arXiv subjects

Ignacio M. De la Jara

Publications and source records attributed to Ignacio M. De la Jara.

5 recordsLinked to original sources

arXiv2026

Representation Trajectories Matters: Complementary Evidence for OOD Detection and Image Classification

Vision models do not form a representation at once; each block revises it. We ask whether the resulting computation path contains evidence that the final representation discards, and whether that evidence improves OOD detection and image classification on clean and shifted data. Unlike approaches that treat intermediate layers as separate snapshots, we retain sample identity across depth and study the transformations connecting successive states. We separate class-coherent transport from input-specific innovation, and coordinate movement from relational reorganization. Across supervised, self-supervised, vision--language, hierarchical, and convolutional encoders, these paths show strong sample-specific continuity and architecture-specific depth profiles that recur across datasets. They are also practically useful. An ID-only transition-surprise score complements strong final-state detectors, reducing FPR95 in 131/152 non-saturated comparisons on a balanced OpenOOD grid; gains are largest for visually disruptive and semantically far shifts, and remain positive on near-OOD for most detectors. Frozen update probes improve 71/72 clean model--dataset cases, while shifted-data gains vary with architecture and corruption type. Computation paths therefore provide a broadly useful reliability signal whose value is determined jointly by model organization and the shift encountered.

cs.CV↗

arXiv2026

Queries Knew More Than We Thought: Uncovering Latent Knowledge in Segmentation Models

Modern segmenters often fail after the expensive computation has already been done: a useful mask is present among the model's query-conditioned candidates, but the deployed selection rule does not expose it. We study this output-selection bottleneck in frozen DETR-family models. A ground-truth-only oracle first shows substantial hidden headroom in already-computed mask proposals. This raises a simple question: How can we better use the masks a segmenter has already computed but does not expose? We then ask whether that headroom can be recovered without adding queries, generating new masks, rerunning the backbone, or updating weights. HYDRA is a small selector trained only on cached frozen outputs. At inference time, it scores the cached candidates against an explicit keep-baseline option and acts only when a held-out calibrated margin indicates the selected candidate is sufficiently better. Trained on training-split caches and calibrated on held-out data, HYDRA improves Mask2Former, MaskDINO, and OneFormer by up to +7.41 dataset mIoU points on ADE20k and COCO, and improves SAM 3 by +9.4 class-macro prompt-IoU points on average across eight domains while preserving useful predictions through calibration. Paired LoRA controls show that lightweight weight adaptation does not remove the bottleneck: exposed predictions are often flat or worse, while routing over the adapted candidates still recovers accuracy. Finally, we connect the effect to query specialization under bipartite matching and verify it in a controlled TinyDETR study. These results show that frozen segmenters should be evaluated not only by the masks they expose, but also by the useful candidates they suppress.

cs.CV↗

arXiv2026

Not All Layers Are Equal: Dynamic Layer Routing for Reliable CLIP OOD Detection

Information aggregation across model layers are revealed to improve OOD detection. In contrast to crafting a method for layer-wise information aggregation in recent work, we investigate if layer selection is a learnable problem. In other words, we transpose the question from how to fuse layers to one asking which layers to trust for an input. Using a generalizable, weak, out of distribution context crafting approach for supervision, shown to be more effective than state of the art methods' mechanisms, we formulate learning a lightweight router to select a sparse, final-layer-anchored expert over CLIP's layer depth for OOD detection. Across three diverse benchmarks we demonstrate our learnable routing method dubbed Voyager improves OOD detection. On ImageNet-1K, Voyager achieves an average FPR@95 of 18.86, outperforming the strongest, comparable, prompt-learning method by 8.8 points. These gains persist across multiple supervision sources, including those used by existing state-of-the-art prompt-learning methods, demonstrating that, whilst our weak OOD supervision context is highly effective, the key advantage is realized from the learnable router component rather than the supervision source. Significantly, Voyager is highly practical; router learning takes approximately two minutes using less than 1 GB of memory, making it approximately 20x more efficient than current prompt-learning approaches. Anonymized Code: https://anonymous.4open.science/r/Voyager/

cs.CV↗

arXiv2026

Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer

Selecting a zero-shot out-of-distribution (OOD) detector for a new deployment is typically based on benchmark rankings, implicitly assuming that the highest-ranked detector will transfer across domains. We show that this assumption does not hold. Through a controlled portability audit across seventeen in-distribution datasets, three vision-language models, and seven representative zero-shot OOD detectors, we find that detector rankings reverse across deployments, every detector exceeds $80\%$ FPR95 on at least one domain, and the preferred detector depends on both the in-distribution data and the underlying VLM. We trace these reversals to complementary evidence channels in vision-language logits. Corpus-free detectors rely on different combinations of absolute match level and relative or spatial sharpness, while WordNet-based methods additionally depend on external semantic coverage. A simple proposition shows that level and sharpness cannot generally be recovered from one another, explaining why no single detector transfers reliably across deployments. Motivated by this diagnosis, we introduce the Complementary Evidence Guard (CEG), a detector-agnostic wrapper that preserves complementary evidence through a non-compensatory fusion of the base detector, level, and sharpness using only empirical in-distribution percentiles. Controls replacing these channels with entropy, logit variance, or random noise do not reproduce the gains. Without OOD samples, auxiliary corpora, or learned fusion, CEG reduces detector sensitivity and improves GL-MCM from $38.1$ to $28.8$ and MCM from $42.6$ to $30.5$ family-balanced FPR95.

cs.CV↗

arXiv2025

An empirical study of the effect of video encoders on Temporal Video Grounding

Temporal video grounding is a fundamental task in computer vision, aiming to localize a natural language query in a long, untrimmed video. It has a key role in the scientific community, in part due to the large amount of video generated every day. Although we find extensive work in this task, we note that research remains focused on a small selection of video representations, which may lead to architectural overfitting in the long run. To address this issue, we propose an empirical study to investigate the impact of different video features on a classical architecture. We extract features for three well-known benchmarks, Charades-STA, ActivityNet-Captions and YouCookII, using video encoders based on CNNs, temporal reasoning and transformers. Our results show significant differences in the performance of our model by simply changing the video encoder, while also revealing clear patterns and errors derived from the use of certain features, ultimately indicating potential feature complementarity.

cs.CV↗