machine learning (ML) applications within the fields of materials science and battery research.
Enabling Uncertainty Estimation in Iterative Neural Networks
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance.
Zero-Shot Object Counting with Language-Vision Models
Thus, we propose zero-shot object counting (ZSC), a new setting where only the class name is available during test time.
Grasp-Anything: Large-scale Grasp Dataset from Foundation Models
Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains.
Enforcing Topological Interaction between Implicit Surfaces via Uniform Sampling
Further, we show that our proposed method can be used to simulate various ways a hand can interact with an arbitrary object.
Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting
In this paper, we point out that the task of counting objects of interest when there are multiple object classes in the image (namely, multi-class object counting) is particularly challenging for current object counting models.
Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Data
Our model achieves a compression ratio of 140 on several benchmark data sets without compromising the reconstruction quality.
Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection
To mitigate this issue, we propose a novel variational autoencoder (VAE) based data generation model, which is capable of generating data with increased crop-related diversity.
Zero-shot Object Counting
By applying this model to all the candidate patches, we can select the most suitable patches as exemplars for counting.
Ranked #4 on
Zero-Shot Counting
on FSC147
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference
Sampling-free approaches can be faster but suffer from other drawbacks, such as lower reliability of uncertainty estimates, difficulty of use, and limited applicability to different types of tasks and data.
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification
The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head.
Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach
This paper focuses on a referring expression generation (REG) task in which the aim is to pick out an object in a complex visual scene.
Generating Representative Samples for Few-Shot Classification
To mitigate this issue, we propose to generate visual samples based on semantic embeddings using a conditional variational autoencoder (CVAE) model.
AutoFR: Automated Filter Rule Generation for Adblocking
AutoFR is effective: it generates filter rules that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage.
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness
Although deep neural networks have achieved great performance on classification tasks, recent studies showed that well trained networks can be fooled by adding subtle noises.
Temporal Feature Warping for Video Shadow Detection
The current video shadow detection method achieves this goal via co-attention, which mostly exploits information that is temporally coherent but is not robust in detecting moving shadows and small shadow regions.
Physics-based Shadow Image Decomposition for Shadow Removal
Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer.
Ranked #2 on
Shadow Removal
on Adjusted ISTD
Variational Feature Disentangling for Fine-Grained Few-Shot Classification
We assume that the distribution of intra-class variance generalizes across the base class and the novel class.
Ranked #14 on
Few-Shot Image Classification
on CUB 200 5-way 5-shot
From Shadow Segmentation to Shadow Removal
Our method achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images.
Ranked #4 on
Shadow Removal
on Adjusted ISTD
Shadow Removal via Shadow Image Decomposition
Training our model on this new augmented ISTD dataset further lowers the RMSE on the shadow area to 7. 4.
Ranked #3 on
Shadow Removal
on Adjusted ISTD
Weakly Labeling the Antarctic: The Penguin Colony Case
This segmentation network is trained with a specific loss function, based on the average activation, to effectively learn from the data with the weakly-annotated labels.
Iterative Crowd Counting
In this work, we tackle the problem of crowd counting in images.
Ranked #10 on
Crowd Counting
on UCF CC 50
A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation
The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net's shadow predictions.
Ranked #4 on
Shadow Detection
on SBU
Geodesic Distance Histogram Feature for Video Segmentation
This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms.
Ranked #1 on
Video Segmentation
on SegTrack v2
Co-localization with Category-Consistent Features and Geodesic Distance Propagation
Co-localization is the problem of localizing objects of the same class using only the set of images that contain them.
Ranked #1 on
Object Localization
on PASCAL VOC 2012
Efficient Video Segmentation Using Parametric Graph Partitioning
Video segmentation is the task of grouping similar pixels in the spatio-temporal domain, and has become an important preprocessing step for subsequent video analysis.
