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Works55 from public data
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- Multi-intent autonomous decision-making for air combat with deep reinforcement learning21
The Temporal Proximal Policy Optimization (T-PPO) algorithm is proposed, which optimizes the ProXimal Policyoptimization algorithm by integrating the long short-term memory network and feedforward neural network and extracts the historical temporal information to enhance situational awareness.
- Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method20
The BSCV is a collection of a proposed three-parameter corruption model for video bitstream, a large-scale dataset containing rich error patterns, multiple corruption levels, and flexible dataset branches, and a plug-and-play module in video recovery framework that serves as a benchmark.
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- Block-wise focal stack image representation for end-to-end applications15
A block-wise Gaussian based representation model for FoSIs is proposed and utilize this model to solve the problem of coding, reconstruction and rendering for end-to-end applications.
- End-to-end varifocal multiview images coding framework from data acquisition end to vision application end14
An end-to-end coding scheme for V FMV images is proposed, which provides a new paradigm for VFMV compression from data acquisition (source) end to vision application end, and extensive experiments demonstrate that the proposed coding scheme is superior to the comparison scheme in objective quality, subjective quality and computational complexity.
- A Byte Sequence is Worth an Image: CNN for File Fragment Classification Using Bit Shift and n-Gram Embeddings13
This work proposes Byte2Image, a novel data augmentation technique, to introduce the neglected intra-byte information into file fragments and re-treat them as 2d gray-scale images, which allows us to capture both inter-byte and intra- byte correlations simultaneously through powerful convolutional neural networks (CNNs).
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- PromptSR: Cascade Prompting for Lightweight Image Super-Resolution10
The experimental results demonstrate the superiority of the PromptSR method, which outperforms state-of-the-art lightweight SR methods in quantitative, qualitative, and complexity evaluations.
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- ASIFusion: An Adaptive Saliency Injection-Based Infrared and Visible Image Fusion Network9
This work presents ASIFusion, an adaptive saliency injection-based IVIF network that outperforms state-of-the-art (SOTA) approaches with regard to fusion performance and computational efficiency.
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- Multifocal multiview imaging and data compression based on angular–focal–spatial representation8
To efficiently compress MFMV data, the first, to the authors' knowledge, MFMV data compression scheme based on angular-focal-spatial representation is proposed, which exploits inter-view, inter-stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively.
- Multi-Scale Dynamic Fusion for Visible-Infrared Person Re-Identification7
A Multi-scale Dynamic Fusion Network (MDFN) to address challenges in the VI-ReID task, which outperforms other state-of-the-art methods, especially in complex dynamic scenes with occlusions, background shifts, and pose changes.
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- Building Egocentric Procedural AI Assistant: Methods, Benchmarks, and Challenges6
This paper identifies three core tasks in EgoProceAssist: egocentric procedural error detection, egocentric procedural learning, and egocentric procedural question answering, and introduces two enabling dimensions: real-time and streaming video understanding, and proactive interaction in procedural contexts.
- Towards Blind Bitstream-corrupted Video Recovery: A Visual Foundation Model-driven Framework6
This paper proposes the first blind bitstream-corrupted video recovery framework that integrates visual foundation models with recovery model, which is adapted to different types of corruption and bitstream-level prompts and introduces a novel Corruption-aware Feature Completion (CFC) module.
- Las-yolo: a lightweight detection method based on YOLOv7 for small objects in airport surveillance6
A lightweight airport surveillance detection based on YOLOv7 named LAS-YOLO is proposed, which significantly reduces network parameters while retaining certain local features and the attention mechanism and small object detection layer are introduced to enhance small object detection accuracy.
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- Rapid detection and social media supervision of runway incursion based on deep learning5
The experimental result shows that the airport target detection based on optimised YOLO has excellent fastness and accuracy.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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