Search Results for author:
Found 25 papers, 8 papers with code
SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition
In our previous work, we propose a novel method to realize seamless adaptation of foundation models to VPR (SelaVPR).
Rolling Ahead Diffusion for Traffic Scene Simulation
Recent developments in diffusion-based scenario generation focus on creating diverse and realistic traffic scenarios by jointly modelling the motion of all the agents in the scene.
Why and How: Knowledge-Guided Learning for Cross-Spectral Image Patch Matching
To the best of our knowledge, this is the first time that hard negative sample mining for metric networks has been implemented and brings significant performance gains.
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision
Specifically, inspired by organisms gradually adapting to their environment and continuously accumulating knowledge, we propose an innovative progressive active learning idea, which emphasizes that the network progressively and actively recognizes and learns more hard samples to achieve continuous performance enhancement.
See Further When Clear: Curriculum Consistency Model
Specifically, we regard the distillation process at each timestep as a curriculum and introduce a metric based on Peak Signal-to-Noise Ratio (PSNR) to quantify the learning complexity of this curriculum, then ensure that the curriculum maintains consistent learning complexity across different timesteps by having the teacher model iterate more steps when the noise intensity is low.
EDTformer: An Efficient Decoder Transformer for Visual Place Recognition
To obtain a global representation for each place image, most approaches typically focus on the aggregation of deep features extracted from a backbone through using current prominent architectures (e. g., CNNs, MLPs, pooling layer and transformer encoder), giving little attention to the transformer decoder.
Towards Fine-Grained Webpage Fingerprinting at Scale
In particular, Oscar combines proxy-based and sample-based metric learning losses to extract webpage features from obfuscated traffic and identify multiple webpages.
LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection
Specifically, to ensure the lightweight and robustness, on the one hand, we construct a lightweight feature extraction attention (LFEA) module, which can fully extract target features and strengthen information interaction across channels.
Refined Infrared Small Target Detection Scheme with Single-Point Supervision
In addition, to further improve the performance and explore the characteristics of this task, on the one hand, we construct and find that a multi-stage loss is helpful for fine-grained detection.
Infrared Small Target Detection based on Adjustable Sensitivity Strategy and Multi-Scale Fusion
Therefore, we propose a refined infrared small target detection scheme based on an adjustable sensitivity (AS) strategy and multi-scale fusion.
Progressive Domain Adaptation for Thermal Infrared Object Tracking
However, tracking methods trained on RGB datasets suffer a significant drop-off in TIR data due to the domain shift issue.
Multi-Scale Direction-Aware Network for Infrared Small Target Detection
Specifically, an innovative multi-directional feature awareness (MDFA) module is constructed, which fully utilizes the prior knowledge of targets and emphasizes the focus on high-frequency directional features.
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be easy to use, and easy to modify.
Semantically Consistent Video Inpainting with Conditional Diffusion Models
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information across frames.
Relational Representation Learning Network for Cross-Spectral Image Patch Matching
Therefore, we propose an innovative relational representation learning idea that simultaneously focuses on sufficiently mining the intrinsic features of individual image patches and the relations between image patch features.
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
Our solution, Layerwise Proximal Replay (LPR), balances learning from new and replay data while only allowing for gradual changes in the hidden activation of past data.
Nearest Neighbour Score Estimators for Diffusion Generative Models
Score function estimation is the cornerstone of both training and sampling from diffusion generative models.
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data.
Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.
OBBStacking: An Ensemble Method for Remote Sensing Object Detection
Trying to address these problems, this paper proposes OBBStacking, an ensemble method that is compatible with OBBs and combines the detection results in a learned fashion.
Vehicle Type Specific Waypoint Generation
We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior.
Conditional Permutation Invariant Flows
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density.
Critic Sequential Monte Carlo
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors.
