Do We Still Need Complex Vision-language Pipelines? Researchers From Bytedance And Whu Introduce Pixel-sail—a Single Transformer Model For Pixel-level Understanding That Outperforms 7b Mllms

MLLMs person precocious advanced successful handling fine-grained, pixel-level ocular understanding, thereby expanding their applications to tasks specified arsenic precise region-based editing and segmentation. Despite their effectiveness, astir existing approaches trust heavy connected analyzable architectures composed of abstracted components specified arsenic imagination encoders (e.g., CLIP), segmentation networks, and further fusion aliases decoding modules. This reliance connected modular systems increases strategy complexity and limits scalability, particularly erstwhile adapting to caller tasks. Inspired by unified architectures that jointly study ocular and textual features utilizing a azygous transformer, caller efforts person explored much simplified designs that debar outer components while still enabling beardown capacity successful tasks requiring elaborate ocular grounding and connection interaction.

Historically, vision-language models person evolved from contrastive learning approaches, specified arsenic CLIP and ALIGN, progressing toward large-scale models that reside open-ended tasks, including ocular mobility answering and optical characteristic recognition. These models typically fuse imagination and connection features either by injecting connection into ocular transformers aliases by appending segmentation networks to ample connection models. However, specified methods often require intricate engineering and are limited connected nan capacity of individual submodules. Recent investigation has begun to research encoder-free designs that unify image and matter learning wrong a azygous transformer, enabling much businesslike training and inference. These approaches person besides been extended to tasks specified arsenic referring look segmentation and ocular punctual understanding, aiming to support region-level reasoning and relationship without nan request for aggregate specialized components.

Researchers from ByteDance and WHU coming Pixel-SAIL, a single-transformer model designed for pixel-wise multimodal tasks that does not trust connected other imagination encoders. It introduces 3 cardinal innovations: a learnable upsampling module to refine ocular features, a ocular punctual injection strategy that maps prompts into matter tokens, and a imagination master distillation method to heighten disguise quality. Pixel-SAIL is trained connected a substance of referring segmentation, VQA, and ocular punctual datasets. It outperforms larger models, specified arsenic GLaMM (7B) and OMG-LLaVA (7B), connected 5 benchmarks, including nan recently projected PerBench, while maintaining a importantly simpler architecture.

Pixel-SAIL, a elemental yet effective single-transformer exemplary for fine-grained vision-language tasks, eliminates nan request for abstracted imagination encoders. They first creation a plain encoder-free MLLM baseline and place its limitations successful segmentation value and ocular punctual understanding. To flooded these, Pixel-SAIL introduces: (1) a learnable upsampling module for high-res characteristic recovery, (2) a ocular punctual injection method enabling early fusion pinch imagination tokens, and (3) a dense characteristic distillation strategy utilizing master models for illustration Mask2Former and SAM2. They besides present PerBench, a caller benchmark assessing entity captioning, visual-prompt understanding, and V-T RES segmentation crossed 1,500 annotated examples.

The research evaluates nan Pixel-SAIL exemplary connected various benchmarks utilizing modified SOLO and EVEv2 architectures, showing its effectiveness successful segmentation and ocular punctual tasks. Pixel-SAIL importantly outperforms different models, including segmentation specialists, pinch higher cIoU scores connected datasets for illustration RefCOCO and gRefCOCO. Scaling up nan exemplary size from 0.5B to 3B leads to further improvements. Ablation studies uncover that incorporating ocular punctual mechanisms, information scaling, and distillation strategies enhances performance. Visualization study reveals that Pixel-SAIL’s image and disguise features are denser and much diverse, resulting successful improved segmentation results.

In conclusion, Pixel-SAIL, a simplified MLLM for pixel-grounded tasks, achieves beardown capacity without requiring further components specified arsenic imagination encoders aliases segmentation models. The exemplary incorporates 3 cardinal innovations: a learnable upsampling module, a ocular punctual encoding strategy, and imagination master distillation for enhanced characteristic extraction. Pixel-SAIL is evaluated connected 4 referring segmentation benchmarks and a new, challenging benchmark, PerBench, which includes tasks specified arsenic entity description, ocular prompt-based Q&A, and referring segmentation. The results show that Pixel-SAIL performs arsenic good arsenic aliases amended than existing models, pinch a simpler architecture.

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