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Found 30 papers, 19 papers with code
OPPO’s Machine Translation Systems for WMT20
In this paper we demonstrate our (OPPO’s) machine translation systems for the WMT20 Shared Task on News Translation for all the 22 language pairs.
The Mininglamp Machine Translation System for WMT21
This paper describes Mininglamp neural machine translation systems of the WMT2021 news translation tasks.
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
The development of control policies for multi-robot systems traditionally follows a complex and labor-intensive process, often lacking the flexibility to adapt to dynamic tasks.
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation
Large foundation models trained on large-scale visual-text data can significantly enhance Open Vocabulary Object Detection (OVD) through data generation.
YOLOMG: Vision-based Drone-to-Drone Detection with Appearance and Pixel-Level Motion Fusion
Next, this motion difference map is combined with an RGB image using a bimodal fusion module, allowing for adaptive feature learning of the drone.
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
Visual prompting has gained popularity as a method for adapting pre-trained models to specific tasks, particularly in the realm of parameter-efficient tuning.
MLLM-as-a-Judge for Image Safety without Human Labeling
To address these challenges, we propose a MLLM-based method includes objectifying safety rules, assessing the relevance between rules and images, making quick judgments based on debiased token probabilities with logically complete yet simplified precondition chains for safety rules, and conducting more in-depth reasoning with cascaded chain-of-thought processes if necessary.
Token-Budget-Aware LLM Reasoning
Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks.
Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction
For Scenario II, based on the reduction strategy from G-Search, we design a parametric sigmoid function (P-Sigmoid) to guide the reduction at each layer of the MLLM, whose parameters are optimized by Bayesian Optimization.
MBA-SLAM: Motion Blur Aware Dense Visual SLAM with Radiance Fields Representation
In our experiments, we demonstrate that MBA-SLAM surpasses previous state-of-the-art methods in both camera localization and map reconstruction, showcasing superior performance across a range of datasets, including synthetic and real datasets featuring sharp images as well as those affected by motion blur, highlighting the versatility and robustness of our approach.
Motion-guided small MAV detection in complex and non-planar scenes
Our proposed method can effectively and efficiently detect extremely small MAVs from dynamic and complex backgrounds because it aggregates pixel-level motion features and eliminates false positives based on the motion and appearance features of MAVs.
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
We aim to evaluate Large Language Models (LLMs) for embodied decision making.
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Existing automatic prompt engineering algorithms primarily focus on language modeling and classification tasks, leaving the domain of IR, particularly reranking, underexplored.
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations.
STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
To address the gap, we develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Relational Knowledge Bases.
AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
This necessitates an expensive process of continuously curating and annotating data with significant human effort.
Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network
A new benchmark for cross-domain MAV detection is proposed based on the proposed dataset.
Generating Enhanced Negatives for Training Language-Based Object Detectors
The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations.
Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion
In this paper, we propose a global-local MAV detector that can fuse both motion and appearance features for MAV detection under challenging conditions.
Multi-Agent Consensus Seeking via Large Language Models
To that end, this work studies a consensus-seeking task where the state of each agent is a numerical value and they negotiate with each other to reach a consensus value.
Predator-prey survival pressure is sufficient to evolve swarming behaviors
The comprehension of how local interactions arise in global collective behavior is of utmost importance in both biological and physical research.
Taming Self-Training for Open-Vocabulary Object Detection
This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs.
OmniLabel: A Challenging Benchmark for Language-Based Object Detection
With more than 28K unique object descriptions on over 25K images, OmniLabel provides a challenging benchmark with diverse and complex object descriptions in a naturally open-vocabulary setting.
Characterizing bearing equivalence in directed graphs
These conditions involve the spectrum and null space of the associated bearing Laplacian matrix for a directed bearing formation.
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.
Exploiting Unlabeled Data with Vision and Language Models for Object Detection
We propose a novel method that leverages the rich semantics available in recent vision and language models to localize and classify objects in unlabeled images, effectively generating pseudo labels for object detection.
Ranked #20 on
Open Vocabulary Object Detection
on MSCOCO
Global Matching with Overlapping Attention for Optical Flow Estimation
In this paper, inspired by the traditional matching-optimization methods where matching is introduced to handle large displacements before energy-based optimizations, we introduce a simple but effective global matching step before the direct regression and develop a learning-based matching-optimization framework, namely GMFlowNet.
Ranked #5 on
Optical Flow Estimation
on KITTI 2015
STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactions
Results: In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (sub-structures) and their DDI types are determined by the linkages between different substructure sets, we design a novel Substructure-ware Tensor Neural Network model for DDI prediction (STNN-DDI).
Deep Animation Video Interpolation in the Wild
In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming.
Time Derivative of Rotation Matrices: A Tutorial
The time derivative of a rotation matrix equals the product of a skew-symmetric matrix and the rotation matrix itself.
Robotics
