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…

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 in image-based SSL, existing point cloud SSL methods have largely been limited due to the issue known as the “geometric shortcut,” where models excessively rely on low-level geometric features like surface…

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 AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by models like OpenAI o1 and DeepSeek R1, enhances model performance by leveraging additional computational resources during inference. Test-time budget forcing has emerged as an efficient technique in LLMs, enabling improved performance…

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…

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly when operating directly on high-dimensional pixel data. Researchers have been investigating ways to optimize latent space representations to improve efficiency without compromising image quality. A critical problem in diffusion models is…

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 movements and rely on filtered training datasets, restricting their ability to handle varied scenarios. Facial animation has seen improvements, but full-body animations remain challenging due to inconsistencies in gesture accuracy and…