World In Your Hands: A Large-Scale and Open-source Ecosystem for Learning Human-centric Manipulation in the Wild

Large-scale pre-training is fundamental for generalization in language and vision models, but data for dexterous hand manipulation remains limited in scale and diversity, hindering policy generalization. Limited scenario diversity, misaligned modalities, and insufficient benchmarking constrain current human manipulation datasets. To address these gaps, we introduce World In Your Hands (WiYH), a large-scale open-source ecosystem for human-centric manipulation learning. WiYH includes (1) the Oracle Suite, a wearable data collection kit with an auto-labeling pipeline for accurate motion capture; (2) the WiYH Dataset, featuring over 1,000 hours of multi-modal manipulation data across hundreds of skills in diverse real-world scenarios; and (3) extensive annotations and benchmarks supporting tasks from perception to action. Furthermore, experiments based on the WiYH ecosystem show that integrating WiYH's human-centric data significantly enhances the generalization and robustness of dexterous hand policies in tabletop manipulation tasks. We believe that World In Your Hands will bring new insights into human-centric data collection and policy learning to the community.

RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion

Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. TKG completion involves forecasting missing or future links, requiring models to reason over time-evolving structure. While LLMs show promise for this task, existing approaches often overemphasize supervised fine-tuning and struggle particularly when historical evidence is limited or missing. We introduce RECIPE-TKG, a lightweight and data-efficient framework designed to improve accuracy and generalization in settings with sparse historical context. It combines (1) rule-based multi-hop retrieval for structurally diverse history, (2) contrastive fine-tuning of lightweight adapters to encode relational semantics, and (3) test-time semantic filtering to iteratively refine generations based on embedding similarity. Experiments on four TKG benchmarks show that RECIPE-TKG outperforms previous LLM-based approaches, achieving up to 30.6\% relative improvement in Hits@10. Moreover, our proposed framework produces more semantically coherent predictions, even for the samples with limited historical context.

Sample-Efficient Behavior Cloning Using General Domain Knowledge

Behavior cloning has shown success in many sequential decision-making tasks by learning from expert demonstrations, yet they can be very sample inefficient and fail to generalize to unseen scenarios. One approach to these problems is to introduce general domain knowledge, such that the policy can focus on the essential features and may generalize to unseen states by applying that knowledge. Although this knowledge is easy to acquire from the experts, it is hard to be combined with learning from individual examples due to the lack of semantic structure in neural networks and the time-consuming nature of feature engineering. To enable learning from both general knowledge and specific demonstration trajectories, we use a large language model's coding capability to instantiate a policy structure based on expert domain knowledge expressed in natural language and tune the parameters in the policy with demonstrations. We name this approach the Knowledge Informed Model (KIM) as the structure reflects the semantics of expert knowledge. In our experiments with lunar lander and car racing tasks, our approach learns to solve the tasks with as few as 5 demonstrations and is robust to action noise, outperforming the baseline model without domain knowledge. This indicates that with the help of large language models, we can incorporate domain knowledge into the structure of the policy, increasing sample efficiency for behavior cloning.

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning

Currently, training large-scale deep learning models is typically achieved through parallel training across multiple GPUs. However, due to the inherent communication overhead and synchronization delays in traditional model parallelism methods, seamless parallel training cannot be achieved, which, to some extent, affects overall training efficiency. To address this issue, we present PPLL (Pipeline Parallelism based on Local Learning), a novel framework that leverages local learning algorithms to enable effective parallel training across multiple GPUs. PPLL divides the model into several distinct blocks, each allocated to a separate GPU. By utilizing queues to manage data transfers between GPUs, PPLL ensures seamless cross-GPU communication, allowing multiple blocks to execute forward and backward passes in a pipelined manner. This design minimizes idle times and prevents bottlenecks typically caused by sequential gradient updates, thereby accelerating the overall training process. We validate PPLL through extensive experiments using ResNet and Vision Transformer (ViT) architectures on CIFAR-10, SVHN, and STL-10 datasets. Our results demonstrate that PPLL significantly enhances the training speed of the local learning method while achieving comparable or even superior training speed to traditional pipeline parallelism (PP) without sacrificing model performance. In a 4-GPU training setup, PPLL accelerated local learning training on ViT and ResNet by 162% and 33%, respectively, achieving 1.25x and 0.85x the speed of traditional pipeline parallelism.

