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Found 27 papers, 4 papers with code
CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing
Large Language Models (LLMs) have revolutionized code generation ability by converting natural language descriptions into executable code.
CoSSegGaussians: Compact and Swift Scene Segmenting 3D Gaussians with Dual Feature Fusion
We propose Compact and Swift Segmenting 3D Gaussians(CoSSegGaussians), a method for compact 3D-consistent scene segmentation at fast rendering speed with only RGB images input.
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.
An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single Camera
This paper proposes a novel wide-range Pseudo-3D Vehicle Detection method based on images from a single camera and incorporates efficient learning methods.
PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer
We present PBFormer, an efficient yet powerful scene text detector that unifies the transformer with a novel text shape representation Polynomial Band (PB).
Head-Tail Cooperative Learning Network for Unbiased Scene Graph Generation
We also propose a self-supervised learning approach to enhance the prediction ability of the tail-prefer feature representation branch by constraining tail-prefer predicate features.
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks
Tabular data analysis is performed every day across various domains.
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints
In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view.
Ranked #6 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency
In domain adaption, we design an embedding representation with prediction consistency to ensure that the linear relationship between gaze directions in different domains remains consistent on gaze space and embedding space.
End-to-end Lane Shape Prediction with Transformers
To tackle these issues, we propose an end-to-end method that directly outputs parameters of a lane shape model, using a network built with a transformer to learn richer structures and context.
Ranked #20 on
Lane Detection
on TuSimple
Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation
In recognition-based action interaction, robots' responses to human actions are often pre-designed according to recognized categories and thus stiff.
Attention-Oriented Action Recognition for Real-Time Human-Robot Interaction
Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction.
Multi-Kernel Correntropy for Robust Learning
In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths.
Automatic Graphics Program Generation using Attention-Based Hierarchical Decoder
Recent progress on deep learning has made it possible to automatically transform the screenshot of Graphic User Interface (GUI) into code by using the encoder-decoder framework.
Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition
We propose a Consistency-aware Selective Fusion (CSF) to integrate the pairwise orders into a globally consistent order.
Color naming guided intrinsic image decomposition
In this paper we propose an efficient way of user interaction that users need only to annotate the color composition of the image.
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association
Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities.
Ranked #22 on
Text based Person Retrieval
on CUHK-PEDES
Topic-Guided Attention for Image Captioning
Attention mechanisms have attracted considerable interest in image captioning because of its powerful performance.
SymmNet: A Symmetric Convolutional Neural Network for Occlusion Detection
Detecting the occlusion from stereo images or video frames is important to many computer vision applications.
Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection
For real-world driver drowsiness detection from videos, the variation of head pose is so large that the existing methods on global face is not capable of extracting effective features, such as looking aside and lowering head.
Learning Fixation Point Strategy for Object Detection and Classification
The network can learn to extract a sequence of local observations with detailed appearance and rough context, instead of sliding windows or convolutions on the entire image.
Coordinating Multiple Disparity Proposals for Stereo Computation
While great progress has been made in stereo computation over the last decades, large textureless regions remain challenging.
Similarity Learning With Spatial Constraints for Person Re-Identification
We therefore learn a novel similarity function, which consists of multiple sub-similarity measurements with each taking in charge of a subregion.
Illumination Robust Color Naming via Label Propagation
In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically.
Saturation-Preserving Specular Reflection Separation
Specular reflection generally decreases the saturation of surface colors, which will be possibly confused with other colors that have the same hue but lower saturation.
Similarity Learning on an Explicit Polynomial Kernel Feature Map for Person Re-Identification
We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.
