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Found 24 papers, 11 papers with code
AdaVideoRAG: Omni-Contextual Adaptive Retrieval-Augmented Efficient Long Video Understanding
Multimodal Large Language Models (MLLMs) struggle with long videos due to fixed context windows and weak long-term dependency modeling.
UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions
In addition, we expand Wan to UltraWan-1K/-4K, which can natively generate high-quality 1K/4K videos with more consistent text controllability, demonstrating the effectiveness of our data curation. We believe that this work can make a significant contribution to future research on UHD video generation.
Task-Core Memory Management and Consolidation for Long-term Continual Learning
In this paper, we focus on a long-term continual learning (CL) task, where a model learns sequentially from a stream of vast tasks over time, acquiring new knowledge while retaining previously learned information in a manner akin to human learning.
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection
To address that, we propose to learn the input features in global and local manners, forcing the network to memorize the normal patterns more comprehensively.
Long Video Diffusion Generation with Segmented Cross-Attention and Content-Rich Video Data Curation
We introduce Presto, a novel video diffusion model designed to generate 15-second videos with long-range coherence and rich content.
Fleximo: Towards Flexible Text-to-Human Motion Video Generation
Current methods for generating human motion videos rely on extracting pose sequences from reference videos, which restricts flexibility and control.
Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention
This ensures that subjects in the target image can better reference those in the reference image, thereby maintaining better consistency.
MobileMamba: Lightweight Multi-Receptive Visual Mamba Network
CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios.
LLaVA-KD: A Framework of Distilling Multimodal Large Language Models
The success of Large Language Models (LLM) has led researchers to explore Multimodal Large Language Models (MLLM) for unified visual and linguistic understanding.
Allegro: Open the Black Box of Commercial-Level Video Generation Model
Significant advancements have been made in the field of video generation, with the open-source community contributing a wealth of research papers and tools for training high-quality models.
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection
The reconstruction branch is simply a plain reconstruction network that learns to reconstruct normal samples, while the auxiliary branch aims to produce attention masks to guide the noise perturbation process for normal samples from easy to hard.
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
This paper addresses this issue by proposing a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework that is highly extensible for new methods.
High-Performance Temporal Reversible Spiking Neural Networks with $O(L)$ Training Memory and $O(1)$ Inference Cost
This work is expected to break the technical bottleneck of significantly increasing memory cost and training time for large-scale SNNs while maintaining high performance and low inference energy cost.
Anomaly Detection by Adapting a pre-trained Vision Language Model
Recently, large vision and language models have shown their success when adapting them to many downstream tasks.
Yi: Open Foundation Models by 01.AI
The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models.
Ranked #1 on
Chatbot
on AlpacaEval
(using extra training data)
A Discrepancy Aware Framework for Robust Anomaly Detection
To alleviate this issue, we present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies across different anomaly detection benchmarks.
RevColV2: Exploring Disentangled Representations in Masked Image Modeling
Such design enables our architecture with the nice property: maintaining disentangled low-level and semantic information at the end of the network in MIM pre-training.
Reversible Column Networks
Such architectural scheme attributes RevCol very different behavior from conventional networks: during forward propagation, features in RevCol are learned to be gradually disentangled when passing through each column, whose total information is maintained rather than compressed or discarded as other network does.
Ranked #10 on
Semantic Segmentation
on ADE20K
(using extra training data)
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices.
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
Object detection plays an important role in self-driving cars for security development.
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
More importantly, our method does not require extra hardware cost compared to the traditional two-column mapping scheme.
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device
3D object detection is an important task, especially in the autonomous driving application domain.
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design.