LAKD-Activation Mapping Distillation Based on Local Learning

Knowledge distillation is widely applied in various fundamental vision models to enhance the performance of compact models. Existing knowledge distillation methods focus on designing different distillation targets to acquire knowledge from teacher models. However, these methods often overlook the efficient utilization of distilled information, crudely coupling different types of information, making it difficult to explain how the knowledge from the teacher network aids the student network in learning. This paper proposes a novel knowledge distillation framework, Local Attention Knowledge Distillation (LAKD), which more efficiently utilizes the distilled information from teacher networks, achieving higher interpretability and competitive performance. The framework establishes an independent interactive training mechanism through a separation-decoupling mechanism and non-directional activation mapping. LAKD decouples the teacher's features and facilitates progressive interaction training from simple to complex. Specifically, the student network is divided into local modules with independent gradients to decouple the knowledge transferred from the teacher. The non-directional activation mapping helps the student network integrate knowledge from different local modules by learning coarse-grained feature knowledge. We conducted experiments on the CIFAR-10, CIFAR-100, and ImageNet datasets, and the results show that our LAKD method significantly outperforms existing methods, consistently achieving state-of-the-art performance across different datasets.

HPFF: Hierarchical Locally Supervised Learning with Patch Feature Fusion

Traditional deep learning relies on end-to-end backpropagation for training, but it suffers from drawbacks such as high memory consumption and not aligning with biological neural networks. Recent advancements have introduced locally supervised learning, which divides networks into modules with isolated gradients and trains them locally. However, this approach can lead to performance lag due to limited interaction between these modules, and the design of auxiliary networks occupies a certain amount of GPU memory. To overcome these limitations, we propose a novel model called HPFF that performs hierarchical locally supervised learning and patch-level feature computation on the auxiliary networks. Hierarchical Locally Supervised Learning (HiLo) enables the network to learn features at different granularity levels along their respective local paths. Specifically, the network is divided into two-level local modules: independent local modules and cascade local modules. The cascade local modules combine two adjacent independent local modules, incorporating both updates within the modules themselves and information exchange between adjacent modules. Patch Feature Fusion (PFF) reduces GPU memory usage by splitting the input features of the auxiliary networks into patches for computation. By averaging these patch-level features, it enhances the network's ability to focus more on those patterns that are prevalent across multiple patches. Furthermore, our method exhibits strong generalization capabilities and can be seamlessly integrated with existing techniques. We conduct experiments on CIFAR-10, STL-10, SVHN, and ImageNet datasets, and the results demonstrate that our proposed HPFF significantly outperforms previous approaches, consistently achieving state-of-the-art performance across different datasets. Our code is available at: https://github.com/Zeudfish/HPFF.

Momentum Auxiliary Network for Supervised Local Learning

Deep neural networks conventionally employ end-to-end backpropagation for their training process, which lacks biological credibility and triggers a locking dilemma during network parameter updates, leading to significant GPU memory use. Supervised local learning, which segments the network into multiple local blocks updated by independent auxiliary networks. However, these methods cannot replace end-to-end training due to lower accuracy, as gradients only propagate within their local block, creating a lack of information exchange between blocks. To address this issue and establish information transfer across blocks, we propose a Momentum Auxiliary Network (MAN) that establishes a dynamic interaction mechanism. The MAN leverages an exponential moving average (EMA) of the parameters from adjacent local blocks to enhance information flow. This auxiliary network, updated through EMA, helps bridge the informational gap between blocks. Nevertheless, we observe that directly applying EMA parameters has certain limitations due to feature discrepancies among local blocks. To overcome this, we introduce learnable biases, further boosting performance. We have validated our method on four image classification datasets (CIFAR-10, STL-10, SVHN, ImageNet), attaining superior performance and substantial memory savings. Notably, our method can reduce GPU memory usage by more than 45\% on the ImageNet dataset compared to end-to-end training, while achieving higher performance. The Momentum Auxiliary Network thus offers a new perspective for supervised local learning. Our code is available at: https://github.com/JunhaoSu0/MAN.

