Large vision-language models (VLMs) commonly process images at native or high resolution to remain effective across tasks.This inflates visual tokens up to to 99% of total tokens of the prefill stage, resulting in high compute and latency, even when lowresolution images would suffice.We introduce CARES-a Context-Aware Resolution Selector, a lightweight preprocessing module that, given an image-query pair, predicts the minimal sufficient input resolution.CARES uses a compact VLM (350M) to extract features and predict when a target pretrained VLM's response converges to its peak ability to answer correctly.Though trained as a discrete classifier over a set of optional resolutions, CARES interpolates continuous resolutions at inference for fine-grained control.Across nine multimodal benchmarks spanning documents and natural images, as well as diverse target VLMs, CARES preserves task performance while reducing compute by up to 78% on average across 9 benchmarks.

Image reconstruction from corrupted images is crucial across many domains. Most reconstruction networks are trained on post-ISP sRGB images, even though the image-signal-processing pipeline irreversibly mixes colors, clips dynamic range and blurs fine detail. This paper uses the rain degradation problem as a "use case" to show that these losses are avoidable and show that learning directly on raw Bayer mosaics yields superior reconstructions. To substantiate the claim we (i) evaluate post-ISP and Bayer reconstruction pipelines, (ii) curate Raw-Rain, the first public benchmark of real rainy scenes captured in both 12-bit Bayer and bit-depth-matched sRGB, and (iii) introduce Information Conservation Score (ICS), a color-invariant metric that aligns more closely with human opinion than PSNR or SSIM. On the test split our raw-domain model improves sRGB results by up to +0.99 dB PSNR and +1.2 % ICS, while running faster with lower GFLOPs. The results advocate an ISP-last paradigm for low-level vision and open the door to end-to-end learnable camera pipelines.

The ability of robots to manipulate objects relies heavily on their aptitude for visual perception. In domains charac-terized by cluttered scenes and high object variability such as traffic, navigation and object grasping, most methods call for vast labeled datasets, laboriously hand-annotated, with the aim of training capable models. Once deployed, the challenge of generalizing to unfamiliar objects implies that the model must evolve alongside its domain. To address this, we propose a novel framework that combines Semi-Supervised Learning (SSL) with Learning Through Interaction (LTI), allowing a model to learn by observing scene alterations and leverage visual consistency despite tempo-ral gaps without requiring curated data of interaction se-quences. As a result, our approach exploits partially anno-tated data through self-supervision and incorporates temporal context using pseudo-sequences generated from unla-beled still images. We validate our method on two common benchmarks, ARMBench mix-object-tote and OCID, where it achieves state-of-the-art performance. Notably, on ARM-Bench, we attain an AP50 of 86.37, almost a 20% improvement over existing work, and obtain remarkable results in scenarios with extremely low annotation, achieving an AP50score of 84.89 with just 1 % of annotated data compared to previous state of the art of 82 which targeted the fully anno-tated dataset.