Point Cloud Geometry Scalable Coding with a Quality-Conditioned Latents Probability Estimator

The widespread usage of point clouds (PC) for immersive visual applications has resulted in the use of very heterogeneous receiving conditions and devices, notably in terms of network, hardware, and display capabilities. In this scenario, quality scalability, i.e., the ability to reconstruct a signal at different qualities by progressively decoding a single bitstream, is a major requirement that has yet to be conveniently addressed, notably in most learning-based PC coding solutions. This paper proposes a quality scalability scheme, named Scalable Quality Hyperprior (SQH), adaptable to learning-based static point cloud geometry codecs, which uses a Quality-conditioned Latents Probability Estimator (QuLPE) to decode a high-quality version of a PC learning-based representation, based on an available lower quality base layer. SQH is integrated in the future JPEG PC coding standard, allowing to create a layered bitstream that can be used to progressively decode the PC geometry with increasing quality and fidelity. Experimental results show that SQH offers the quality scalability feature with very limited or no compression performance penalty at all when compared with the corresponding non-scalable solution, thus preserving the significant compression gains over other state-of-the-art PC codecs.

Gemma: Open Models Based on Gemini Research and Technology

This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations.

Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a undamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data.

Generating Synthetic Data for Conversational Music Recommendation Using Random Walks and Language Models

Conversational recommendation systems (CRSs) enable users to use natural language feedback to control their recommendations, overcoming many of the challenges of traditional recommendation systems. However, the practical adoption of CRSs remains limited due to a lack of rich and diverse conversational training data that pairs user utterances with recommendations. To address this problem, we introduce a new method to generate synthetic training data by transforming curated item collections, such as playlists or movie watch lists, into item-seeking conversations. First, we use a biased random walk to generate a sequence of slates, or sets of item recommendations; then, we use a language model to generate corresponding user utterances. We demonstrate our approach by generating a conversational music recommendation dataset with over one million conversations, which were found to be consistent with relevant recommendations by a crowdsourced evaluation. Using the synthetic data to train a CRS, we significantly outperform standard retrieval baselines in offline and online evaluations.

IT/IST/IPLeiria Response to the Call for Proposals on JPEG Pleno Point Cloud Coding

This document describes a deep learning-based point cloud geometry codec and a deep learning-based point cloud joint geometry and colour codec, submitted to the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022. The proposed codecs are based on recent developments in deep learning-based PC geometry coding and offer some of the key functionalities targeted by the Call for Proposals. The proposed geometry codec offers a compression efficiency that outperforms the MPEG G-PCC standard and outperforms or is competitive with the V-PCC Intra standard for the JPEG Call for Proposals test set; however, the same does not happen for the joint geometry and colour codec due to a quality saturation effect that needs to be overcome.

Joint Geometry and Color Projection-based Point Cloud Quality Metric

Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images. The experimental results show that the proposed projection-based quality metric offers the best subjective-objective correlation performance in comparison with other metrics in the literature. The Pearson correlation gains regarding D1-PSNR and D2-PSNR metrics are 17% and 14.2 when data with all coding degradations is considered.

A Point-to-Distribution Joint Geometry and Color Metric for Point Cloud Quality Assessment

Point clouds (PCs) are a powerful 3D visual representation paradigm for many emerging application domains, especially virtual and augmented reality, and autonomous vehicles. However, the large amount of PC data required for highly immersive and realistic experiences requires the availability of efficient, lossy PC coding solutions are critical. Recently, two MPEG PC coding standards have been developed to address the relevant application requirements and further developments are expected in the future. In this context, the assessment of PC quality, notably for decoded PCs, is critical and asks for the design of efficient objective PC quality metrics. In this paper, a novel point-to-distribution metric is proposed for PC quality assessment considering both the geometry and texture. This new quality metric exploits the scale-invariance property of the Mahalanobis distance to assess first the geometry and color point-to-distribution distortions, which are after fused to obtain a joint geometry and color quality metric. The proposed quality metric significantly outperforms the best PC quality assessment metrics in the literature.

Multi-Perspective LSTM for Joint Visual Representation Learning

We present a novel LSTM cell architecture capable of learning both intra- and inter-perspective relationships available in visual sequences captured from multiple perspectives. Our architecture adopts a novel recurrent joint learning strategy that uses additional gates and memories at the cell level. We demonstrate that by using the proposed cell to create a network, more effective and richer visual representations are learned for recognition tasks. We validate the performance of our proposed architecture in the context of two multi-perspective visual recognition tasks namely lip reading and face recognition. Three relevant datasets are considered and the results are compared against fusion strategies, other existing multi-input LSTM architectures, and alternative recognition solutions. The experiments show the superior performance of our solution over the considered benchmarks, both in terms of recognition accuracy and complexity. We make our code publicly available at https://github.com/arsm/MPLSTM.

CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routing

Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometrics and affective computing. Despite the significant success of LF representation for constrained facial image analysis, this technology has never been used for face and expression recognition in the wild. In this context, this paper proposes a new deep face and expression recognition solution, called CapsField, based on a convolutional neural network and an additional capsule network that utilizes dynamic routing to learn hierarchical relations between capsules. CapsField extracts the spatial features from facial images and learns the angular part-whole relations for a selected set of 2D sub-aperture images rendered from each LF image. To analyze the performance of the proposed solution in the wild, the first in the wild LF face dataset, along with a new complementary constrained face dataset captured from the same subjects recorded earlier have been captured and are made available. A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests. An extensive performance assessment study using the new datasets has been conducted for the proposed and relevant prior solutions, showing that the CapsField proposed solution achieves superior performance for both face and expression recognition tasks when compared to the state-of-the-art.

Guessing What's Plausible But Remembering What's True: Accurate Neural Reasoning for Question-Answering

Neural approaches to natural language processing (NLP) often fail at the logical reasoning needed for deeper language understanding. In particular, neural approaches to reasoning that rely on embedded \emph{generalizations} of a knowledge base (KB) implicitly model which facts that are \emph{plausible}, but may not model which facts are \emph{true}, according to the KB. While generalizing the facts in a KB is useful for KB completion, the inability to distinguish between plausible inferences and logically entailed conclusions can be problematic in settings like as KB question answering (KBQA). We propose here a novel KB embedding scheme that supports generalization, but also allows accurate logical reasoning with a KB. Our approach introduces two new mechanisms for KB reasoning: neural retrieval over a set of embedded triples, and "memorization" of highly specific information with a compact sketch structure. Experimentally, this leads to substantial improvements over the state-of-the-art on two KBQA benchmarks.