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Eye tracking is becoming an increasingly important task domain in emerging computing platforms such as Augmented/Virtual Reality (AR/VR). Today's eye tracking system suffers from long end-to-end tracking latency and can easily eat up half of the power budget of a mobile VR device. Most existing optimization efforts exclusively focus on the computation pipeline by optimizing the algorithm and/or designing dedicated accelerators while largely ignoring the front-end of any eye tracking pipeline: the image sensor. This paper makes a case for co-designing the imaging system with the computing system. In particular, we propose the notion of "in-sensor sparse sampling", whereby the pixels are drastically downsampled (by 20x) within the sensor. Such in-sensor sampling enhances the overall tracking efficiency by significantly reducing 1) the power consumption of the sensor readout chain and sensor-host communication interfaces, two major power contributors, and 2) the work done on the host, which receives and operates on far fewer pixels. With careful reuse of existing pixel circuitry, our proposed BLISSCAM requires little hardware augmentation to support the in-sensor operations. Our synthesis results show up to 8.2x energy reduction and 1.4x latency reduction over existing eye tracking pipelines.

Recently, Segment Anything Model (SAM) shows exceptional performance in generating high-quality object masks and achieving zero-shot image segmentation. However, as a versatile vision model, SAM is primarily trained with large-scale natural light images. In underwater scenes, it exhibits substantial performance degradation due to the light scattering and absorption. Meanwhile, the simplicity of the SAM's decoder might lead to the loss of fine-grained object details. To address the above issues, we propose a novel feature learning framework named MAS-SAM for marine animal segmentation, which involves integrating effective adapters into the SAM's encoder and constructing a pyramidal decoder. More specifically, we first build a new SAM's encoder with effective adapters for underwater scenes. Then, we introduce a Hypermap Extraction Module (HEM) to generate multi-scale features for a comprehensive guidance. Finally, we propose a Progressive Prediction Decoder (PPD) to aggregate the multi-scale features and predict the final segmentation results. When grafting with the Fusion Attention Module (FAM), our method enables to extract richer marine information from global contextual cues to fine-grained local details. Extensive experiments on four public MAS datasets demonstrate that our MAS-SAM can obtain better results than other typical segmentation methods. The source code is available at //github.com/Drchip61/MAS-SAM.

This paper explores SynTOD, a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling complex tasks such as intent classification, slot filling, conversational question-answering, and retrieval-augmented response generation, without relying on crowdsourcing or real-world data. SynTOD utilizes a state transition graph to define the desired behavior of a TOD system and generates diverse, structured conversations through random walks and response simulation using large language models (LLMs). In our experiments, using graph-guided response simulations leads to significant improvements in intent classification, slot filling and response relevance compared to naive single-prompt simulated conversations. We also investigate the end-to-end TOD effectiveness of different base and instruction-tuned LLMs, with and without the constructed synthetic conversations. Finally, we explore how various LLMs can evaluate responses in a TOD system and how well they are correlated with human judgments. Our findings pave the path towards quick development and evaluation of domain-specific TOD systems. We release our datasets, models, and code for research purposes.

The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly restricting the usages of LVLMs. Most previous work detects and mitigates hallucination at the coarse-grained level or requires expensive annotation (e.g., labeling by proprietary models or human experts). To address these issues, we propose detecting and mitigating hallucinations in LVLMs via fine-grained AI feedback. The basic idea is that we generate a small-size sentence-level hallucination annotation dataset by proprietary models, whereby we train a hallucination detection model which can perform sentence-level hallucination detection, covering primary hallucination types (i.e., object, attribute, and relationship). Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model. Furthermore, we propose differentiating the severity of hallucinations, and introducing a Hallucination Severity-Aware Direct Preference Optimization (HSA-DPO) for mitigating hallucination in LVLMs by incorporating the severity of hallucinations into preference learning. Extensive experiments demonstrate the effectiveness of our method.

This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X). It highlights the latest developments in large-scale benchmarks that accelerate advancements in perception tasks for autonomous vehicles. The paper systematically analyzes a variety of datasets, comparing them based on aspects such as diversity, sensor setup, quality, public availability, and their applicability to downstream tasks. It also highlights the key challenges such as domain shift, sensor setup limitations, and gaps in dataset diversity and availability. The importance of addressing privacy and security concerns in the development of datasets is emphasized, regarding data sharing and dataset creation. The conclusion underscores the necessity for comprehensive, globally accessible datasets and collaborative efforts from both technological and research communities to overcome these challenges and fully harness the potential of autonomous driving.

