Computer Vision
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Subject-Driven Image Evaluation Gets Simpler: Google Researchers Introduce REFVNLI to Jointly...
Text-to-image (T2I) generation has evolved to include subject-driven approaches, which enhance standard T2I models by incorporating reference images alongside text prompts. This advancement allows...
UniME: A Two-Stage Framework for Enhancing Multimodal Representation Learning with MLLMs
The CLIP framework has become foundational in multimodal representation learning, particularly for tasks such as image-text retrieval. However, it faces several limitations: a strict...
ViSMaP: Unsupervised Summarization of Hour-Long Videos Using Meta-Prompting and Short-Form Datasets
Video captioning models are typically trained on datasets consisting of short videos, usually under three minutes in length, paired with corresponding captions. While this...
Meta AI Introduces Token-Shuffle: A Simple AI Approach to Reducing Image...
Autoregressive (AR) models have made significant advances in language generation and are increasingly explored for image synthesis. However, scaling AR models to high-resolution images...
Skywork AI Advances Multimodal Reasoning: Introducing Skywork R1V2 with Hybrid Reinforcement...
Recent advancements in multimodal AI have highlighted a persistent challenge: achieving strong specialized reasoning capabilities while preserving generalization across diverse tasks. "Slow-thinking" models such...
Microsoft Research Introduces MMInference to Accelerate Pre-filling for Long-Context Vision-Language Models
Integrating long-context capabilities with visual understanding significantly enhances the potential of VLMs, particularly in domains such as robotics, autonomous driving, and healthcare. Expanding the...
NVIDIA AI Releases Describe Anything 3B: A Multimodal LLM for Fine-Grained...
Challenges in Localized Captioning for Vision-Language Models
Describing specific regions within images or videos remains a persistent challenge in vision-language modeling. While general-purpose vision-language...
Decoupled Diffusion Transformers: Accelerating High-Fidelity Image Generation via Semantic-Detail Separation and...
Diffusion Transformers have demonstrated outstanding performance in image generation tasks, surpassing traditional models, including GANs and autoregressive architectures. They operate by gradually adding noise...
Long-Context Multimodal Understanding No Longer Requires Massive Models: NVIDIA AI Introduces...
In recent years, vision-language models (VLMs) have advanced significantly in bridging image, video, and textual modalities. Yet, a persistent limitation remains: the inability to...
Stanford Researchers Propose FramePack: A Compression-based AI Framework to Tackle Drifting...
Video generation, a branch of computer vision and machine learning, focuses on creating sequences of images that simulate motion and visual realism over time....
Meta AI Released the Perception Language Model (PLM): An Open and...
Despite rapid advances in vision-language modeling, much of the progress in this field has been shaped by models trained on proprietary datasets, often relying...
Meta AI Introduces Perception Encoder: A Large-Scale Vision Encoder that Excels...
The Challenge of Designing General-Purpose Vision Encoders
As AI systems grow increasingly multimodal, the role of visual perception models becomes more complex. Vision encoders are...
Do We Still Need Complex Vision-Language Pipelines? Researchers from ByteDance and...
MLLMs have recently advanced in handling fine-grained, pixel-level visual understanding, thereby expanding their applications to tasks such as precise region-based editing and segmentation. Despite...
Advancing Vision-Language Reward Models: Challenges, Benchmarks, and the Role of Process-Supervised...
Process-supervised reward models (PRMs) offer fine-grained, step-wise feedback on model responses, aiding in selecting effective reasoning paths for complex tasks. Unlike output reward models...
VideoMind: A Role-Based Agent for Temporal-Grounded Video Understanding
LLMs have shown impressive capabilities in reasoning tasks like Chain-of-Thought (CoT), enhancing accuracy and interpretability in complex problem-solving. While researchers are extending these capabilities...
Efficient Inference-Time Scaling for Flow Models: Enhancing Sampling Diversity and Compute...
Recent advancements in AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by...
Meta Reality Labs Research Introduces Sonata: Advancing Self-Supervised Representation Learning for...
3D self-supervised learning (SSL) has faced persistent challenges in developing semantically meaningful point representations suitable for diverse applications with minimal supervision. Despite substantial progress...
TokenBridge: Bridging The Gap Between Continuous and Discrete Token Representations In...
Autoregressive visual generation models have emerged as a groundbreaking approach to image synthesis, drawing inspiration from language model token prediction mechanisms. These innovative models...
Vision-R1: Redefining Reinforcement Learning for Large Vision-Language Models
Large Vision-Language Models (LVLMs) have made significant strides in recent years, yet several key limitations persist. One major challenge is aligning these models effectively...
