arXiv subjects

Hani Alomari

Publications and source records attributed to Hani Alomari.

5 recordsLinked to original sources

arXiv2026

Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning

Multimodal large language models (MLLMs) ground whole objects well from free-form language queries, but they struggle when the query names a part rather than the object. We trace this to a missing object-part hierarchy, since parts are localized in the same single step used for objects. We propose Object-Part Hierarchical Reflective Grounding (OP-HRG), a coarse-to-fine reasoning-guided grounding strategy that first localizes the parent object and then the part within it. A self-check then reflects on the result, with an extension to re-encode the predicted crop to inspect the region it is correcting. We introduce a part-aware GRPO framework to train our pipeline with stage-wise rewards. A 4B model trained this way outperforms 7B grounding LLMs and SAM3 across PascalPart, PartImageNet, and InstructPart, and transfers to reasoning segmentation.

cs.CV↗

arXiv2026

NEST: Narrative Event Structures in Time for Long Video Understanding

Recent progress in vision-language models has enabled processing of increasingly long video sequences, but handling extended token streams does not translate to understanding complex narrative structure in long videos. Existing long-video benchmarks focus on needle-in-a-haystack retrieval rather than evaluating how low-level actions form events, interact across time, and drive narratives, for example whether a model can connect an early job loss to a later relationship breakup despite intervening scenes or flashbacks. We introduce NEST (Narrative Event Structures in Time for Long Video Understanding), a dataset of 1,005 full-length movies (avg. 98 minutes), each annotated with about 103 multimodal narrative events grounded in visual content, dialogue, and audio. NEST links these events through relations that reflect narrative structure, including explicit temporal ordering, hierarchical composition, and long-range dependencies. We introduce baselines for event trigger detection (ETD), event localization (EL), event argument extraction (EAE), and event relation extraction (ERE). The benchmark is highly challenging for grounded event discovery, with the best performance across evaluated zero-shot models on main evaluation subsets remaining below 8% for ETD, 6% for EL, and 11% for EAE. In contrast, ERE is more tractable once events are given, reaching 34.23% F1 zero-shot and 49.20% F1 after fine-tuning.

cs.CV↗

arXiv2025

Maximal Matching Matters: Preventing Representation Collapse for Robust Cross-Modal Retrieval

Cross-modal image-text retrieval is challenging because of the diverse possible associations between content from different modalities. Traditional methods learn a single-vector embedding to represent semantics of each sample, but struggle to capture nuanced and diverse relationships that can exist across modalities. Set-based approaches, which represent each sample with multiple embeddings, offer a promising alternative, as they can capture richer and more diverse relationships. In this paper, we show that, despite their promise, these set-based representations continue to face issues including sparse supervision and set collapse, which limits their effectiveness. To address these challenges, we propose Maximal Pair Assignment Similarity to optimize one-to-one matching between embedding sets which preserve semantic diversity within the set. We also introduce two loss functions to further enhance the representations: Global Discriminative Loss to enhance distinction among embeddings, and Intra-Set Divergence Loss to prevent collapse within each set. Our method achieves state-of-the-art performance on MS-COCO and Flickr30k without relying on external data.

cs.CV↗

arXiv2025

ENTER: Event Based Interpretable Reasoning for VideoQA

In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships (temporal/causal/hierarchical) form the edges. This structured representation offers many benefits: 1) Interpretable VideoQA via generated code that parses event-graph; 2) Incorporation of contextual visual information in the reasoning process (code generation) via event graphs; 3) Robust VideoQA via Hierarchical Iterative Update of the event graphs. Existing interpretable VideoQA systems are often top-down, disregarding low-level visual information in the reasoning plan generation, and are brittle. While bottom-up approaches produce responses from visual data, they lack interpretability. Experimental results on NExT-QA, IntentQA, and EgoSchema demonstrate that not only does our method outperform existing top-down approaches while obtaining competitive performance against bottom-up approaches, but more importantly, offers superior interpretability and explainability in the reasoning process.

cs.CV↗

arXiv2025

JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images

Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perform well with only a shallow visual understanding by relying on background language biases. Thus, strong performance on these benchmarks does not necessarily correlate with strong visual understanding. In this paper, we release JourneyBench, a comprehensive human-annotated benchmark of generated images designed to assess the model's fine-grained multimodal reasoning abilities across five tasks: complementary multimodal chain of thought, multi-image VQA, imaginary image captioning, VQA with hallucination triggers, and fine-grained retrieval with sample-specific distractors. Unlike existing benchmarks, JourneyBench explicitly requires fine-grained multimodal reasoning in unusual imaginary scenarios where language bias and holistic image gist are insufficient. We benchmark state-of-the-art models on JourneyBench and analyze performance along a number of fine-grained dimensions. Results across all five tasks show that JourneyBench is exceptionally challenging for even the best models, indicating that models' visual reasoning abilities are not as strong as they first appear. We discuss the implications of our findings and propose avenues for further research.

cs.CV↗