The ability to model interactions among agents is crucial for effective coordination and understanding their cooperation mechanisms in multi-agent reinforcement learning (MARL). However, previous efforts to model high-order interactions have been primarily hindered by the combinatorial explosion or the opaque nature of their black-box network structures. In this paper, we propose a novel value decomposition framework, called Continued Fraction Q-Learning (QCoFr), which can flexibly capture arbitrary-order agent interactions with only linear complexity $\mathcal{O}\left({n}\right)$ in the number of agents, thus avoiding the combinatorial explosion when modeling rich cooperation. Furthermore, we introduce the variational information bottleneck to extract latent information for estimating credits. This latent information helps agents filter out noisy interactions, thereby significantly enhancing both cooperation and interpretability. Extensive experiments demonstrate that QCoFr not only consistently achieves better performance but also provides interpretability that aligns with our theoretical analysis.

This paper presents an Open-Structure Table Extraction (OpenTE) task, which aims to extract a table with intrinsic semantic, calculational, and hierarchical structure from unstructured text. We devise a novel Identification-Extraction-Grounding (IEG) framework for language models (LMs) comprising three chaining steps: (1) identifying semantic and calculational relationships among columns, (2) extracting structured data from unstructured text, and (3) aligning extracted data with the source text and the table structure with a separate discrete grounding model. Experiment results suggest that OpenTE presents a significant challenge for state-of-the-art LMs and demonstrate that the IEG framework achieves superior performance on both datasets, with over 9% F1 improvements in the few-shot setting for GPT-3.5&4 and other large language models (LLMs) and over 4.9% F1 enhancements in the fine-tuning setting for open-source BART. We’ll release the dataset to facilitate future research.

Recent years have witnessed the great progress of Visual Object Tracking (VOT) which aims to predict the position of an object in each video frame given only its initial appearance. However, even the state-of-the-art methods are confronted with performance degradation, i.e., the tracker drift problem, in sports video scenes (e.g., soccer, basketball). There are two main causes that should be responsible for the tracker drift problem. First, the object of interest is often occluded by other objects that share a similar appearance. Such severe occlusion prevents the model from distinguishing the correct tracking object from other distractors in the future frames. Second, in sports videos, the objects often move fast from one place to another, which incurs severe blurry visual effects among consecutive frames. To address the issues of the tracker drift problem, we treat VOT as a tracking-by-re-detection task. Specifically, we detect candidate objects within a searching area (determined by object location in the previous frame) in the current frame and develop a progressive algorithm to filter out distractors in the area, which proves robust towards occlusion scenarios and tracker drift problems. Combining the advantages of our settings, the proposed framework method is robust to motion blur and object occlusion issues and achieves state-of-the-art tracking results on our challenging dataset.

A novel bimodal emotion recognition approach from facial expression and speech based on the sparse kernel reduced-rank regression (SKRRR) fusion method is proposed in this paper. In this method, we use the openSMILE feature extractor and the scale invariant feature transform feature descriptor to respectively extract effective features from speech modality and facial expression modality, and then propose the SKRRR fusion approach to fuse the emotion features of two modalities. The proposed SKRRR method is a nonlinear extension of the traditional reduced-rank regression (RRR), where both predictor and response feature vectors in RRR are kernelized by being mapped onto two high-dimensional feature space via two nonlinear mappings, respectively. To solve the SKRRR problem, we propose a sparse representation (SR)-based approach to find the optimal solution of the coefficient matrices of SKRRR, where the introduction of the SR technique aims to fully consider the different contributions of training data samples to the derivation of optimal solution of SKRRR. Finally, we utilize the eNTERFACE '05 and AFEW 4.0 bimodal emotion database to conduct the experiments of monomodal emotion recognition and bimodal emotion recognition, and the results indicate that our presented approach acquires the highest or comparable bimodal emotion recognition rate among some state-of-the-art approaches.