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Found 23 papers, 9 papers with code
Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning
By modifying responses of unknown questions in the training data to refusal responses such as "I don't know", RAIT enhances the reliability of LLMs and reduces their hallucination.
CATP: Context-Aware Trajectory Prediction with Competition Symbiosis
To address this issue, we propose a ``manager-worker'' framework to unleash the full potential of contextual information and construct CATP model, an implementation of the framework for Context-Aware Trajectory Prediction.
Diffusion Actor-Critic with Entropy Regulator
This algorithm conceptualizes the reverse process of the diffusion model as a novel policy function and leverages the capability of the diffusion model to fit multimodal distributions, thereby enhancing the representational capacity of the policy.
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
In the burgeoning field of large language models (LLMs), the assessment of fundamental knowledge remains a critical challenge, particularly for models tailored to Chinese language and culture.
The Third Monocular Depth Estimation Challenge
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo
More specifically, we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals.
Ranked #2 on
3D Reconstruction
on DTU
VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
VHM is built on a large-scale remote sensing image-text dataset with rich-content captions (VersaD), and an honest instruction dataset comprising both factual and deceptive questions (HnstD).
Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations
We introduce CHARM, the first benchmark for comprehensively and in-depth evaluating the commonsense reasoning ability of large language models (LLMs) in Chinese, which covers both globally known and Chinese-specific commonsense.
Enhanced User Interaction in Operating Systems through Machine Learning Language Models
This iterative optimization process can continuously improve the quality and performance of the product to meet the changing needs of users.
Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models
We gain a comprehensive understanding of semantic search principles and acquire practical skills for implementing semantic search in real-world model application scenarios.
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models
With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems.
Boosting Multi-view Stereo with Late Cost Aggregation
To address this challenge, we present a late aggregation approach that allows for aggregating pairwise costs throughout the network feed-forward process, achieving accurate estimations with only minor changes of the plain CasMVSNet.
HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation Distillation
This ensures effective knowledge transfer while maintaining the student model's training flexibility.
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
In this paper, we propose a new PFL algorithm called \emph{FedDWA (Federated Learning with Dynamic Weight Adjustment)} to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients.
Detecting Building Changes with Off-Nadir Aerial Images
The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the semantic ambiguity of the building facades.
Building change detection for remote sensing images
Change Detection
Rethinking the Detection Head Configuration for Traffic Object Detection
The first is the detection head and object distribution matching strategy, which guides the rational configuration of detection head, so as to leverage multi-scale features to effectively detect objects at vastly different scales.
SimPer: Simple Self-Supervised Learning of Periodic Targets
From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes.
Uncertainty Set Prediction of Aggregated Wind Power Generation based on Bayesian LSTM and Spatio-Temporal Analysis
This paper focuses on the uncertainty set prediction of the aggregated generation of geographically distributed wind farms.
EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos
The popularity of racket sports (e. g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance.
Robust Protection of III-V Nanowires in Water Splitting by a Thin Compact TiO$_2$ Layer
Working as a photocathode for water splitting, they exhibited a 45% larger photocurrent density compared with un-protected counterparts and a high Faraday efficiency of 91%, and can also maintain a record-long highly-stable performance among narrow-bandgap III-V NW photoelectrodes; after 67 hours photoelectrochemical stability test reaction in strong acid electrolyte solution (pH = 1), they show no apparent indication of corrosion, which is in stark contrast to the un-protected NWs that are fully failed after 35-hours.
Mesoscale and Nanoscale Physics Chemical Physics
VisImages: A Fine-Grained Expert-Annotated Visualization Dataset
Images in visualization publications contain rich information, e. g., novel visualization designs and implicit design patterns of visualizations.
When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning
In this paper, we propose an actor-critic method - Attention-based Twin Delayed Deep Deterministic policy gradient (ATD3) algorithm to approximate a driver' s action according to observations and measure the driver' s attention allocation for consecutive time steps in car-following model.
Spatiotemporal Attention Networks for Wind Power Forecasting
This model captures spatial correlations among wind farms and temporal dependencies of wind power time series.
