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Found 23 papers, 19 papers with code
Optimizing LiDAR Placements for Robust Driving Perception in Adverse Conditions
The robustness of driving perception systems under unprecedented conditions is crucial for safety-critical usages.
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning
In addition, we unify the input representation of multi-modality into a 2D image format, enabling multi-modal anomaly detection and reasoning.
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection
In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling.
$\text{R}^2$-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the environment where intelligent systems perceive.
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e. g., industrial defect inspection, and medical lesion detection.
Generative Denoise Distillation: Simple Stochastic Noises Induce Efficient Knowledge Transfer for Dense Prediction
Knowledge distillation is the process of transferring knowledge from a more powerful large model (teacher) to a simpler counterpart (student).
Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network
During testing, the point cloud repeatedly goes through the Mask Reconstruction Network, with each iteration's output becoming the next input.
Towards Transferable Multi-modal Perception Representation Learning for Autonomy: NeRF-Supervised Masked AutoEncoder
This work proposes a unified self-supervised pre-training framework for transferable multi-modal perception representation learning via masked multi-modal reconstruction in Neural Radiance Field (NeRF), namely NeRF-Supervised Masked AutoEncoder (NS-MAE).
Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead
This study explores the use of GPT-4V(ision), a powerful visual-linguistic model, to address anomaly detection tasks in a generic manner.
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition
We propose a semantic decomposition method based on product quantization, where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics.
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Our dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can advance the development of panoramic segmentation/tracking.
HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation
For snippet-level learning, we introduce an online-updated memory to store reliable snippet prototypes for each class.
Ranked #1 on
Weakly Supervised Action Localization
on BEOID
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.
Segment Any Anomaly without Training via Hybrid Prompt Regularization
We present a novel framework, i. e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models.
Ranked #1 on
Anomaly Detection
on KSDD2
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection.
Ranked #1 on
Depth Anomaly Detection and Segmentation
on MVTEC 3D-AD
(using extra training data)
3D Anomaly Detection and Segmentation
Depth Anomaly Detection and Segmentation
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization
CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i. e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples.
Ranked #1 on
Anomaly Detection
on MVTEC 3D-AD
(using extra training data)
Robust Referring Video Object Segmentation with Cyclic Structural Consensus
Our model achieves state-of-the-art performance on R-VOS benchmarks, Ref-DAVIS17 and Ref-Youtube-VOS, and also our RRYTVOS dataset.
Optimization of Forcemyography Sensor Placement for Arm Movement Recognition
To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected.
Online Video Instance Segmentation via Robust Context Fusion
We propose a robust context fusion network to tackle VIS in an online fashion, which predicts instance segmentation frame-by-frame with a few preceding frames.
Towards Robust Referring Video Object Segmentation with Cyclic Relational Consensus
Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression.
Ranked #11 on
Referring Video Object Segmentation
on Refer-YouTube-VOS
Referring Expression Segmentation
Referring Video Object Segmentation
+2
Towards Robust Video Object Segmentation with Adaptive Object Calibration
We consolidate this conditional mask calibration process in a progressive manner, where the object representations and proto-masks evolve to be discriminative iteratively.
Ranked #1 on
Visual Object Tracking
on YouTube-VOS
Reliable Propagation-Correction Modulation for Video Object Segmentation
We introduce two modulators, propagation and correction modulators, to separately perform channel-wise re-calibration on the target frame embeddings according to local temporal correlations and reliable references respectively.
Ranked #3 on
Video Object Segmentation
on DAVIS 2017 (test-dev)
