The integration of long -term context capacities into visual understanding considerably improves the potential of VLM, in particular in fields such as robotics, autonomous driving and health care. The expansion of the size of the context allows VLMs to process extensive video and text sequences, thus improving temporal resolution and performance in complex tasks, such as video understanding. However, a major limitation is the quadratic complexity of the attention mechanisms during the pre-filled phase, which causes high latency before the start of autoregressive decoding. This delay, known as time for first, makes the real deployment of VLM with long context. Various methods of sparse attention, such as the sparse transformer, the Swin transformer and the streaming, neglect the specific sparse models found in the VLM with mixed methods, thus limiting their efficiency and their effectiveness.
Unlike text inputs only, visual and video data in VLMS show unique space-time attention structures, forming grid patterns due to local correlations. In the scenarios of mixed modality, clear borders exist between different methods, leading to distinct attention behaviors that clear general methods fail to capture. Recent progress, such as minferference and dynamic approaches of sparse attention, aim to improve the efficiency of inference by adapting online attention models. However, these techniques are often not able to manipulate the subtleties of mixed modality entries. While compression of vision tokens and RNN transformer hybrids have been explored to reduce the calculation load, most of these methods are focused on long video and short text appearances, neglecting the more complex dynamics of multiturn and mod-modalities interactions, which are increasingly important in practical applications.
Researchers from the University of Surrey and Microsoft have introduced Mminferfe, a dynamic and sparse attention method designed to speed up the pre-filling stage of long context VLM. By identifying the models of grid rarity in video inputs and distinct mode limits, Mminferfe applies strategies based on permutation to optimize the calculation of attention. It dynamically builds sparse distributions for each input and uses personalized GPU nuclei for improved efficiency, all without requiring modifications to existing models. Tested on references such as QA video, subtitling and vision-niah, the Mminferse has reached up to 8.3 × acceleration with 1 m tokens, surpassing previous methods while retaining great precision through several point VLM.
MINFERFE is a frame designed to accelerate the pre-filling phase of long-context vision-contexte models by taking advantage of clear attention from the modality. It incorporates three key components: (1) sparse intra-modalian models such as the grid, form A and the attention of vertical slash; (2) Transversal modality models such as the q blinar and the 2D limit; and (3) an algorithm of research of attention little aware of the modality. Instead of a dense calculation, it uses clear dynamic attention with optimized GPU nuclei and effective manipulation of the tensor. The framework dynamically identifies attention diagrams and permut tensioners according to the modality, allowing effective management of multimodal entries and a reduction in general calculation costs while maintaining high performance.
The study assesses Mminference's performance and efficiency on long video tasks, including subtitling, answers to questions and recovery in unimodal and mixed modality. Experiences have been carried out using advanced models, such as Llava-Video and Longvila, with comparisons with several clear attention lines. The results show that the Mminference achieves performance close to full attention while being more effective in calculation. It works particularly well in the newly introduced mixed modality needle in a hay boot task (MM-Niah) by taking advantage of the inter-model peoples models. In addition, Mminferfe demonstrates important acceleners in end -to -end latency and maintains robustness through context lengths and variable input types.
In conclusion, the Mminferse is a clear attention technique with a modality designed to accelerate the Long context VLM without compromising precision. It uses a pattern of attention on the grid based on the permutation adapted to the temporal spatial locality of video entries, as well as a specialized manipulation for the limits of mixed modality. A research algorithm identifies optimal sparse models per head of attention, dynamically adapting to the entry. The method fits directly into current VLM pipelines without requiring models of model or fine adjustment. With optimized GPU nuclei, the Mminferse reached up to 8.3 × acceleration during the pre-filling phase with 1m tokens on various tasks, including video QA, subtitling and mixed modality marks, while retaining complete waiting performance.
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Sana Hassan, consulting trainee at Marktechpost and double -degree student at Iit Madras, is passionate about the application of technology and AI to meet the challenges of the real world. With a great interest in solving practical problems, it brings a new perspective to the intersection of AI and real life solutions.
