Spiking Neural Networks promise brain-inspired and energy-efficient computation by transmitting information through binary (0/1) spikes. Yet, their performance still lags behind that of artificial neural networks, often assumed to result from information loss caused by sparse and binary activations. In this work, we challenge this long-standing assumption and reveal a previously overlooked frequency bias: **spiking neurons inherently suppress high-frequency components and preferentially propagate low-frequency information.** This frequency-domain imbalance, we argue, is the root cause of degraded feature representation in SNNs. Empirically, on Spiking Transformers, adopting Avg-Pooling (low-pass) for token mixing lowers performance to 76.73% on Cifar-100, whereas replacing it with Max-Pool (high-pass) pushes the top-1 accuracy to 79.12%. Accordingly, we introduce **Max-Former** that restores high-frequency signals through two frequency-enhancing operators: (1) extra Max-Pool in patch embedding, and (2) Depth-Wise Convolution in place of self-attention. Notably, **Max-Former** attains 82.39% top-1 accuracy on ImageNet using only 63.99M parameters, surpassing Spikformer (74.81%, 66.34M) by +7.58%. Extending our insight beyond transformers, our **Max-ResNet-18** achieves state-of-the-art performance on convolution-based benchmarks: 97.17% on CIFAR-10 and 83.06% on CIFAR-100. We hope this simple yet effective solution inspires future research to explore the distinctive nature of spiking neural networks. Code is available: https://github.com/bic-L/MaxFormer.

Articulated objects are ubiquitous in daily life. In this paper, we present DexSim2Real$^{\mathbf{2}}$, a novel framework for goal-conditioned articulated object manipulation. The core of our framework is constructing an explicit world model of unseen articulated objects through active interactions, which enables sampling-based model predictive control to plan trajectories achieving different goals without requiring demonstrations or RL. It first predicts an interaction using an affordance network trained on self-supervised interaction data or videos of human manipulation. After executing the interactions on the real robot to move the object parts, we propose a novel modeling pipeline based on 3D AIGC to build a digital twin of the object in simulation from multiple frames of observations. For dexterous hands, we utilize eigengrasp to reduce the action dimension, enabling more efficient trajectory searching. Experiments validate the framework's effectiveness for precise manipulation using a suction gripper, a two-finger gripper and two dexterous hands. The generalizability of the explicit world model also enables advanced manipulation strategies like manipulating with tools.

Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy both cumulative and state- wise costs from offline datasets. This arrangement provides an effective approach for the widespread appli-cation of RL in high-risk scenarios where both cumulative and state-wise costs need to be considered simulta-neously. However, previously constrained offline RL algorithms are primarily designed to handle single-constraint problems related to cumulative cost, which faces challenges when addressing multi-constraint tasks that involve both cumulative and state-wise costs. In this work, we pro-pose a novel Primal policy Optimization with Conservative Estimation algorithm (POCE) to address the problem of multi-constraint offline RL. Concretely, we reframe the ob-jective of multi-constraint offline RL by introducing the con-cept of Maximum Markov Decision Processes (MMDP). Subsequently, we present a primal policy optimization al-gorithm to confront the multi-constraint problems, which improves the stability and convergence speed of model training. Furthermore, we propose a conditional Bell-man operator to estimate cumulative and state-wise Q-values, reducing the extrapolation error caused by out-of-distribution (OOD) actions. Finally, extensive experiments demonstrate that the POCE algorithm achieves competitive performance across multiple experimental tasks, particu-larly outperforming baseline algorithms in terms of safety. Our code is available at github. POCE.

While large language models (LLMs) excel in a simulated world of texts, they struggle to interact with the more realistic world without perceptions of other modalities such as visual or audio signals. Although vision-language models (VLMs) integrate LLM modules (1) aligned with static image features, and (2) may possess prior knowledge of world dynamics (as demonstrated in the text world), they have not been trained in an embodied visual world and thus cannot align with its dynamics. On the other hand, training an embodied agent in a noisy visual world without expert guidance is often chal-lenging and inefficient. In this paper, we train a VLM agent living in a visual world using an LLM agent excelling in a parallel text world. Specifically, we distill LLM's reflection outcomes (improved actions by analyzing mistakes) in a text world's tasks to finetune the VLM on the same tasks of the visual world, resulting in an Embodied Multi-Modal Agent (EMMA) quickly adapting to the visual world dy-namics. Such cross-modality imitation learning between the two parallel worlds is achieved by a novel DAgger-DPO algorithm, enabling EMMA to generalize to a broad scope of new tasks without any further guidance from the LLM expert. Extensive evaluations on the ALFWorld benchmark's diverse tasks highlight EMMA's superior performance to SOTA VLM-based agents, e.g., 20%-70% improvement in the success rate.

