Search Results for author:
Found 25 papers, 6 papers with code
Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency
Image semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission.
AUTOHALLUSION: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
AUTOHALLUSION enables us to create new benchmarks at the minimum cost and thus overcomes the fragility of hand-crafted benchmarks.
AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales
We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps.
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process.
Real-World Atmospheric Turbulence Correction via Domain Adaptation
Atmospheric turbulence, a common phenomenon in daily life, is primarily caused by the uneven heating of the Earth's surface.
Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model
Adversarial attacks involve adding perturbations to the source image to cause misclassification by the target model, which demonstrates the potential of attacking face recognition models.
ViLA: Efficient Video-Language Alignment for Video Question Answering
In this work, we propose an efficient Video-Language Alignment (ViLA) network.
Ranked #1 on
Video Question Answering
on STAR Benchmark
Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration
Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI).
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs.
Ranked #1 on
Visual Question Answering (VQA)
on HallusionBench
ICAR: Image-based Complementary Auto Reasoning
Based on this notion, we propose a compatibility learning framework, a category-aware Flexible Bidirectional Transformer (FBT), for visual "scene-based set compatibility reasoning" with the cross-domain visual similarity input and auto-regressive complementary item generation.
SCSC: Spatial Cross-scale Convolution Module to Strengthen both CNNs and Transformers
This paper presents a module, Spatial Cross-scale Convolution (SCSC), which is verified to be effective in improving both CNNs and Transformers.
Triplet Knowledge Distillation
Distillation then begins in an online manner, and the teacher is only allowed to express solutions within the aforementioned subspace.
SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition
In practice, we observe a 3. 17-10. 2% accuracy improvement on the aerial video datasets (Okutama, NECDrone), which consist of scenes with single-agent and multi-agent actions.
Ranked #1 on
Action Recognition
on Okutama-Action
Atmospheric Turbulence Correction via Variational Deep Diffusion
We use the learned information to further condition the diffusion model.
Video-Specific Query-Key Attention Modeling for Weakly-Supervised Temporal Action Localization
To better learn these action category queries, we exploit not only the features of the current input video but also the correlation between different videos through a novel video-specific action category query learner worked with a query similarity loss.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
Our algorithm utilizes the motion bias within aerial videos, which enables the selection of motion-salient frames.
Ranked #1 on
Action Recognition
on UAV-Human
CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition
We present CrossLoc3D, a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting.
Ranked #1 on
3D Place Recognition
on CS-Campus3D
DPP-based Client Selection for Federated Learning with Non-IID Data
This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue.
MITFAS: Mutual Information based Temporal Feature Alignment and Sampling for Aerial Video Action Recognition
We present a novel approach for action recognition in UAV videos.
Ranked #2 on
Action Recognition
on UAV-Human
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning
We propose a novel approach for aerial video action recognition.
Ranked #1 on
Action Recognition
on RoCoG-v2
FAR: Fourier Aerial Video Recognition
Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background.
Ranked #1 on
Action Recognition
on UAV Human
METEOR:A Dense, Heterogeneous, and Unstructured Traffic Dataset With Rare Behaviors
We present a new traffic dataset, METEOR, which captures traffic patterns and multi-agent driving behaviors in unstructured scenarios.
Optimizing the Long-Term Average Reward for Continuing MDPs: A Technical Report
cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
Dynamic Region-Aware Convolution
More gracefully, our DRConv transfers the increasing channel-wise filters to spatial dimension with learnable instructor, which not only improve representation ability of convolution, but also maintains computational cost and the translation-invariance as standard convolution dose.
Ranked #19 on
Semantic Segmentation
on MCubeS
Fully Learnable Group Convolution for Acceleration of Deep Neural Networks
Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e. g. smartphone, embedded devices, etc.
