Ziqi Wen

Parsa Madinei, Ryan Solgi, Ziqi Wen et al.

We introduce INTERLACE, a novel framework that prunes redundant layers in VLMs while maintaining performance through sample-efficient finetuning. Existing layer pruning methods lead to significant performance drop when applied to VLMs. Instead, we analyze triplets of consecutive layers to identify local redundancy, removing the most redundant of the first two layers, finetune the remaining layer to compensate for the lost capacity, and freeze the third layer to serve as a stable anchor during finetuning. We found that this interleaved finetune-freeze design enables rapid convergence with minimal data after pruning. By finetuning only a subset of layers on just 1% of the FineVision dataset for one epoch, Interlace achieves 88.9% average performance retention after dropping 25% of the network, achieving SOTA performance. Our code is available at: https://github.com/pmadinei/Interlace.git

Tianqin Li, Ziqi Wen, Leiran Song et al.

Human vision organizes local cues into coherent global forms using Gestalt principles like closure, proximity, and figure-ground assignment -- functions reliant on global spatial structure. We investigate whether modern vision models show similar behaviors, and under what training conditions these emerge. We find that Vision Transformers (ViTs) trained with Masked Autoencoding (MAE) exhibit activation patterns consistent with Gestalt laws, including illusory contour completion, convexity preference, and dynamic figure-ground segregation. To probe the computational basis, we hypothesize that modeling global dependencies is necessary for Gestalt-like organization. We introduce the Distorted Spatial Relationship Testbench (DiSRT), which evaluates sensitivity to global spatial perturbations while preserving local textures. Using DiSRT, we show that self-supervised models (e.g., MAE, CLIP) outperform supervised baselines and sometimes even exceed human performance. ConvNeXt models trained with MAE also exhibit Gestalt-compatible representations, suggesting such sensitivity can arise without attention architectures. However, classification finetuning degrades this ability. Inspired by biological vision, we show that a Top-K activation sparsity mechanism can restore global sensitivity. Our findings identify training conditions that promote or suppress Gestalt-like perception and establish DiSRT as a diagnostic for global structure sensitivity across models.

Ziqi Wen, Jonathan Skaza, Shravan Murlidaran et al.

Although models exist that predict human response times (RTs) in tasks such as target search and visual discrimination, the development of image-computable predictors for scene understanding time remains an open challenge. Recent advances in vision-language models (VLMs), which can generate scene descriptions for arbitrary images, combined with the availability of quantitative metrics for comparing linguistic descriptions, offer a new opportunity to model human scene understanding. We hypothesize that the primary bottleneck in human scene understanding and the driving source of variability in response times across scenes is the interaction between the foveated nature of the human visual system and the spatial distribution of task-relevant visual information within an image. Based on this assumption, we propose a novel image-computable model that integrates foveated vision with VLMs to produce a spatially resolved map of scene understanding as a function of fixation location (Foveated Scene Understanding Map, or F-SUM), along with an aggregate F-SUM score. This metric correlates with average (N=17) human RTs (r=0.47) and number of saccades (r=0.51) required to comprehend a scene (across 277 scenes). The F-SUM score also correlates with average (N=16) human description accuracy (r=-0.56) in time-limited presentations. These correlations significantly exceed those of standard image-based metrics such as clutter, visual complexity, and scene ambiguity based on language entropy. Together, our work introduces a new image-computable metric for predicting human response times in scene understanding and demonstrates the importance of foveated visual processing in shaping comprehension difficulty.