Feature invariance under different data transformations, i.e., transformation invariance, can be regarded as a type of self-supervision for representation learning. In this paper, we present PointClustering, a new unsupervised representation learning scheme that leverages transformation invariance for point cloud pre-training. PointClustering formulates the pretext task as deep clustering and employs transformation invariance as an inductive bias, following the philosophy that common point cloud transformation will not change the geometric properties and semantics. Technically, PointClustering iteratively optimizes the feature clusters and backbone, and delves into the transformation invariance as learning regularization from two perspectives: point level and instance level. Point-level invariance learning maintains local geometric properties through gathering point features of one instance across transformations, while instance-level invariance learning further measures clusters over the entire dataset to explore semantics of instances. Our PointClustering is architecture-agnostic and readily applicable to MLP-based, CNN-based and Transformer-based backbones. We empirically demonstrate that the models pre-learnt on the ScanNet dataset by PointClustering provide superior performances on six benchmark-s, across downstream tasks of classification and segmentation. More remarkably, PoinrClustering achieves an accuracy of 94.5% on ModelNet40 with Transformer backbone. Source code is available at https://github.com/FuchenUSTC/PointClustering.

The Internet has emerged as the most effective and a highly popular medium for advertising. Current contextual advertising platforms need publishers to manually change the original structure of their Web pages and predefine the position and style of embedded ads. Although publishers spend significant effort optimizing their Web page layout, a large number of Web pages contain noticeable blank regions. We present an innovative stylewise advertising platform for contextual advertising, called PageSense. The “style” of Web pages refers to the visual appearance of a Web page, such as color and layout. PageSense aims to associate style-consistent ads with Web pages. It provides two advertising options: 1) If publishers predefine ad positions within Web pages, PageSense will analyze the page style and select ads, which are consistent with the Web page layout, and 2) if publishers impose no constraints for ad placement, PageSense will automatically detect blank regions, select the most nonintrusive region for ad insertion, associate color-consistent ads with the Web pages, and deliver them to blank regions without breaking the original Web page style. Our experiments have verified the effectiveness of PageSense as a complement to existing contextual advertising.

Popularity prediction is a key problem in networks to analyze the information diffusion, especially in social media communities. Recently, there have been some custom-build prediction models in Digg and YouTube. However, these models are hardly transplant to an incomplete social network site (e.g., Flickr) by their unique parameters. In addition, because of the large scale of the network in Flickr, it is difficult to get all of the photos and the whole network. Thus, we are seeking for a method which can be used in such incomplete network. Inspired by a collaborative filtering method-Network-based Inference (NBI), we devise a weighted bipartite graph with undetected users and items to represent the resource allocation process in an incomplete network. Instead of image analysis, we propose a modified interdisciplinary models, called Incomplete Network-based Inference (INI). Using the data from 30 months in Flickr, we show the proposed INI is able to increase prediction accuracy by over 58.1%, compared with traditional NBI. We apply our proposed INI approach to personalized advertising application and show that it is more attractive than traditional Flickr advertising.

Considering the continuously increasing availability and accessibility of 3D media and the depth camera such as Kinect, we demonstrate an innovative 3D media system called 3DME. The objective of this demo is three-fold. First, the demo exhibits the creation of 3D images from RGB-D images. Second, 3DME allows a user to insert impressive effects to the produced 3D content. Last but not least, our demo is one of the first attempts towards advertising for 3D content which enables both the advertisers and content providers deliver more effective ads carried through 3D media.

