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 models (VLMs) perform well at generating global captions, they often fall short in producing detailed, region-specific descriptions. These limitations are amplified in video data, where models must account for temporal…

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 on distillation from closed-source systems. This reliance creates barriers to scientific transparency and reproducibility, particularly for tasks involving fine-grained image and video understanding. Benchmark performance may reflect the training data and…

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 to multi-modal domains, videos present unique challenges due to their temporal dimension. Unlike static images, videos require understanding dynamic interactions over time. Current visual CoT methods excel with static inputs but…

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 (ORMs), which evaluate responses based on final outputs, PRMs provide detailed assessments at each step, making them particularly valuable for reasoning-intensive applications. While PRMs have been extensively studied in language tasks,…

Recent advancements in artificial intelligence have significantly improved how machines learn to associate visual content with language. Contrastive learning models have been pivotal in this transformation, particularly those aligning images and text through a shared embedding space. These models are central to zero-shot classification, image-text retrieval, and multimodal reasoning. However, while these tools have pushed…

Converting complex documents into structured data has long posed significant challenges in the field of computer science. Traditional approaches, involving ensemble systems or very large foundational models, often encounter substantial hurdles such as difficulty in fine-tuning, generalization issues, hallucinations, and high computational costs. Ensemble systems, though efficient for specific tasks, frequently fail to generalize due…

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 as a continuous flow, missing important motion details and disrupting continuity. This lack of temporal modeling prevents tracing changes; therefore, events and interactions are partially unknown. Long videos also make the…

Visual programming has emerged strongly in computer vision and AI, especially regarding image reasoning. Visual programming enables computers to create executable code that interacts with visual content to offer correct responses. These systems form the backbone of object detection, image captioning, and VQA applications. Its effectiveness stems from the ability to modularize multiple reasoning tasks,…

With researchers aiming to unify visual generation and understanding into a single framework, multimodal artificial intelligence is evolving rapidly. Traditionally, these two domains have been treated separately due to their distinct requirements. Generative models focus on producing fine-grained image details while understanding models prioritize high-level semantics. The challenge lies in integrating both capabilities effectively without…

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 extraction. Many traditional models focus on high-level semantic understanding and zero-shot classification but struggle with detailed spatial reasoning. These limitations can impact applications that require precise localization, such as document analysis…