3.5k citations · 14.1k across the 436 of their papers we have counts for
84 papers · 1 filter
Responsible AI Governance: A Response to UN Interim Report on Governing AI for Humanity
Sarah Kiden, Bernd Stahl, Beverley Townsend +22
This report presents a comprehensive response to the United Nation's Interim Report on Governing Artificial Intelligence (AI) for Humanity. It emphasizes the transformative potenti…
Learning Camera Movement Control from Real-World Drone Videos
Yunzhong Hou, Liang Zheng, Philip Torr
This study seeks to automate camera movement control for filming existing subjects into attractive videos, contrasting with the creation of non-existent content by directly generat…
4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives
Zeyu Yang, Zijie Pan, Xiatian Zhu +4
Dynamic 3D scene representation and novel view synthesis are crucial for enabling immersive experiences required by AR/VR and metaverse applications. It is a challenging task due t…
AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
Runtao Liu, I Chieh Chen, Jindong Gu +6
Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures a…
Detecting LLM Hallucination Through Layer-wise Information Deficiency: Analysis of Ambiguous Prompts and Unanswerable Questions
Hazel Kim, Tom A. Lamb, Adel Bibi +2
Large language models (LLMs) frequently generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. We present a novel, te…
Olympus: A Universal Task Router for Computer Vision Tasks
Yuanze Lin, Yunsheng Li, Dongdong Chen +3
We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks. Ut…
Video Motion Transfer with Diffusion Transformers
Alexander Pondaven, Aliaksandr Siarohin, Sergey Tulyakov +2
We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process t…
Benchmarking Open-ended Audio Dialogue Understanding for Large Audio-Language Models
Kuofeng Gao, Shu-Tao Xia, Ke Xu +2
Large Audio-Language Models (LALMs), such as GPT-4o, have recently unlocked audio dialogue capabilities, enabling direct spoken exchanges with humans. The potential of LALMs broade…
Learnable Sparsity for Vision Generative Models
Yang Zhang, Er Jin, Wenzhong Liang +5
Diffusion models have achieved impressive advancements in various vision tasks. However, these gains often rely on increasing model size, which escalates computational complexity a…
MALT: Improving Reasoning with Multi-Agent LLM Training
Sumeet Ramesh Motwani, Chandler Smith, Rocktim Jyoti Das +6
Large Language Models (LLMs) often produce answers with a single chain-of-thought, which restricts their ability to explore reasoning paths or self-correct flawed outputs in comple…
MatchDiffusion: Training-free Generation of Match-cuts
Alejandro Pardo, Fabio Pizzati, Tong Zhang +4
Match-cuts are powerful cinematic tools that create seamless transitions between scenes, delivering strong visual and metaphorical connections. However, crafting match-cuts is a ch…
Select2Plan: Training-Free ICL-Based Planning through VQA and Memory Retrieval
Davide Buoso, Luke Robinson, Giuseppe Averta +3
This study explores the potential of off-the-shelf Vision-Language Models (VLMs) for high-level robot planning in the context of autonomous navigation. Indeed, while most of existi…
No Culture Left Behind: ArtELingo-28, a Benchmark of WikiArt with Captions in 28 Languages
Youssef Mohamed, Runjia Li, Ibrahim Said Ahmad +4
Research in vision and language has made considerable progress thanks to benchmarks such as COCO. COCO captions focused on unambiguous facts in English; ArtEmis introduced subjecti…
OASIS: Open Agent Social Interaction Simulations with One Million Agents
Ziyi Yang, Zaibin Zhang, Zirui Zheng +20
There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agen…
A Scalable Communication Protocol for Networks of Large Language Models
Samuele Marro, Emanuele La Malfa, Jesse Wright +4
Communication is a prerequisite for collaboration. When scaling networks of AI-powered agents, communication must be versatile, efficient, and portable. These requisites, which we…
Semantic Score Distillation Sampling for Compositional Text-to-3D Generation
Ling Yang, Zixiang Zhang, Junlin Han +4
Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art a…
SAGE: Scalable Ground Truth Evaluations for Large Sparse Autoencoders
Constantin Venhoff, Anisoara Calinescu, Philip Torr +1
A key challenge in interpretability is to decompose model activations into meaningful features. Sparse autoencoders (SAEs) have emerged as a promising tool for this task. However,…
Toward Robust Real-World Audio Deepfake Detection: Closing the Explainability Gap
Georgia Channing, Juil Sock, Ronald Clark +2
The rapid proliferation of AI-manipulated or generated audio deepfakes poses serious challenges to media integrity and election security. Current AI-driven detection solutions lack…
Focus On This, Not That! Steering LLMs with Adaptive Feature Specification
Tom A. Lamb, Adam Davies, Alasdair Paren +2
Despite the success of Instruction Tuning (IT) in training large language models (LLMs), such models often leverage spurious or biased features learnt from their training data and…
MAD-Sherlock: Multi-Agent Debate for Visual Misinformation Detection
Kumud Lakara, Georgia Channing, Christian Rupprecht +4
One of the most challenging forms of misinformation involves pairing images with misleading text to create false narratives. Existing AI-driven detection systems often require doma…
Causal Fine-Tuning under Latent Confounded Shift
Jialin Yu, Yuxiang Zhou, Haoxuan Li +6
Adapting to latent confounded shift remains a core challenge in modern AI. This setting is driven by hidden variables that induce spurious correlations between inputs and outputs d…
Are LLMs Socially Adaptive? Contrasting Belief Evolution in Large Language Models and Humans
Yu Lei, Hao Liu, Chengxing Xie +6
As large language models (LLMs) increasingly engage in complex social interactions, ensuring that their behaviors align with human ethical principles and intentions, known as value…
Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System
Haoyang Su, Renqi Chen, Shixiang Tang +10
The rapid advancement of scientific progress requires innovative tools that can accelerate knowledge discovery. Although recent AI methods, particularly large language models (LLMs…
Towards Multi-Modal Animal Pose Estimation: A Survey and In-Depth Analysis
Qianyi Deng, Oishi Deb, Amir Patel +4
Animal pose estimation (APE) aims to locate the animal body parts using a diverse array of sensor and modality inputs (e.g. RGB cameras, LiDAR, infrared, IMU, acoustic and language…