This AI Paper from UC Berkeley Introduces TULIP: A Unified Contrastive...
Recent advancements in artificial intelligence have significantly improved how machines learn to associate visual content with language. Contrastive learning models have been pivotal in...
IBM and Hugging Face Researchers Release SmolDocling: A 256M Open-Source Vision...
Converting complex documents into structured data has long posed significant challenges in the field of computer science. Traditional approaches, involving ensemble systems or very...
This AI Paper Introduces R1-Onevision: A Cross-Modal Formalization Model for Advancing...
Multimodal reasoning is an evolving field that integrates visual and textual data to enhance machine intelligence. Traditional artificial intelligence models excel at processing either...
VisualWebInstruct: A Large-Scale Multimodal Reasoning Dataset for Enhancing Vision-Language Models
VLMs have shown notable progress in perception-driven tasks such as visual question answering (VQA) and document-based visual reasoning. However, their effectiveness in reasoning-intensive tasks...
This AI Paper Introduces FoundationStereo: A Zero-Shot Stereo Matching Model for...
Stereo depth estimation plays a crucial role in computer vision by allowing machines to infer depth from two images. This capability is vital for...
STORM (Spatiotemporal TOken Reduction for Multimodal LLMs): A Novel AI Architecture...
Understanding videos with AI requires handling sequences of images efficiently. A major challenge in current video-based AI models is their inability to process videos...
Salesforce AI Proposes ViUniT (Visual Unit Testing): An AI Framework to...
Visual programming has emerged strongly in computer vision and AI, especially regarding image reasoning. Visual programming enables computers to create executable code that interacts...
MVGD from Toyota Research Institute: Zero Shot 3D Scene Reconstruction
Toyota Research Institute Researchers have unveiled Multi-View Geometric Diffusion (MVGD), a groundbreaking diffusion-based architecture that directly synthesizes high-fidelity novel RGB and depth maps from...
This AI Paper from Aalto University Introduces VQ-VFM-OCL: A Quantization-Based Vision...
Object-centric learning (OCL) is an area of computer vision that aims to decompose visual scenes into distinct objects, enabling advanced vision tasks such as...
This AI Paper Introduces UniTok: A Unified Visual Tokenizer for Enhancing...
With researchers aiming to unify visual generation and understanding into a single framework, multimodal artificial intelligence is evolving rapidly. Traditionally, these two domains have...
Simplifying Self-Supervised Vision: How Coding Rate Regularization Transforms DINO & DINOv2
Learning useful features from large amounts of unlabeled images is important, and models like DINO and DINOv2 are designed for this. These models work...
CoSyn: An AI Framework that Leverages the Coding Capabilities of Text-only...
Vision-language models (VLMs) have demonstrated impressive capabilities in general image understanding, but face significant challenges when processing text-rich visual content such as charts, documents,...
Google DeepMind Research Releases SigLIP2: A Family of New Multilingual Vision-Language...
Modern vision-language models have transformed how we process visual data, yet they often fall short when it comes to fine-grained localization and dense feature...
Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language,...
Multimodal AI agents are designed to process and integrate various data types, such as images, text, and videos, to perform tasks in digital and...
Learning Intuitive Physics: Advancing AI Through Predictive Representation Models
Humans possess an innate understanding of physics, expecting objects to behave predictably without abrupt changes in position, shape, or color. This fundamental cognition is...
ViLa-MIL: Enhancing Whole Slide Image Classification with Dual-Scale Vision-Language Multiple Instance...
Whole Slide Image (WSI) classification in digital pathology presents several critical challenges due to the immense size and hierarchical nature of WSIs. WSIs contain...
Ola: A State-of-the-Art Omni-Modal Understanding Model with Advanced Progressive Modality Alignment...
Understanding different data types like text, images, videos, and audio in one model is a big challenge. Large language models that handle all these...
LLMDet: How Large Language Models Enhance Open-Vocabulary Object Detection
Open-vocabulary object detection (OVD) aims to detect arbitrary objects with user-provided text labels. Although recent progress has enhanced zero-shot detection ability, current techniques handicap...
This AI Paper Introduces MAETok: A Masked Autoencoder-Based Tokenizer for Efficient...
Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly...
Singapore University of Technology and Design (SUTD) Explores Advancements and Challenges...
After the success of large language models (LLMs), the current research extends beyond text-based understanding to multimodal reasoning tasks. These tasks integrate vision and...
Researchers from ETH Zurich and TUM Share Everything You Need to...
There is no gainsaying that artificial intelligence has developed tremendously in various fields. However, the accurate evaluation of its progress would be incomplete without...
