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Found 27 papers, 14 papers with code
OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge
This article describes the OPDAI submission to SemEval-2022 Task 11 on Chinese complex NER.
Observation, Analysis, and Solution: Exploring Strong Lightweight Vision Transformers via Masked Image Modeling Pre-Training
In this paper, we question if the extremely simple ViTs' fine-tuning performance with a small-scale architecture can also benefit from this pre-training paradigm, which is considerably less studied yet in contrast to the well-established lightweight architecture design methodology with sophisticated components introduced.
BEV2PR: BEV-Enhanced Visual Place Recognition with Structural Cues
To tackle the above issues, we design a new BEV-enhanced VPR framework, nemely BEV2PR, which can generate a composite descriptor with both visual cues and spatial awareness solely based on a single camera.
Multi-Generative Agent Collective Decision-Making in Urban Planning: A Case Study for Kendall Square Renovation
In this study, we develop a multiple-generative agent system to simulate community decision-making for the redevelopment of Kendall Square's Volpe building.
Data-Centric Foundation Models in Computational Healthcare: A Survey
The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare.
Multi-Correlation Siamese Transformer Network with Dense Connection for 3D Single Object Tracking
Instead of performing correlation of the two branches at just one point in the network, in this paper, we present a multi-correlation Siamese Transformer network that has multiple stages and carries out feature correlation at the end of each stage based on sparse pillars.
Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video
This is achieved by leveraging the object-level 3D-aware image diffusion model as the primary supervision signal for training Dynamic Neural Radiance Fields (DyNeRF).
AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning
By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs and provides a robust platform for automating complex real-world urban planning processes.
ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking
To this end, we non-uniformly resize the cropped image to have a smaller input size while the resolution of the area where the target is more likely to appear is higher and vice versa.
Text-guided Foundation Model Adaptation for Pathological Image Classification
In this paper, we propose to Connect Image and Text Embeddings (CITE) to enhance pathological image classification.
Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques
In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl. github. io/}.
Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption
We update the target data instead, and project all test inputs toward the source domain with a generative diffusion model.
Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing
Face anti-spoofing researches are widely used in face recognition and has received more attention from industry and academics.
Image Quality Assessment with Gradient Siamese Network
In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment.
A Semantic Alignment System for Multilingual Query-Product Retrieval
Our models are all trained with cross-entropy loss to classify the query-product pairs into ESCI 4 categories at first, and then we use weighted sum with the 4-class probabilities to get the score for ranking.
DSPNet: Towards Slimmable Pretrained Networks based on Discriminative Self-supervised Learning
However, when facing various resource budgets in real-world applications, it costs a huge computation burden to pretrain multiple networks of various sizes one by one.
Back to the Source: Diffusion-Driven Test-Time Adaptation
We instead update the target data, by projecting all test inputs toward the source domain with a generative diffusion model.
PolarFormer: Multi-camera 3D Object Detection with Polar Transformer
3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3D world.
Ranked #2 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Narrowing the Gap: Improved Detector Training with Noisy Location Annotations
Experiments for both synthesized and real-world scenarios consistently demonstrate the effectiveness of our approach, e. g., our method increases the degraded performance of the FCOS detector from 33. 6% AP to 35. 6% AP on COCO.
A Closer Look at Self-Supervised Lightweight Vision Transformers
We also point out some defects of such pre-training, e. g., failing to benefit from large-scale pre-training data and showing inferior performance on data-insufficient downstream tasks.
Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation
Open-vocabulary object detection aims to detect novel object categories beyond the training set.
Ranked #26 on
Open Vocabulary Object Detection
on MSCOCO
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
In this paper, we propose a simple yet effective recursive least-squares estimator-aided online learning approach for few-shot online adaptation without requiring offline training.
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
In this paper, we show the existence of universal perturbations that can enable the targeted attack, e. g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network.
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process.
Ranked #3 on
Robotic Grasping
on GraspNet-1Billion
Visual Tracking via Spatially Aligned Correlation Filters Network
Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background.
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online.
Ranked #3 on
Visual Object Tracking
on OTB-2013
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.
