In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by up to 2.3-3.8% and Average Recall by up to 5.3-7.2% when compared against de-facto standard and state of the art NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes.

pdf/confluence-a-robust-non-iou-alternative-to-non-maxima-326b8swmvt.pdf

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection.

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by 0.2-2.7% and 1-3.8% respectively and Average Recall by 1.3-9.3 and 2.4-7.3% when compared against Greedy and Soft-NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes. 

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Region NMS-based deep network for gigapixel level pedestrian detection with two-step cropping

Video surveillance system is set up and 21, being calibrated 22, tasking and 23 is operated (24). The system extracts an event from the video primitives using an extracting video primitives and 42, the event determination (44). The system may based on the extracted event performing a response (45).

Video surveillance system employing video primitives

Three-dimensional (3-D) ultrasound provides physicians with volume rendering of patient ultrasound data. This medical ultrasound technique carries important clinical value in image-guided surgery. However, 3-D ultrasound is not widely used because obtaining 3-D ultrasound images is difficult and costly, and the technology used is incapable of scanning large-volume organs. This paper proposes a novel method for 3-D freehand ultrasound reconstruction, which can accurately restore 3-D volumes from 2-D B-scans through cube propagation. The 3-D multi-resolution method creates a 3-D volume pyramid and a 3-D texture pyramid to preserve the structural and textural details of the imaged volume. Computation efficiency is improved with the 3-D approximate nearest neighbors (ANN) search technique, which is proposed to accelerate the nearest-neighbor search through the whole 3-D ultrasound volume. Texture information in the texture pyramid is added to the cube distance in the nearest-neighbors searching step to identify suitable nearest neighbors. Experiments performed on clinical and synthetic datasets demonstrate that the proposed algorithm is robust and can achieve quality results in a short execution time relative to current reconstruction techniques.

Multiresolution Cube Propagation for 3-D Ultrasound Image Reconstruction

This study proposes a novel inference adaptive thresholding based non-maximum suppression (NMS) (IAT-NMS) algorithm for deriving temporal cues between video sequences. The inference of temporal connectivity is first derived according to an overlapping measure of the bounding boxes between adjacent frames. Frames with high-confidence detection object are taken as key frames to leverage the scores of neighbor detections and preserve potential detections of blurred objects with low scores. Then, bounding boxes within each frame are ranked via their confidence scores and the overlapping ratio between the bounding box with the highest score against the remaining surrounding boxes is computed. This measure of overlapping is brought into a Gaussian function to estimate weights for adaptive suppression and to softly suppress the detection scores of possible severely overlapped objects. The proposed method is compared with state-of-the-art video object detection techniques. With the application of IAT-NMS, overlapping objects originally undistinguishable in the compared methods become detectable. Experimental results demonstrate that this simple and unsupervised method outperforms state-of-the-art NMS algorithms, with an increase of 6% in mean average precision (mAP) on the ImageNet VID dataset. Our study on performance limitations and sensitivity to parametric variations also finds that IAT-NMS demonstrates better detection capability than does the three compared algorithms, which fail to detect all targets or distinguish in the presence of multiple overlapping targets.

Inference Adaptive Thresholding based Non-Maximum Suppression for Object Detection in Video Image Sequence

Vision Transformers (ViTs) are promising for solving video-related tasks, but often suffer from computational bottlenecks or insufficient temporal information. Recent advances in large-scale pre-training show great potential for high-quality video representation, providing new remedies to transformer limitations. Inspired by this, we propose a SpatioTemporal Representation Enhanced Vision Transformer (STRE-ViT), which follows a two-stream paradigm to fuse large-scale pre-training visual prior knowledge and video-level temporal biases in a simple and effective manner. Specifically, one stream employs a well-pretrained ViT with rich vision priors to alleviate data requirements and learning workload. Another stream is our designed spatiotemporal interaction stream, which first models video-level temporal dynamics and then extracts fine-grained and salient spatiotemporal representations by introducing appropriate temporal bias. Through this interaction stream, the model capacity of ViT is enhanced for video spatiotemporal representations. Moreover, we provide a fresh perspective to adapt well-pretrained ViT for video recognition. Experimental results show that STRE-ViT learns high-quality video representations and achieves competitive performance on two popular video benchmarks. 

Spatiotemporal Representation Enhanced ViT for Video Recognition

Constructions of almost complementary pairs based on non-standard Golay sequences

Human-object interaction (HOI) detection has witnessed remarkable advancements in recent years, primarily driven by deep learning networks and the availability of large-scale HOI datasets. However, there are still scenarios where HOI detection lacks a suitable training database. The process of expanding existing datasets by manually annotating more images can be challenging because it is time-consuming and labor-intensive. In response to this challenge, domain adaptation has emerged as a promising approach to address the scarcity of annotated data. Drawing inspiration from this, we propose an innovative unsupervised adaptive approach for HOI detection called UDA-HOID. Our method aims to adapt HOI detection from a label-rich source domain to a label-poor target domain, thereby reducing annotation costs and enhancing detection performance. Specifically, UDA-HOID leverages attention-based full alignment for low-level features, which tackles domain shifts caused by image style, illumination, and other factors. Additionally, semantic-based weak alignment is applied to high-level features, as domain shifts in these features contain more semantic information. Adversarial learning techniques are employed to facilitate this alignment process. To evaluate the effectiveness of our proposed method, we conduct experiments on the test data of the low-light HOI image set (LLHOI). The results demonstrate that our approach achieves a relative improvement of 9.8 percent in the mean average precision of roles compared to existing methods.

UDA-HOID: Unsupervised Domain Adaptation for Human-Object Interaction Detection

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Min Li

Author Tools

Chat about Author