Meta AI Introduces MILS: A Training-Free Multimodal AI Framework for Zero-Shot...
Large Language Models (LLMs) are primarily designed for text-based tasks, limiting their ability to interpret and generate multimodal content such as images, videos, and...
Meta AI Introduces VideoJAM: A Novel AI Framework that Enhances Motion...
Despite recent advancements, generative video models still struggle to represent motion realistically. Many existing models focus primarily on pixel-level reconstruction, often leading to inconsistencies...
ByteDance Proposes OmniHuman-1: An End-to-End Multimodality Framework Generating Human Videos based...
Despite progress in AI-driven human animation, existing models often face limitations in motion realism, adaptability, and scalability. Many models struggle to generate fluid body...
Light3R-SfM: A Scalable and Efficient Feed-Forward Approach to Structure-from-Motion
Structure-from-motion (SfM) focuses on recovering camera positions and building 3D scenes from multiple images. This process is important for tasks like 3D reconstruction and...
InternVideo2.5: Hierarchical Token Compression and Task Preference Optimization for Video MLLMs
Multimodal large language models (MLLMs) have emerged as a promising approach towards artificial general intelligence, integrating diverse sensing signals into a unified framework. However,...
This AI Paper Introduces IXC-2.5-Reward: A Multi-Modal Reward Model for Enhanced...
Artificial intelligence has grown significantly with the integration of vision and language, allowing systems to interpret and generate information across multiple data modalities. This...
Netflix Introduces Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
Generative modeling challenges in motion-controllable video generation present significant research hurdles. Current approaches in video generation struggle with precise motion control across diverse scenarios....
Alibaba Researchers Propose VideoLLaMA 3: An Advanced Multimodal Foundation Model for...
Advancements in multimodal intelligence depend on processing and understanding images and videos. Images can reveal static scenes by providing information regarding details such as...
Introducing GS-LoRA++: A Novel Approach to Machine Unlearning for Vision Tasks
Pre-trained vision models have been foundational to modern-day computer vision advances across various domains, such as image classification, object detection, and image segmentation. There...
Create Portrait Mode Effect with Segment Anything Model 2 (SAM2)
Have you ever admired how smartphone cameras isolate the main subject from the background, adding a subtle blur to the background based on depth?...
Google AI Proposes a Fundamental Framework for Inference-Time Scaling in Diffusion...
Generative models have revolutionized fields like language, vision, and biology through their ability to learn and sample from complex data distributions. While these models...
Researchers from MIT, Google DeepMind, and Oxford Unveil Why Vision-Language Models...
Vision-language models (VLMs) play a crucial role in multimodal tasks like image retrieval, captioning, and medical diagnostics by aligning visual and linguistic data. However,...
Researchers from China Develop Advanced Compression and Learning Techniques to process ...
One of the most significant and advanced capabilities of a multimodal large language model is long-context video modeling, which allows models to handle movies,...
GameFactory: Leveraging Pre-trained Video Models for Creating New Game
Video diffusion models have emerged as powerful tools for video generation and physics simulation, showing promise in developing game engines. These generative game engines...
Meet OmAgent: A New Python Library for Building Multimodal Language Agents
Understanding long videos, such as 24-hour CCTV footage or full-length films, is a major challenge in video processing. Large Language Models (LLMs) have shown...
Purdue University Researchers Introduce ETA: A Two-Phase AI Framework for Enhancing...
Vision-language models (VLMs) represent an advanced field within artificial intelligence, integrating computer vision and natural language processing to handle multimodal data. These models allow...
Researchers from Meta AI and UT Austin Explored Scaling in Auto-Encoders...
Modern image and video generation methods rely heavily on tokenization to encode high-dimensional data into compact latent representations. While advancements in scaling generator models...
ByteDance Researchers Introduce Tarsier2: A Large Vision-Language Model (LVLM) with 7B...
Video understanding has long presented unique challenges for AI researchers. Unlike static images, videos involve intricate temporal dynamics and spatial-temporal reasoning, making it difficult...
Revolutionizing Vision-Language Tasks with Sparse Attention Vectors: A Lightweight Approach to...
Generative Large Multimodal Models (LMMs), such as LLaVA and Qwen-VL, excel in vision-language (VL) tasks like image captioning and visual question answering (VQA). However,...
Meet VideoRAG: A Retrieval-Augmented Generation (RAG) Framework Leveraging Video Content for...
Video-based technologies have become essential tools for information retrieval and understanding complex concepts. Videos combine visual, temporal, and contextual data, providing a multimodal representation...
