Search Results for author:
Found 21 papers, 14 papers with code
MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models
Existing visual alignment methods, primarily designed for single-image scenarios, struggle to effectively handle the complexity of multi-image tasks due to the scarcity of diverse training data and the high cost of annotating chosen/rejected pairs.
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
Finally, we design the Query-Decoupled Modality Decoder (QDMD) that leverages a one-to-one strategy to provide an independent decoding channel for every modality.
Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification
Self-supervised contrastive learning has become a key technique in deep learning, particularly in time series analysis, due to its ability to learn meaningful representations without explicit supervision.
MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
Moreover, 33. 2% of the questions are cross-page questions requiring evidence across multiple pages.
MMDU: A Multi-Turn Multi-Image Dialog Understanding Benchmark and Instruction-Tuning Dataset for LVLMs
Generating natural and meaningful responses to communicate with multi-modal human inputs is a fundamental capability of Large Vision-Language Models(LVLMs).
Ranked #92 on
Visual Question Answering
on MM-Vet
V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results
2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects.
TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models
We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results.
TBNet: A Neural Architectural Defense Framework Facilitating DNN Model Protection in Trusted Execution Environments
Trusted Execution Environments (TEEs) have become a promising solution to secure DNN models on edge devices.
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
Notably, our approach demonstrates a significant improvement in performance on 5 fine-grained visual recognition benchmarks, 11 few-shot image recognition datasets, and the 2 object detection datasets under the zero-shot recognition setting.
Self-Supervised Learning for Time Series: Contrastive or Generative?
In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series.
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation
Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module.
Motley: Benchmarking Heterogeneity and Personalization in Federated Learning
To better answer these questions, we propose Motley, a benchmark for personalized federated learning.
On Privacy and Personalization in Cross-Silo Federated Learning
While the application of differential privacy (DP) has been well-studied in cross-device federated learning (FL), there is a lack of work considering DP and its implications for cross-silo FL, a setting characterized by a limited number of clients each containing many data subjects.
The Skellam Mechanism for Differentially Private Federated Learning
We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy mechanism based on the difference of two independent Poisson random variables.
ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks
Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning.
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
To ensure privacy, we add on-device noise and use secure aggregation so that only the noisy sum is revealed to the server.
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
We present a multi-agent actor-critic method that aims to implicitly address the credit assignment problem under fully cooperative settings.
TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking.
Target localization and proposal generation are two essential subtasks in generic visual tracking, and it is a challenge to address both the two efficiently.
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics.
Ranked #4 on
3D Action Recognition
on Assembly101
LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm
Instead of using fixed coding matrix and decoding strategy, LightMC uses a differentiable decoding strategy, which enables it to dynamically optimize the coding matrix and decoding strategy, toward increasing the overall accuracy of multiclass classification, via back propagation jointly with the training of base learners in an iterative way.
Towards Understanding Chinese Checkers with Heuristics, Monte Carlo Tree Search, and Deep Reinforcement Learning
The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second, unlike Go, there are also no upper bounds on the depth of the search tree since repetitions and backward movements are allowed.
