​​LISA: Reasoning Segmentation via Large Language Model

The field of image segmentation has taken a leap forward with the introduction of LISA (Large Language Instructed Segmentation Assistant). This cutting-edge model excels at "reasoning segmentation," a novel task that generates segmentation masks from complex and implicit text queries. Building upon the capabilities of multi-modal Large Language Models, LISA expands its vocabulary with a <SEG> token and introduces an innovative "embedding-as-mask" paradigm to achieve this feat. Notably, the model is adept at intricate reasoning, utilizes world knowledge, offers explanatory answers, and can handle multi-turn conversations.

What's astonishing about LISA is its robust zero-shot learning abilities. Even when trained on datasets that lack reasoning-based tasks, LISA performs impressively well. Moreover, when fine-tuned with just 239 specific reasoning segmentation image-instruction pairs, the model's performance is further enhanced.

Paper link: https://arxiv.org/abs/2308.00692
Code link: https://github.com/dvlab-research/LISA

A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-lisa

#deeplearning #cv #nlp #imagesegmentation #largelanguagemodel

​​OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents

The OBELICS dataset is a game-changer in the world of machine learning and AI! Unlike existing closed-source datasets, OBELICS is a vast, open-source, web-scale dataset specially curated for training large multimodal models. Boasting 141 million web pages from Common Crawl, 353 million high-quality images, and an impressive 115 billion text tokens, OBELICS sets a new standard in the richness and diversity of training data.

But it's not just about the numbers; it's about results. To prove its mettle, models with 9 and 80 billion parameters were trained on OBELICS, showcasing competitive performance across various multimodal benchmarks. Named IDEFICS, these models outperformed or matched their closed-source counterparts, proving that OBELICS isn't just a theoretical concept—it's a practical, high-impact alternative.

Paper link: https://huggingface.co/papers/2306.16527
Model card link: https://huggingface.co/HuggingFaceM4/idefics-80b-instruct
Blogpost link: https://huggingface.co/blog/idefics

A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-obelisc

#deeplearning #cv #nlp #largelanguagemodel #opensource

All of LibGen.
131TB of high quality text.

Just think about it.

​​Giraffe: Adventures in Expanding Context Lengths in LLMs

Modern Large Language Models (LLMs) have revolutionized our ability to process and understand vast amounts of textual data. Yet, these models, like LLaMA and LLaMA2, often come with a caveat: they're constrained by fixed context lengths, which means they're limited in handling longer sequences of input data at evaluation. This paper tackles that constraint by investigating a variety of methods for "context length extrapolation," which essentially enables these models to understand and work with longer text sequences. Among the techniques explored, the paper introduces an innovative "truncated basis" strategy for altering positional encodings within the attention mechanism, promising a more scalable future for LLMs.

The researchers put their theories to the test with three brand-new evaluation tasks—FreeFormQA, AlteredNumericQA, and LongChat-Lines—providing a more nuanced measure of model performance than the traditionally used metric of perplexity. Their findings? Linear scaling came out on top as the most effective way to extend the context length, but the truncated basis method showed potential for future exploration. To propel the research community even further, the paper releases three game-changing long-context models, named Giraffe, with context lengths ranging from 4k to an astonishing 32k.

Paper link: https://arxiv.org/abs/2308.10882
Code link: https://github.com/abacusai/Long-Context

A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-giraffe

#deeplearning #cv #nlp #largelanguagemodel #opensource #largecontext

​​CoTracker: It is Better to Track Together

The CoTracker paper proposes a groundbreaking approach that takes video motion prediction to the next level. Traditional methods have often been limited, either tracking the motion of all points in a frame collectively using optical flow, or tracking individual points through a video. These approaches tend to overlook the crucial interrelationships between multiple points, especially when they're part of the same physical object. CoTracker flips the script by employing a transformer-based architecture to jointly track multiple points throughout a video, effectively modeling the correlations between different points in time.

What really sets CoTracker apart is its versatility and adaptability. It's engineered to handle extremely long videos through a unique sliding-window mechanism, and iteratively updates estimates for multiple trajectories. The system even allows for the addition of new tracking points on-the-fly, offering unmatched flexibility. CoTracker outshines state-of-the-art methods in nearly all benchmark tests.

Paper link: https://arxiv.org/abs/2307.07635
Code link: https://github.com/facebookresearch/co-tracker
Project link: https://co-tracker.github.io/

A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-cotracker

#deeplearning #cv #objecttracking

​​RecMind: Large Language Model Powered Agent For Recommendation

Recent advancements have significantly improved the capabilities of Large Language Models (LLMs) in various tasks, yet their potential in the realm of personalized recommendations has been relatively unexplored. To address this gap, a new LLM-powered autonomous recommender agent called RecMind has been developed. RecMind is designed to provide highly personalized recommendations by leveraging planning algorithms, tapping into external data sources, and using individualized data.

One standout feature of RecMind is its novel "Self-Inspiring" algorithm, which enhances the model's planning abilities. During each step of planning, the algorithm encourages the model to consider all its past actions, thereby improving its understanding and use of historical data. The performance of RecMind has been evaluated across multiple recommendation tasks like rating prediction, sequential and direct recommendation, explanation generation, and review summarization. The results show that RecMind outperforms existing LLM-based methods in these tasks and is competitive with the specialized P5 model.

Paper link: https://arxiv.org/abs/2308.14296

A detailed unofficial overview of the paper:
https://andlukyane.com/blog/paper-review-recmind

#deeplearning #nlp #llm #recommender

​​Contrastive Feature Masking Open-Vocabulary Vision Transformer

Contrastive Feature Masking Vision Transformer (CFM-ViT): a new approach for image-text pretraining that is optimized for open-vocabulary object detection. Unlike traditional masked autoencoders, which typically operate in the pixel space, CFM-ViT uses a joint image-text embedding space for reconstruction. This approach enhances the model's ability to learn region-level semantics. Additionally, the model features a Positional Embedding Dropout to better handle scale variations that occur when transitioning from image-text pretraining to detection finetuning. PED also enables the model to use a "frozen" ViT backbone as a region classifier without loss of performance.

In terms of results, CFM-ViT sets a new benchmark in open-vocabulary object detection with a 33.9 APr score on the LVIS dataset, outperforming the closest competitor by 7.6 points. The model also demonstrates strong capabilities in zero-shot detection transfer. Beyond object detection, it excels in image-text retrieval, outperforming the state of the art on 8 out of 12 key metrics. These features and results position CFM-ViT as a significant advancement in the field of computer vision and machine learning.

Paper link: https://arxiv.org/abs/2309.00775

My overview of the paper:
https://andlukyane.com/blog/paper-review-cfmvit
https://artgor.medium.com/paper-review-contrastive-feature-masking-open-vocabulary-vision-transformer-4639d1bf7043

#paperreview