Salesforce AI Introduces TACO: A New Family of Multimodal Action Models...
Developing effective multi-modal AI systems for real-world applications requires handling diverse tasks such as fine-grained recognition, visual grounding, reasoning, and multi-step problem-solving. Existing open-source...
Meta AI Introduces CLUE (Constitutional MLLM JUdgE): An AI Framework Designed...
The rapid growth of digital platforms has brought image safety into sharp focus. Harmful imagery—ranging from explicit content to depictions of violence—poses significant challenges...
This AI Paper Introduces Toto: Autoregressive Video Models for Unified Image...
Autoregressive pre-training has proved to be revolutionary in machine learning, especially concerning sequential data processing. Predictive modeling of the following sequence elements has been...
Sa2VA: A Unified AI Framework for Dense Grounded Video and Image...
Multi-modal Large Language Models (MLLMs) have revolutionized various image and video-related tasks, including visual question answering, narrative generation, and interactive editing. A critical challenge...
ProVision: A Scalable Programmatic Approach to Vision-Centric Instruction Data for Multimodal...
The rise of multimodal applications has highlighted the importance of instruction data in training MLMs to handle complex image-based queries effectively. Current practices for...
Content-Adaptive Tokenizer (CAT): An Image Tokenizer that Adapts Token Count based...
One of the major hurdles in AI-driven image modeling is the inability to account for the diversity in image content complexity effectively. The tokenization...
This AI Paper Introduces Virgo: A Multimodal Large Language Model for...
Artificial intelligence research has steadily advanced toward creating systems capable of complex reasoning. Multimodal large language models (MLLMs) represent a significant development in this...
HBI V2: A Flexible AI Framework that Elevates Video-Language Learning with...
Video-Language Representation Learning is a crucial subfield of multi-modal representation learning that focuses on the relationship between videos and their associated textual descriptions. Its...
EPFL Researchers Releases 4M: An Open-Source Training Framework to Advance Multimodal...
Multimodal foundation models are becoming increasingly relevant in artificial intelligence, enabling systems to process and integrate multiple forms of data—such as images, text, and...
VITA-1.5: A Multimodal Large Language Model that Integrates Vision, Language, and...
The development of multimodal large language models (MLLMs) has brought new opportunities in artificial intelligence. However, significant challenges persist in integrating visual, linguistic, and...
From Latent Spaces to State-of-the-Art: The Journey of LightningDiT
Latent diffusion models are advanced techniques for generating high-resolution images by compressing visual data into a latent space using visual tokenizers. These tokenizers reduce...
DiTCtrl: A Training-Free Multi-Prompt Video Generation Method Under MM-DiT Architectures
Generative AI has revolutionized video synthesis, producing high-quality content with minimal human intervention. Multimodal frameworks combine the strengths of generative adversarial networks (GANs), autoregressive...
ByteDance Research Introduces 1.58-bit FLUX: A New AI Approach that Gets...
Vision Transformers (ViTs) have become a cornerstone in computer vision, offering strong performance and adaptability. However, their large size and computational demands create challenges,...
Collective Monte Carlo Tree Search (CoMCTS): A New Learning-to-Reason Method for...
In today's world, Multimodal large language models (MLLMs) are advanced systems that process and understand multiple input forms, such as text and images. By...
Microsoft and Tsinghua University Researchers Introduce Distilled Decoding: A New Method...
Autoregressive (AR) models have changed the field of image generation, setting new benchmarks in producing high-quality visuals. These models break down the image creation...
CoordTok: A Scalable Video Tokenizer that Learns a Mapping from Co-ordinate-based...
Breaking down videos into smaller, meaningful parts for vision models remains challenging, particularly for long videos. Vision models rely on these smaller parts, called...
Deep Learning and Vocal Fold Analysis: The Role of the GIRAFE...
Semantic segmentation of the glottal area from high-speed videoendoscopic (HSV) sequences presents a critical challenge in laryngeal imaging. The field faces a significant shortage...
Evaluation Agent: A Multi-Agent AI Framework for Efficient, Dynamic, Multi-Round Evaluation,...
Visual generative models have advanced significantly in terms of the ability to create high-quality images and videos. These developments, powered by AI, enable applications...
NOVA: A Novel Video Autoregressive Model Without Vector Quantization
Autoregressive LLMs are complex neural networks that generate coherent and contextually relevant text through sequential prediction. These LLms excel at handling large datasets and...
This AI Paper from Microsoft and Oxford Introduce Olympus: A Universal...
Computer vision models have made significant strides in solving individual tasks such as object detection, segmentation, and classification. Complex real-world applications such as autonomous...