MLAAN: Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network

End-to-end (E2E) training approaches are commonly plagued by high memory consumption, reduced efficiency in training, challenges in model parallelization, and suboptimal biocompatibility. Local learning is considered a novel interactive training method that holds promise as an alternative to E2E. Nonetheless, conventional local learning methods fall short in achieving high model accuracy due to inadequate local inter-module interactions. In this paper, we introduce a new model known as the Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network (MLAAN). MLAAN features an innovative supervised local learning approach coupled with a robust reinforcement module. This dual-component design enables the MLAAN to integrate smoothly with established local learning techniques, thereby enhancing the efficacy of the foundational methods. The method simultaneously acquires the local and global features of the model separately by constructing an independent auxiliary network and a cascade auxiliary network on the one hand and incorporates a leap augmented module, which serves to counteract the reduced learning capacity often associated with weaker supervision. This architecture not only augments the exchange of information amongst the local modules but also effectively mitigates the model's tendency toward myopia. The experimental evaluations conducted on four benchmark datasets, CIFAR-10, STL-10, SVHN, and ImageNet, demonstrate that the integration of MLAAN with existing supervised local learning methods significantly enhances the original methodologies. Of particular note, MLAAN enables local learning methods to comprehensively outperform end-to-end training approaches in terms of optimal performance while saving GPU memory.

GLCAN: Global-Local Collaborative Auxiliary Network for Local Learning

Traditional deep neural networks typically use end-to-end backpropagation, which often places a big burden on GPU memory. Another promising training method is local learning, which involves splitting the network into blocks and training them in parallel with the help of an auxiliary network. Local learning has been widely studied and applied to image classification tasks, and its performance is comparable to that of end-to-end method. However, different image tasks often rely on different feature representations, which is difficult for typical auxiliary networks to adapt to. To solve this problem, we propose the construction method of Global-Local Collaborative Auxiliary Network (GLCAN), which provides a macroscopic design approach for auxiliary networks. This is the first demonstration that local learning methods can be successfully applied to other tasks such as object detection and super-resolution. GLCAN not only saves a lot of GPU memory, but also has comparable performance to an end-to-end approach on data sets for multiple different tasks.

Listen, Disentangle, and Control: Controllable Speech-Driven Talking Head Generation

Most earlier investigations on talking face generation have focused on the synchronization of lip motion and speech content. However, human head pose and facial emotions are equally important characteristics of natural human faces. While audio-driven talking face generation has seen notable advancements, existing methods either overlook facial emotions or are limited to specific individuals and cannot be applied to arbitrary subjects. In this paper, we propose a one-shot Talking Head Generation framework (SPEAK) that distinguishes itself from general Talking Face Generation by enabling emotional and postural control. Specifically, we introduce the Inter-Reconstructed Feature Disentanglement (IRFD) method to decouple human facial features into three latent spaces. We then design a face editing module that modifies speech content and facial latent codes into a single latent space. Subsequently, we present a novel generator that employs modified latent codes derived from the editing module to regulate emotional expression, head poses, and speech content in synthesizing facial animations. Extensive trials demonstrate that our method can generate realistic talking head with coordinated lip motions, authentic facial emotions, and smooth head movements. The demo video is available at the anonymous link: https://anonymous.4open.science/r/SPEAK-F56E