The daunting volumes of community-contributed media contents on the Internet have become one of the primary sources for online advertising. However, conventional advertising treats image and video advertising as general text advertising by displaying relevant ads based on the contents of the Web page, without considering the inherent characteristics of visual contents. This article presents a contextual advertising system driven by images, which automatically associates relevant ads with an image rather than the entire text in a Web page and seamlessly inserts the ads in the nonintrusive areas within each individual image. The proposed system, called ImageSense , supports scalable advertising of, from root to node, Web sites, pages, and images. In ImageSense, the ads are selected based on not only textual relevance but also visual similarity, so that the ads yield contextual relevance to both the text in the Web page and the image content. The ad insertion positions are detected based on image salience, as well as face and text detection, to minimize intrusiveness to the user. We evaluate ImageSense on a large-scale real-world images and Web pages, and demonstrate the effectiveness of ImageSense for online image advertising.

PageRank has been broadly applied to get credible rank sequences of nodes in many networks such as the web, citation networks, or online social networks. However, in the real world, it is usually hard to ascertain a complete structure of a network, particularly a large-scale one. Some researchers have begun to explore how to get a relatively accurate rank more efficiently. They have proposed some local approximation methods, which are especially designed for quickly estimating the PageRank value of a new node, after it is just added to the network. Yet, these local approximation methods rely on the link server too much, and it is difficult to use them to estimate rank sequences of nodes in a group. So we propose a new method called DIGRank, which uses global Degree to facilitate Ranking in an Incomplete Graph and which takes into account the frequent need for applications to rank users in a community, retrieve pages in a particular area, or mine nodes in a fractional or limited network. Based on experiments in small-world and scale-free networks generated by models, the DIGRank method performs better than other local estimation methods on ranking nodes in a given subgraph. In the models, it tends to perform best in graphs that have low average shortest path length, high average degree, or weak community structure. Besides, compared with an local PageRank and an advanced local approximation method, it significantly reduces the computational cost and error rate.

Page Rank has been widely used in ranking retrieval results on the web, finding the top influential papers in citation networks or detecting valuable users in online social networks. However, in practice, it is usually hard to obtain a complete structure of any above networks to rank nodes. Thus, some researchers have begun to explore how to get estimated ranks efficiently without acquiring the whole network. They have proposed some approximating methods, however, it is difficult to determine which method is the best one or which is suitable to a certain application. In this case, we set experiments in small-world and scale-free generated networks to certify the feasibility and characteristics of four approximating methods. We also use eleven real networks to mention different optimal conditions for these methods. We find the DIG Rank method performs better than other local estimation methods in almost every given sub graph. Besides, Mean field approach method tends to perform well in networks that have low average shortest path length, small amount of nodes with the same low in degree, or weak community structure. Finally, we apply the most versatile method DIG Rank to Sina micro-blog website to precisely classify users in a group as elites, grassroots or mummy users.

This paper presents an innovative style-wise advertising platform for web page. Web page mainly refers to visual effects, such as color and layout. Unlike the most popular ad-network such as Google AdSense which needs publishers to change the original structure of their pages and define the position and style of the embedded ads manually, style-wise page advertising aims to automatically deliver style-consistent ads at proper positions within the web page, without breaking the layout of the original page. Our system is motivated from the fact that almost 90% web pages contain blank regions without any content. Given a web page with some blank regions, style-wise page advertising is able to detect the suitable blank areas for advertising, rank the ads according to the semantic relevance and web page style, and embed relevant ads into these nonintrusive areas. Style-wise page advertising represents one of the first attempts towards contextual advertising which enables the publishers to save effort when applying online advertising services.

This paper presents a novel game-like advertising system called GameSense, which is driven by the compelling contents of online images. Given a Web page which typically contains images, GameSense is able to select suitable images to create online in-image games for advertising. The contextually relevant ads (i.e., product logos) are embedded at appropriate positions within the online games. The ads are selected based on not only textual relevance but also visual content similarity. The game is able to provide viewers rich experience and thus promote the embedded ads to provide more effective advertising.

This demonstration presents an innovative contextual advertising platform for online image service, called ImageSense. Unlike most current ad-networks which treat image advertising as general text advertising by displaying relevant ads based on the contents of the Web page, ImageSense aims to embed more contextually relevant ads at less intrusive positions within each suitable image. Given a Web page containing images, ImageSense is able to decompose the page into a set of semantic blocks, select the suitable images from these blocks for advertising, rank the ads according to the relevance derived from surrounding text and visual similarity, and insert the relevant ads into the nonintrusive areas within the selected images. ImageSense represents one of the first attempts towards contextual image advertising which enables both the publishers and advertisers deliver more effective ads carried through image contents.