Meta AI Releases Apollo: A New Family of Video-LMMs Large Multimodal...
While multimodal models (LMMs) have advanced significantly for text and image tasks, video-based models remain underdeveloped. Videos are inherently complex, combining spatial and temporal...
Gaze-LLE: A New AI Model for Gaze Target Estimation Built on...
Accurately predicting where a person is looking in a scene—gaze target estimation—represents a significant challenge in AI research. Integrating complex cues such as head...
Microsoft AI Research Introduces OLA-VLM: A Vision-Centric Approach to Optimizing Multimodal...
Multimodal large language models (MLLMs) are advancing rapidly, enabling machines to interpret and reason about textual and visual data simultaneously. These models have transformative...
BiMediX2: A Groundbreaking Bilingual Bio-Medical Large Multimodal Model integrating Text and...
Recent advancements in healthcare AI, including medical LLMs and LMMs, show great potential for improving access to medical advice. However, these models are largely...
This AI Paper Introduces SRDF: A Self-Refining Data Flywheel for High-Quality...
Vision-and-Language Navigation (VLN) combines visual perception with natural language understanding to guide agents through 3D environments. The goal is to enable agents to follow...
MosAIC: A Multi-Agent AI Framework for Cross-Cultural Image Captioning
Large Multimodal Models (LMMs) excel in many vision-language tasks, but their effectiveness needs to improve in cross-cultural contexts. This is because they need to...
Researchers from UCLA and Apple Introduce STIV: A Scalable AI Framework...
Video generation has improved with models like Sora, which uses the Diffusion Transformer (DiT) architecture. While text-to-video (T2V) models have advanced, they often find...
Transforming Video Diffusion Models: The CausVid Approach
AI Video Generation has become increasingly popular in many industries due to its efficacy, cost-effectiveness, and ease of use. However, most state-of-the-art video generators...
Meet Maya: An 8B Open-Source Multilingual Multimodal Model with Toxicity-Free Datasets...
Vision-Language Models (VLMs) allow machines to understand and reason about the visual world through natural language. These models have applications in image captioning, visual...
ByteDance Introduces Infinity: An Autoregressive Model with Bitwise Modeling for High-Resolution...
High-resolution, photorealistic image generation presents a multifaceted challenge in text-to-image synthesis, requiring models to achieve intricate scene creation, prompt adherence, and realistic detailing. Among...
DEIM: A New AI Framework that Enhances DETRs for Faster Convergence...
Transformer-based Detection models are gaining popularity due to their one-to-one matching strategy. Unlike familiar many-to-One Detection models like YOLO, which require Non-Maximum Suppression (NMS)...
The Power of Active Data Curation in Multimodal Knowledge Distillation
AI advancements have led to the incorporation of a large variety of datasets for multimodal models, allowing for a more comprehensive understanding of complex...
This AI Paper from UC Santa Cruz and the University of...
Web-crawled image-text datasets are critical for training vision-language models, enabling advancements in tasks such as image captioning and visual question answering. However, these datasets...
Microsoft Introduces Florence-VL: A Multimodal Model Redefining Vision-Language Alignment with Generative...
Integrating vision and language processing in AI has become a cornerstone for developing systems capable of simultaneously understanding visual and textual data, i.e., multimodal...
UC Berkeley Researchers Explore the Role of Task Vectors in Vision-Language...
Vision-and-language models (VLMs) are important tools that use text to handle different computer vision tasks. Tasks like recognizing images, reading text from images (OCR),...
NVIDIA AI Introduces NVILA: A Family of Open Visual Language Models...
Visual language models (VLMs) have come a long way in integrating visual and textual data. Yet, they come with significant challenges. Many of today’s...
Advancing Large Multimodal Models: DocHaystack, InfoHaystack, and the Vision-Centric Retrieval-Augmented Generation...
LMMs have made significant strides in vision-language understanding but still need help reasoning over large-scale image collections, limiting their real-world applications like visual search...
Google DeepMind Just Released PaliGemma 2: A New Family of Open-Weight...
Vision-language models (VLMs) have come a long way, but they still face significant challenges when it comes to effectively generalizing across different tasks. These...
TimeMarker: Precise Temporal Localization for Video-LLM Interactions
Large language models (LLMs) have rapidly advanced multimodal large language models (LMMs), particularly in vision-language tasks. Videos represent complex, information-rich sources crucial for understanding...
Can You Turn Your Vision-Language Model from a Zero-Shot Model to...
Contrastive language-image pretraining has emerged as a promising approach in artificial intelligence, enabling dual vision and text encoders to align modalities while maintaining dissimilarity...