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Found 47 papers, 33 papers with code
基于图文细粒度对齐语义引导的多模态神经机器翻译方法(Based on Semantic Guidance of Fine-grained Alignment of Image-Text for Multi-modal Neural Machine Translation)
“多模态神经机器翻译旨在利用视觉信息来提高文本翻译质量。传统多模态机器翻译将图像的全局语义信息融入到翻译模型, 而忽略了图像的细粒度信息对翻译质量的影响。对此, 该文提出一种基于图文细粒度对齐语义引导的多模态神经机器翻译方法, 该方法首先跨模态交互图文信息, 以提取图文细粒度对齐语义信息, 然后以图文细粒度对齐语义信息为枢纽, 采用门控机制将多模态细粒度信息对齐到文本信息上, 实现图文多模态特征融合。在多模态机器翻译基准数据集Multi30K 英语→德语、英语→法语以及英语→捷克语翻译任务上的实验结果表明, 论文提出方法的有效性, 并且优于大多数最先进的多模态机器翻译方法。”
Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation
This paper proposes a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT.
Unsupervised Learning for Joint Beamforming Design in RIS-aided ISAC Systems
It is critical to design efficient beamforming in reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems for enhancing spectrum utilization.
PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling
First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist.
CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models
Adversarial misuse, particularly through `jailbreaking' that circumvents a model's safety and ethical protocols, poses a significant challenge for Large Language Models (LLMs).
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
To overcome the limitations of existing data augmentation methods that compromise semantic integrity and address the uncertainty inherent in LLM-generated text, we leverage the distinctive characteristics of the NER task by augmenting the original data at both the contextual and entity levels.
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
HAG extends the current paradigm in the text generation process, highlighting the feasibility of endowing the LLMs with self-regulate decoding strategies.
ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios.
MouSi: Poly-Visual-Expert Vision-Language Models
This technique introduces a fusion network to unify the processing of outputs from different visual experts, while bridging the gap between image encoders and pre-trained LLMs.
Ranked #42 on
Visual Question Answering
on MM-Vet
Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback
This reliance limits the applicability of RLHF and hinders the development of professional assistants tailored to diverse human preferences.
RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning
To bridge this gap, we introduce RoTBench, a multi-level benchmark for evaluating the robustness of LLMs in tool learning.
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios
Furthermore, a sole emphasis on outcomes disregards the intricate capabilities essential for LLMs to effectively utilize tools.
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments
In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher uncertainty.
Energy Efficiency Optimization in Active Reconfigurable Intelligent Surface-Aided Integrated Sensing and Communication Systems
Energy efficiency (EE) is a challenging task in integrated sensing and communication (ISAC) systems, where high spectral efficiency and low energy consumption appear as conflicting requirements.
A Language Agent for Autonomous Driving
Our approach, termed Agent-Driver, transforms the traditional autonomous driving pipeline by introducing a versatile tool library accessible via function calls, a cognitive memory of common sense and experiential knowledge for decision-making, and a reasoning engine capable of chain-of-thought reasoning, task planning, motion planning, and self-reflection.
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars.
GPT-Driver: Learning to Drive with GPT
In this paper, we propose a novel approach to motion planning that capitalizes on the strong reasoning capabilities and generalization potential inherent to Large Language Models (LLMs).
Ranked #1 on
Motion Planning
on nuScenes
Bio-Inspired Simple Neural Network for Low-Light Image Restoration: A Minimalist Approach
In this study, we explore the potential of using a straightforward neural network inspired by the retina model to efficiently restore low-light images.
InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
Large language models have unlocked strong multi-task capabilities from reading instructive prompts.
Ranked #2 on
Zero-shot Named Entity Recognition (NER)
on CrossNER
(using extra training data)
Tracker Meets Night: A Transformer Enhancer for UAV Tracking
To realize reliable UAV tracking at night, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches.
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.
Out-of-distribution Detection with Implicit Outlier Transformation
It leads to a min-max learning scheme -- searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for uniform performance in OOD detection.
Ranked #12 on
Out-of-Distribution Detection
on ImageNet-1k vs Textures
SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking
The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation and effectively discriminate foreground and background information.
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation
Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask.
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
The GPT-3. 5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities.
Natural Language Inference
Natural Language Understanding
+1
Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention
The key to the visual UAM approaching lies in object tracking, while current UAM tracking typically relies on costly model-based methods.
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
Visual object tracking is essential to intelligent robots.
Causal Intervention Improves Implicit Sentiment Analysis
In this work, we propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV).
HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking
Pixel-level range mask is introduced to make HighlightNet more focused on the enhancement of the tracking object and regions without light sources.
Local Perception-Aware Transformer for Aerial Tracking
Specifically, with local-modeling to global-search mechanism, the proposed tracker replaces the global encoder by a novel local-recognition encoder.
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i. e., minimizing the dependency between inputs (i. e., features) and outputs (i. e., labels) during training the classifier.
Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis
Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation systems because of its versatility and effectiveness.
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications.
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking
Therefore, to help increase awareness of the potential risk and the robustness of UAV tracking, this work proposes a novel adaptive adversarial attack approach, i. e., Ad$^2$Attack, against UAV object tracking.
OVD-Explorer: A General Information-theoretic Exploration Approach for Reinforcement Learning
Many exploration strategies are built upon the optimism in the face of the uncertainty (OFU) principle for reinforcement learning.
ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones
To this concern, this work proposes a real-time augmented reality virtual shoe try-on system for smartphones, namely ARShoe.
HiFT: Hierarchical Feature Transformer for Aerial Tracking
Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps.
DarkLighter: Light Up the Darkness for UAV Tracking
Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems.
SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking
By virtue of the attention mechanism, we conduct a special attentional aggregation network (AAN) consisting of self-AAN and cross-AAN for raising the representation ability of features eventually.
Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label
However, prevalent discriminative correlation filter (DCF) based trackers are insensitive to target mutations due to a predefined label, which concentrates on merely the centre of the training region.
ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking
The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking.
Predictive Visual Tracking: A New Benchmark and Baseline Approach
As a crucial robotic perception capability, visual tracking has been intensively studied recently.
All-Day Object Tracking for Unmanned Aerial Vehicle
However, prior tracking methods have merely focused on robust tracking in the well-illuminated scenes, while ignoring trackers' capabilities to be deployed in the dark.
Noise against noise: stochastic label noise helps combat inherent label noise
The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect, previously studied in optimization by analyzing the dynamics of parameter updates.
Siamese Anchor Proposal Network for High-Speed Aerial Tracking
In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency.
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption. The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN).
Ranked #45 on
Image Classification
on Clothing1M
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping.
