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Found 15 papers, 2 papers with code
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challenging due to this complexity and the limited availability of annotated data.
A Range-Free Node Localization Method for Anisotropic Wireless Sensor Networks with Sparse Anchors
To address this, we introduce an adaptive weighted method, termed AW-MinMax, for range-free node localization.
Exponentially Consistent Statistical Classification of Continuous Sequences with Distribution Uncertainty
Subsequently, we generalize our results to a more general case with the null hypothesis by allowing the testing sequence to be generated from a distribution that is vastly different from the generating distributions of all training sequences.
A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks
In the ride-hailing industry, subsidies are predominantly employed to incentivize consumers to place more orders, thereby fostering market growth.
Collaborative Secret and Covert Communications for Multi-User Multi-Antenna Uplink UAV Systems: Design and Optimization
To characterize the theoretical benchmark of the above model, we formulate a weighted multi-objective optimization problem to maximize the average of secret and covert transmission rates subject to constraints SOP, DEP, the beamformers of Bob and Carlo, and UAV trajectory parameters.
Exponentially Consistent Outlier Hypothesis Testing for Continuous Sequences
In outlier hypothesis testing, one aims to detect outlying sequences among a given set of sequences, where most sequences are generated i. i. d.
From OTFS to DD-ISAC: Integrating Sensing and Communications in the Delay Doppler Domain
The recently proposed Orthogonal Time Frequency Space (OTFS) modulation, which exploits various advantages of Delay Doppler (DD) channels, has been shown to support reliable communication in high-mobility scenarios.
Efficient Image Super-Resolution using Vast-Receptive-Field Attention
In this work, we design an efficient SR network by improving the attention mechanism.
Resolution Limits of Non-Adaptive 20 Questions Search for a Moving Target
Our task is to query the oracle as few times as possible to accurately estimate the location of the target at any specified time.
Clip-aware expressive feature learning for video-based facial expression recognition
In this paper, we divide a video into several short clips for processing and propose a clip-aware emotion-rich feature learning network (CEFLNet) for robust video-based FER.
Ranked #21 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
Chow groups of Gushel-Mukai 5-folds
We compute the Chow groups of smooth Gushel-Mukai varieties of dimension $5$
Algebraic Geometry
Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates
In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via using simpler object representation model and less regression parameters.
Ranked #15 on
Oriented Object Detection
on DOTA 1.0
Objects detection for remote sensing images based on polar coordinates
In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via uses simpler object representation model and less regression parameters.
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level
Scenario-based question answering (SQA) has attracted increasing research attention.
Second-Order Asymptotically Optimal Statistical Classification
Motivated by real-world machine learning applications, we analyze approximations to the non-asymptotic fundamental limits of statistical classification.
