Egocentric visual grounding requires high-resolution inputs to localize small objects. However, scaling Multimodal Large Language Models to this domain is constrained by the excessive cost of visual token processing. We identify that current efficient strategies based on token reduction are unreliable for selecting object-centric spatial evidence. To overcome this, we propose SmartRes, a framework that performs efficiency optimization in the pixel space via dynamic resolution routing. SmartRes first encodes a low-resolution view for global context and uses a lightweight router to activate high-resolution patches in object-centric regions and constructs an order-preserving visual sequence. To further enable robust routing under severe foreground-background imbalance, we introduce a margin-regularized routing objective that increases foreground-background logit separation and improves foreground recall. Experiments on Ego4D and EgoIntention show that SmartRes reduces visual tokens by up to 67% while retaining 86.4% of full-resolution performance, and achieves up to 1.66X faster inference than state-of-the-art token reduction methods with higher accuracy. Furthermore, strong performance on small object grounding indicates the effectiveness of SmartRes towards egocentric applications. Code will be publicly available.
H. Sun, Wangbo Zhao, Fanyue Wei et al.· 0 citations
Large-scale pre-trained Vision-Language Models (VLMs) have demonstrated remarkable performance across various visual and multimodal tasks. However, deploying these models on downstream application platforms remains challenging due to computational demands and domain gaps. Quantization offers a promising solution by significantly reducing these costs, making VLMs more feasible for deployment in such environments. There are two prevailing paradigms: Quantization-Aware Training (QAT), which preserves model performance but incurs substantial training costs; and Post-Training Quantization (PTQ), which offers greater efficiency but introduces multimodal gaps and leads to performance degradation on downstream tasks. To reduce computational costs and bridge domain gaps, we propose the “Prompt for Quantization” (P4Q) by integrating PTQ with Parameter-Efficient Fine-Tuning (PEFT) techniques. P4Q compresses model parameters and activations via PTQ, introducing learnable prompts and low-bit adapters to enhance performance on downstream tasks. The learnable prompts embed downstream knowledge to mitigate domain gaps, while the low-bit adapters realign the distributions of image and text features, thereby mitigating multimodal gaps. We also introduce a distillation loss based on cosine similarity predictions to distill the quantized model using a full-precision teacher model. Extensive experiments on thirteen datasets demonstrate that P4Q significantly enhances the performance of low-bit CLIP while reducing deployment costs. For instance, an 8-bit P4Q compressed CLIP-ViT/B-32 achieves 66.94% Top-1 accuracy on ImageNet, surpassing the prompt fine-tuned full-precision counterpart by 2.24% while reducing model size by 4 \(\times\) . The source code is publicly available at https://github.com/HuixinSun/P4Q_official.
H. Sun, Runqi Wang, Yanjing Li et al.· ACM Transactions on Multimed...· 0 citations
