The fields of computer vision, large language models, and vision-language models are witnessing significant advancements towards developing more efficient and human-aligned systems. A common theme among these areas is the pursuit of compact, generalizable, and interpretable representations.
In computer vision, researchers are exploring structure-first pretraining methods, such as using line drawings, to induce more efficient and human-aligned visual understanding. Notable papers include Learning More by Seeing Less and Dynamic Pattern Alignment Learning, which propose novel pretraining frameworks for efficient and transferable vision models.
The field of large language models is moving towards ultra-low-bit quantization, with innovative methods such as 2-bit quantization and microkernel design enabling significant reductions in computational costs and memory requirements. Papers like The Fourth State and Pushing the Envelope of LLM Inference on AI-PC demonstrate state-of-the-art performance in LLM inference.
In large language models, optimizing Key-Value cache management and reducing computational demands are key focus areas. Techniques like dynamic token pruning, expert-sharded KV storage, and semantic caching are being explored to enhance performance and scalability. SlimInfer and PiKV are notable examples of frameworks that accelerate inference and improve KV cache management.
Vision-language models are also becoming more efficient and compressed, with methods like frequency domain compression, adaptive token pruning, and collaborative frameworks reducing computational overhead and inference latency. Papers like Fourier-VLM and AdaptInfer achieve competitive performance with strong generalizability and reduced inference FLOPs.
Overall, these advancements are driving the development of more practical and widely applicable models in computer vision and language processing. By focusing on efficiency, interpretability, and human alignment, researchers are creating systems that can better understand and interact with the world around us.