We present a sublinear time algorithm for computing a near optimal low-rank approximation to any positive semidefinite (PSD) Toeplitz matrix $T\in \mathbb{R}^{d\times d}$, given noisy access to its entries. In particular, given entrywise query access to $T+E$ for an arbitrary noise matrix $E\in \mathbb{R}^{d\times d}$, integer rank $k\leq d$, and error parameter $\delta>0$, our algorithm runs in time $\text{poly}(k,\log(d/\delta))$ and outputs (in factored form) a Toeplitz matrix $\widetilde{T} \in \mathbb{R}^{d \times d}$ with rank $\text{poly}(k,\log(d/\delta))$ satisfying, for some fixed constant $C$, \begin{equation*} \|T-\widetilde{T}\|_F \leq C \cdot \max\{\|E\|_F,\|T-T_k\|_F\} + \delta \cdot \|T\|_F. \end{equation*} Here $\|\cdot \|_F$ is the Frobenius norm and $T_k$ is the best (not necessarily Toeplitz) rank-$k$ approximation to $T$ in the Frobenius norm, given by projecting $T$ onto its top $k$ eigenvectors. Our result has the following applications. When $E = 0$, we obtain the first sublinear time near-relative-error low-rank approximation algorithm for PSD Toeplitz matrices, resolving the main open problem of Kapralov et al. SODA `23, whose algorithm had sublinear query complexity but exponential runtime. Our algorithm can also be applied to approximate the unknown Toeplitz covariance matrix of a multivariate Gaussian distribution, given sample access to this distribution, resolving an open question of Eldar et al. SODA `20. Our algorithm applies sparse Fourier transform techniques to recover a low-rank Toeplitz matrix using its Fourier structure. Our key technical contribution is the first polynomial time algorithm for \emph{discrete time off-grid} sparse Fourier recovery, which may be of independent interest.

We introduce EC-SLAM, a real-time dense RGB-D simultaneous localization and mapping (SLAM) system utilizing Neural Radiance Fields (NeRF). Although recent NeRF-based SLAM systems have demonstrated encouraging outcomes, they have yet to completely leverage NeRF's capability to constrain pose optimization. By employing an effectively constrained global bundle adjustment (BA) strategy, our system makes use of NeRF's implicit loop closure correction capability. This improves the tracking accuracy by reinforcing the constraints on the keyframes that are most pertinent to the optimized current frame. In addition, by implementing a feature-based and uniform sampling strategy that minimizes the number of ineffective constraint points for pose optimization, we mitigate the effects of random sampling in NeRF. EC-SLAM utilizes sparse parametric encodings and the truncated signed distance field (TSDF) to represent the map in order to facilitate efficient fusion, resulting in reduced model parameters and accelerated convergence velocity. A comprehensive evaluation conducted on the Replica, ScanNet, and TUM datasets showcases cutting-edge performance, including enhanced reconstruction accuracy resulting from precise pose estimation, 21 Hz run time, and tracking precision improvements of up to 50\%. The source code is available at //github.com/Lightingooo/EC-SLAM.

Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67.2% on the MedQA dataset. However, this and other prior work suggested significant room for improvement, especially when models' answers were compared to clinicians' answers. Here we present Med-PaLM 2, which bridges these gaps by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach. Med-PaLM 2 scored up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19% and setting a new state-of-the-art. We also observed performance approaching or exceeding state-of-the-art across MedMCQA, PubMedQA, and MMLU clinical topics datasets. We performed detailed human evaluations on long-form questions along multiple axes relevant to clinical applications. In pairwise comparative ranking of 1066 consumer medical questions, physicians preferred Med-PaLM 2 answers to those produced by physicians on eight of nine axes pertaining to clinical utility (p < 0.001). We also observed significant improvements compared to Med-PaLM on every evaluation axis (p < 0.001) on newly introduced datasets of 240 long-form "adversarial" questions to probe LLM limitations. While further studies are necessary to validate the efficacy of these models in real-world settings, these results highlight rapid progress towards physician-level performance in medical question answering.

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We discuss various techniques in each of the categories and provide the relative strengths and weaknesses of the approaches. Our goal in this survey is to provide an easier yet better understanding of the techniques belonging to different categories in which research has been done on this topic. Finally, we highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.

Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence. Previous neural network based methods largely ignore the syntax structure in one sentence. In this paper, we propose a novel target-dependent graph attention network (TD-GAT) for aspect level sentiment classification, which explicitly utilizes the dependency relationship among words. Using the dependency graph, it propagates sentiment features directly from the syntactic context of an aspect target. In our experiments, we show our method outperforms multiple baselines with GloVe embeddings. We also demonstrate that using BERT representations further substantially boosts the performance.