Visual Relationship Detection (VRD) has significantly advanced with Transformer-based architectures. However, we identify two fundamental drawbacks in conventional label assignment methods used for training Transformer-based VRD models, where ground-truth (GT) annotations are matched to model predictions. In conventional assignment, queries are trained to detect all relations rather than specializing in specific ones, resulting in ‘unspecialized’ queries. Also, each ground-truth (GT) annotation is assigned to only one prediction under conventional assignment, suppressing other near-correct predictions by labeling them as ‘no relation’. To address these issues, we introduce a novel method called Groupwise Query Spe ci a lization and Q uality-Aware Multi-Assignment (SpeaQ). Groupwise Query Specialization clusters queries and relations into exclusive groups, promoting specialization by assigning a set of relations only to a corresponding query group. Quality-Aware Multi-Assignment enhances training signals by allowing multiple predictions closely matching the GT to be positively assigned. Additionally, we introduce dynamic query reallocation, which transfers queries from high- to low-performing groups for balanced training. Experimental results demonstrate that SpeaQ+, combining SpeaQ with dynamic query reallocation, consistently improves performance across seven baseline models on five benchmarks without additional inference cost.

Recent works have demonstrated the effectiveness of reinforcement learning (RL)-based post-training for enhancing the reasoning capabilities of large language models (LLMs). In particular, Group Relative Policy Optimization (GRPO) has shown impressive success using a PPO-style reinforcement learning algorithm with group-normalized rewards. However, the effectiveness of GRPO in Video Large Language Models (VideoLLMs) remains underexplored. In this paper, we explore GRPO and identify two issues that hinder effective learning: (1) reliance on safeguards, and (2) vanishing advantage. To mitigate these challenges, we propose DeepVideo-R1, a video large language model trained with Reg-GRPO (Regressive GRPO) and difficulty-aware data augmentation. Reg-GRPO reformulates the GRPO loss function as a regression task that directly predicts the advantage in GRPO, eliminating the need for safeguards such as clipping and min operations. This directly aligns the model with the advantages, providing guidance to prefer better outputs. The difficulty-aware data augmentation strategy augments input prompts/videos to target solvable difficulty levels, enabling diverse reward signals. Our experimental results show that our approach significantly improves video reasoning performance across multiple benchmarks.

Visual Relationship Detection (VRD) has seen significant advancements with Transformer-based architectures recently. However, we identify two key limitations in a conventional label assignment for training Transformer-based VRD models, which is a process of mapping a ground-truth (GT) to a prediction. Under the conventional assignment, an ‘unspecialized’ query is trained since a query is expected to detect every relation, which makes it difficult for a query to specialize in specific relations. Furthermore, a query is also insufficiently trained since a GT is assigned only to a single prediction, therefore near-correct or even correct predictions are suppressed by being assigned ‘no relation (⊘)‘ as a GT. To address these issues, we propose Groupwise Query Specialization and Quality-Aware Multi-Assignment (SpeaQ). Groupwise Query Specialization trains a ‘specialized’ query by dividing queries and relations into disjoint groups and directing a query in a specific query group solely toward relations in the corresponding relation group. Quality-Aware Multi-Assignment further facilitates the training by assigning a GT to multiple predictions that are significantly close to a GT in terms of a subject, an object, and the relation in between. Experimental results and analyses show that SpeaQ effectively trains ‘specialized’ queries, which better utilize the capacity of a model, resulting in consistent performance gains with ‘zero’ additional inference cost across multiple VRD models and benchmarks. Code is available at https://github.com/m1vlab/SpeaQ.