Zero-shot Generative Linguistic Steganography

Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. We also design several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext. Our experimental results indicate that our method produces $1.926\times$ more innocent and intelligible stegotext than any other method.

Lost in Overlap: Exploring Watermark Collision in LLMs

The proliferation of large language models (LLMs) in generating content raises concerns about text copyright. Watermarking methods, particularly logit-based approaches, embed imperceptible identifiers into text to address these challenges. However, the widespread use of watermarking across diverse LLMs has led to an inevitable issue known as watermark collision during common tasks like question answering and paraphrasing. This study focuses on dual watermark collisions, where two watermarks are present simultaneously in the same text. The research demonstrates that watermark collision poses a threat to detection performance for detectors of both upstream and downstream watermark algorithms.

PISA: Point-cloud-based Instructed Scene Augmentation

Indoor scene augmentation has become an emerging topic in the field of computer vision with applications in augmented and virtual reality. However, existing scene augmentation methods mostly require a pre-built object database with a given position as the desired location. In this paper, we propose the first end-to-end multi-modal deep neural network that can generate point cloud objects consistent with their surroundings, conditioned on text instructions. Our model generates a seemly object in the appropriate position based on the inputs of a query and point clouds, thereby enabling the creation of new scenarios involving previously unseen layouts of objects. Database of pre-stored CAD models is no longer needed. We use Point-E as our generative model and introduce methods including quantified position prediction and Top-K estimation to mitigate the false negative problems caused by ambiguous language description. Moreover, we evaluate the ability of our model by demonstrating the diversity of generated objects, the effectiveness of instruction, and quantitative metric results, which collectively indicate that our model is capable of generating realistic in-door objects. For a more thorough evaluation, we also incorporate visual grounding as a metric to assess the quality of the scenes generated by our model.

WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor

Weight sharing based and predictor based methods are two major types of fast neural architecture search methods. In this paper, we propose to jointly use weight sharing and predictor in a unified framework. First, we construct a SuperNet in a weight-sharing way and probabilisticly sample architectures from the SuperNet. To increase the correctness of the evaluation of architectures, besides direct evaluation using the inherited weights, we further apply a few-shot predictor to assess the architecture on the other hand. The final evaluation of the architecture is the combination of direct evaluation, the prediction from the predictor and the cost of the architecture. We regard the evaluation as a reward and apply a self-critical policy gradient approach to update the architecture probabilities. To further reduce the side effects of weight sharing, we propose a weakly weight sharing method by introducing another HyperNet. We conduct experiments on datasets including CIFAR-10, CIFAR-100 and ImageNet under NATS-Bench, DARTS and MobileNet search space. The proposed WPNAS method achieves state-of-the-art performance on these datasets.

MMChat: Multi-Modal Chat Dataset on Social Media

Incorporating multi-modal contexts in conversation is an important step for developing more engaging dialogue systems. In this work, we explore this direction by introducing MMChat: a large scale multi-modal dialogue corpus (32.4M raw dialogues and 120.84K filtered dialogues). Unlike previous corpora that are crowd-sourced or collected from fictitious movies, MMChat contains image-grounded dialogues collected from real conversations on social media, in which the sparsity issue is observed. Specifically, image-initiated dialogues in common communications may deviate to some non-image-grounded topics as the conversation proceeds. We develop a benchmark model to address this issue in dialogue generation tasks by adapting the attention routing mechanism on image features. Experiments demonstrate the usefulness of incorporating image features and the effectiveness in handling the sparsity of image features.

Optimally Combining Classifiers for Semi-Supervised Learning

This paper considers semi-supervised learning for tabular data. It is widely known that Xgboost based on tree model works well on the heterogeneous features while transductive support vector machine can exploit the low density separation assumption. However, little work has been done to combine them together for the end-to-end semi-supervised learning. In this paper, we find these two methods have complementary properties and larger diversity, which motivates us to propose a new semi-supervised learning method that is able to adaptively combine the strengths of Xgboost and transductive support vector machine. Instead of the majority vote rule, an optimization problem in terms of ensemble weight is established, which helps to obtain more accurate pseudo labels for unlabeled data. The experimental results on the UCI data sets and real commercial data set demonstrate the superior classification performance of our method over the five state-of-the-art algorithms improving test accuracy by about $3\%-4\%$. The partial code can be found at https://github.com/hav-cam-mit/CTO.

Multi-modal Feature Fusion with Feature Attention for VATEX Captioning Challenge 2020

This report describes our model for VATEX Captioning Challenge 2020. First, to gather information from multiple domains, we extract motion, appearance, semantic and audio features. Then we design a feature attention module to attend on different feature when decoding. We apply two types of decoders, top-down and X-LAN and ensemble these models to get the final result. The proposed method outperforms official baseline with a significant gap. We achieve 76.0 CIDEr and 50.0 CIDEr on English and Chinese private test set. We rank 2nd on both English and Chinese private test leaderboard.

Exploration-efficient Deep Reinforcement Learning with Demonstration Guidance for Robot Control

Although deep reinforcement learning (DRL) algorithms have made important achievements in many control tasks, they still suffer from the problems of sample inefficiency and unstable training process, which are usually caused by sparse rewards. Recently, some reinforcement learning from demonstration (RLfD) methods have shown to be promising in overcoming these problems. However, they usually require considerable demonstrations. In order to tackle these challenges, on the basis of the SAC algorithm we propose a sample efficient DRL-EG (DRL with efficient guidance) algorithm, in which a discriminator D(s) and a guider G(s) are modeled by a small number of expert demonstrations. The discriminator will determine the appropriate guidance states and the guider will guide agents to better exploration in the training phase. Empirical evaluation results from several continuous control tasks verify the effectiveness and performance improvements of our method over other RL and RLfD counterparts. Experiments results also show that DRL-EG can help the agent to escape from a local optimum.

A Semantics-Assisted Video Captioning Model Trained with Scheduled Sampling

Given the features of a video, recurrent neural network can be used to automatically generate a caption for the video. Existing methods for video captioning have at least three limitations. First, semantic information has been widely applied to boost the performance of video captioning models, but existing networks often fail to provide meaningful semantic features. Second, Teacher Forcing algorithm is often utilized to optimize video captioning models, but during training and inference, different strategies are applied to guide word generation, which lead to poor performance. Third, current video captioning models are prone to generate relatively short captions, which express video contents inappropriately. Towards resolving these three problems, we make three improvements correspondingly. First of all, we utilize both static spatial features and dynamic spatio-temporal features as input for semantic detection network (SDN) in order to generate meaningful semantic features for videos. Then, we propose a scheduled sampling strategy which gradually transfers the training phase from a teacher guiding manner towards a more self teaching manner. At last, the ordinary logarithm probability loss function is leveraged by sentence length so that short sentence inclination is alleviated. Our model achieves state-of-the-art results on the Youtube2Text dataset and is competitive with the state-of-the-art models on the MSR-VTT dataset.