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Found 18 papers, 8 papers with code
The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR).
Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention
Many-shot in-context learning has recently shown promise as an alternative to finetuning, with the major advantage that the same model can be served for multiple tasks.
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation
This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture.
PAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for LLMs
Large Language Models (LLMs) have excelled in various tasks but are still vulnerable to jailbreaking attacks, where attackers create jailbreak prompts to mislead the model to produce harmful or offensive content.
Effect of Adaptation Rate and Cost Display in a Human-AI Interaction Game
The cost feedback was provided by one of two types of visual displays: (a) cost at the current joint action vector, or (b) cost in a local neighborhood of the current joint action vector.
MV-DETR: Multi-modality indoor object detection by Multi-View DEtecton TRansformers
Unfortunately, single RGBD dataset with thousands of data is not enough for training an discriminating filter for visual texture feature extraction.
Ranked #4 on
3D Object Detection
on ScanNetV2
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #3 on
Question Answering
on PeerQA
A Two-Stage Adverse Weather Semantic Segmentation Method for WeatherProof Challenge CVPR 2024 Workshop UG2+
In the second stage, we employ the InternImage network to train for the semantic segmentation task using the generated pseudo ground truths.
Trusted Multi-view Learning with Label Noise
This motivates us to delve into a new generalized trusted multi-view learning problem: how to develop a reliable multi-view learning model under the guidance of noisy labels?
HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes
The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings.
VIALM: A Survey and Benchmark of Visually Impaired Assistance with Large Models
Visually Impaired Assistance (VIA) aims to automatically help the visually impaired (VI) handle daily activities.
Mapping Information in Feature Extraction Transformation for Chirp Signal
The demonstration of the noise suppression in the iteration process and feature enhancement supports the effectiveness of the mapping information model.
Discrete Search in Heterogeneous Integer Spaces for Automated Choice of Parameters using Correct-by-Construction Methods
Discrete Search of integer spaces for tool parameter values provides a powerful methodology for modeling and finding a heuristically optimal parameter list for a given system.
Detecting fake news by enhanced text representation with multi-EDU-structure awareness
Finally, the two EDU representations are incorporated as the enhanced text representation for fake news detection, using a gated recursive unit combined with a global attention mechanism.
A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo
In recent years, due to the mental burden of depression, the number of people who endanger their lives has been increasing rapidly.
Understanding Regularized Spectral Clustering via Graph Conductance
The second part of the paper starts from a previously proposed form of regularized spectral clustering and shows that it is related to the graph conductance on a `regularized graph'.
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints.
Discovering Political Topics in Facebook Discussion threads with Graph Contextualization
In the Facebook data, spectral clustering without the contextualizing text information finds a mixture of (i) candidate and (ii) issue clusters.
