Search Results for author:
Found 31 papers, 2 papers with code
Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints
This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication constraints.
Attribute-Aware Implicit Modality Alignment for Text Attribute Person Search
Text attribute person search aims to find specific pedestrians through given textual attributes, which is very meaningful in the scene of searching for designated pedestrians through witness descriptions.
Data Augmentation for Text-based Person Retrieval Using Large Language Models
LLM-DA uses LLMs to rewrite the text in the current TPR dataset, achieving high-quality expansion of the dataset concisely and efficiently.
On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks
We derive bounds on the advantage of an MIA adversary with the aim of offering insights into the impact of uncertainty and calibration on the effectiveness of MIAs.
A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges
This paper provides a comprehensive survey of VLP systems.
OFDM-Based Digital Semantic Communication with Importance Awareness
Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance.
Boundary-Aware Proposal Generation Method for Temporal Action Localization
More importantly, few works consider the background frames that are similar to action frames in pixels but dissimilar in semantics, which also leads to inaccurate temporal boundaries.
Learning From Noisy Correspondence With Tri-Partition for Cross-Modal Matching
Due to high labeling cost, it is inevitable to introduce a certain proportion of noisy correspondence into visual-text datasets, resulting in poor model robustness for cross-modal matching.
Cross-modal retrieval with noisy correspondence
Memorization
+3
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision.
Integrating Listwise Ranking into Pairwise-based Image-Text Retrieval
Given a query caption, the goal is to rank candidate images by relevance, from large to small.
Integrating Language Guidance Into Image-Text Matching for Correcting False Negatives
Extensive experiments on two ITM benchmarks show that our method can improve the performance of existing ITM models.
Cross-modal retrieval with noisy correspondence
Image-text matching
+2
Explainable Semantic Communication for Text Tasks
In this paper, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently.
Selectively Hard Negative Mining for Alleviating Gradient Vanishing in Image-Text Matching
To alleviate the gradient vanishing problem, we propose a Selectively Hard Negative Mining (SelHN) strategy, which chooses whether to mine hard negative samples according to the gradient vanishing condition.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Semantic Importance
Specifically, we first propose semantic importance weight calculation method, which is based on the gradient of intelligent task's perception results with respect to the features.
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference.
Image-Text Retrieval with Binary and Continuous Label Supervision
Such a binary indicator covers only a limited subset of image-text semantic relations, which is insufficient to represent relevance degrees between images and texts described by continuous labels such as image captions.
Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval
More specifically, Triplet loss with Hard Negative mining (Triplet-HN), which is widely used in existing retrieval models to improve the discriminative ability, is easy to fall into local minima in training.
Deep Joint Source-Channel Coding Based on Semantics of Pixels
The semantic information of the image for intelligent tasks is hidden behind the pixels, and slight changes in the pixels will affect the performance of intelligent tasks.
Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications
To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks.
Positioning Using Visible Light Communications: A Perspective Arcs Approach
In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for VLC-enabled indoor positioning, and a novel perspective arcs approach is proposed.
Adaptive Information Bottleneck Guided Joint Source and Channel Coding for Image Transmission
In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate.
Semantic-assisted image compression
Experimental results show that the proposed SAIC method can retain more semantic-level information and achieve better performance of downstream AI tasks compared to the traditional deep learning-based method and the advanced perceptual method at the same compression ratio.
Bandwidth and Power Allocation for Task-Oriented SemanticCommunication
The first subproblem is a compression ratio optimization problem with a given resource allocation scheme, which is solved by a enumeration algorithm.
Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction
JAL-MTP use a Social to Lane (S2L) module to jointly represent the static lane and the dynamic motion of the neighboring agents as instance-level lane, a Recurrent Lane Attention (RLA) mechanism for utilizing the instance-level lanes to predict the map-adaptive future trajectories and two selectors to identify the typical and reasonable trajectories.
Semantic Communications With AI Tasks
A radical paradigm shift of wireless networks from ``connected things'' to ``connected intelligence'' undergoes, which coincides with the Shanno and Weaver's envisions: Communications will transform from the technical level to the semantic level.
Learning Multiscale Correlations for Human Motion Prediction
We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3. 6M and CMU motion capture dataset.
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance.
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network
To support the ever increasing computational needs in such a vehicular network, the distributed virtual cloud network (VCN) is formed, based on which a computational resource sharing scheme through offloading among nearby vehicles is proposed.
Power Efficient LED Placement Algorithm for Indoor Visible Light Communication
Due to the interactions among LEDs and the illumination uniformity constraint, the formulated problem is complex and non-convex.
Exploiting Visual Semantic Reasoning for Video-Text Retrieval
Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity.
A Novel Received Signal Strength Assisted Perspective-three-Point Algorithm for Indoor Visible Light Positioning
The basic idea of R-P3P is to joint visual and strength information to estimate the receiver position using 3 LEDs regardless of the LEDs' orientations.
