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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.06926">arXiv:2412.06926</a> <span> [<a href="https://arxiv.org/pdf/2412.06926">pdf</a>, <a href="https://arxiv.org/format/2412.06926">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> When Every Token Counts: Optimal Segmentation for Low-Resource Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=S%2C+B+R">Bharath Raj S</a>, <a href="/search/cs?searchtype=author&query=Suri%2C+G">Garvit Suri</a>, <a href="/search/cs?searchtype=author&query=Dewangan%2C+V">Vikrant Dewangan</a>, <a href="/search/cs?searchtype=author&query=Sonavane%2C+R">Raghav Sonavane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.06926v3-abstract-short" style="display: inline;"> Traditional greedy tokenization methods have been a critical step in Natural Language Processing (NLP), influencing how text is converted into tokens and directly impacting model performance. While subword tokenizers like Byte-Pair Encoding (BPE) are widely used, questions remain about their optimality across model scales and languages. In this work, we demonstrate through extensive experiments th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06926v3-abstract-full').style.display = 'inline'; document.getElementById('2412.06926v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.06926v3-abstract-full" style="display: none;"> Traditional greedy tokenization methods have been a critical step in Natural Language Processing (NLP), influencing how text is converted into tokens and directly impacting model performance. While subword tokenizers like Byte-Pair Encoding (BPE) are widely used, questions remain about their optimality across model scales and languages. In this work, we demonstrate through extensive experiments that an optimal BPE configuration significantly reduces token count compared to greedy segmentation, yielding improvements in token-saving percentages and performance benefits, particularly for smaller models. We evaluate tokenization performance across various intrinsic and extrinsic tasks, including generation and classification. Our findings suggest that compression-optimized tokenization strategies could provide substantial advantages for multilingual and low-resource language applications, highlighting a promising direction for further research and inclusive NLP. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.06926v3-abstract-full').style.display = 'none'; document.getElementById('2412.06926v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">LoResLM @ COLING 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.02251">arXiv:2310.02251</a> <span> [<a href="https://arxiv.org/pdf/2310.02251">pdf</a>, <a href="https://arxiv.org/format/2310.02251">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Choudhary%2C+T">Tushar Choudhary</a>, <a href="/search/cs?searchtype=author&query=Dewangan%2C+V">Vikrant Dewangan</a>, <a href="/search/cs?searchtype=author&query=Chandhok%2C+S">Shivam Chandhok</a>, <a href="/search/cs?searchtype=author&query=Priyadarshan%2C+S">Shubham Priyadarshan</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+A">Anushka Jain</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+A+K">Arun K. Singh</a>, <a href="/search/cs?searchtype=author&query=Srivastava%2C+S">Siddharth Srivastava</a>, <a href="/search/cs?searchtype=author&query=Jatavallabhula%2C+K+M">Krishna Murthy Jatavallabhula</a>, <a href="/search/cs?searchtype=author&query=Krishna%2C+K+M">K. Madhava Krishna</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.02251v2-abstract-short" style="display: inline;"> Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving scenarios have largely focused on a pre-defined (closed) set of object categories and driving scenarios, Talk2BEV blends recent advances in general-purpose language and vision models with BEV-structured map representation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.02251v2-abstract-full').style.display = 'inline'; document.getElementById('2310.02251v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.02251v2-abstract-full" style="display: none;"> Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving scenarios have largely focused on a pre-defined (closed) set of object categories and driving scenarios, Talk2BEV blends recent advances in general-purpose language and vision models with BEV-structured map representations, eliminating the need for task-specific models. This enables a single system to cater to a variety of autonomous driving tasks encompassing visual and spatial reasoning, predicting the intents of traffic actors, and decision-making based on visual cues. We extensively evaluate Talk2BEV on a large number of scene understanding tasks that rely on both the ability to interpret free-form natural language queries, and in grounding these queries to the visual context embedded into the language-enhanced BEV map. To enable further research in LVLMs for autonomous driving scenarios, we develop and release Talk2BEV-Bench, a benchmark encompassing 1000 human-annotated BEV scenarios, with more than 20,000 questions and ground-truth responses from the NuScenes dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.02251v2-abstract-full').style.display = 'none'; document.getElementById('2310.02251v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Project page at https://llmbev.github.io/talk2bev/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.04939">arXiv:2306.04939</a> <span> [<a href="https://arxiv.org/pdf/2306.04939">pdf</a>, <a href="https://arxiv.org/format/2306.04939">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> UAP-BEV: Uncertainty Aware Planning using Bird's Eye View generated from Surround Monocular Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dewangan%2C+V">Vikrant Dewangan</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+B">Basant Sharma</a>, <a href="/search/cs?searchtype=author&query=Choudhary%2C+T">Tushar Choudhary</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+S">Sarthak Sharma</a>, <a href="/search/cs?searchtype=author&query=Aanegola%2C+A">Aakash Aanegola</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+A+K">Arun K. Singh</a>, <a href="/search/cs?searchtype=author&query=Krishna%2C+K+M">K. Madhava Krishna</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.04939v1-abstract-short" style="display: inline;"> Autonomous driving requires accurate reasoning of the location of objects from raw sensor data. Recent end-to-end learning methods go from raw sensor data to a trajectory output via Bird's Eye View(BEV) segmentation as an interpretable intermediate representation. Motion planning over cost maps generated via Birds Eye View (BEV) segmentation has emerged as a prominent approach in autonomous drivin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04939v1-abstract-full').style.display = 'inline'; document.getElementById('2306.04939v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04939v1-abstract-full" style="display: none;"> Autonomous driving requires accurate reasoning of the location of objects from raw sensor data. Recent end-to-end learning methods go from raw sensor data to a trajectory output via Bird's Eye View(BEV) segmentation as an interpretable intermediate representation. Motion planning over cost maps generated via Birds Eye View (BEV) segmentation has emerged as a prominent approach in autonomous driving. However, the current approaches have two critical gaps. First, the optimization process is simplistic and involves just evaluating a fixed set of trajectories over the cost map. The trajectory samples are not adapted based on their associated cost values. Second, the existing cost maps do not account for the uncertainty in the cost maps that can arise due to noise in RGB images, and BEV annotations. As a result, these approaches can struggle in challenging scenarios where there is abrupt cut-in, stopping, overtaking, merging, etc from the neighboring vehicles. In this paper, we propose UAP-BEV: A novel approach that models the noise in Spatio-Temporal BEV predictions to create an uncertainty-aware occupancy grid map. Using queries of the distance to the closest occupied cell, we obtain a sample estimate of the collision probability of the ego-vehicle. Subsequently, our approach uses gradient-free sampling-based optimization to compute low-cost trajectories over the cost map. Importantly, the sampling distribution is adapted based on the optimal cost values of the sampled trajectories. By explicitly modeling probabilistic collision avoidance in the BEV space, our approach is able to outperform the cost-map-based baselines in collision avoidance, route completion, time to completion, and smoothness. To further validate our method, we also show results on the real-world dataset NuScenes, where we report improvements in collision avoidance and smoothness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04939v1-abstract-full').style.display = 'none'; document.getElementById('2306.04939v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to CASE 2023. Project video available at https://vikr-182.github.io/UAP-BEV</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1011.3482">arXiv:1011.3482</a> <span> [<a href="https://arxiv.org/pdf/1011.3482">pdf</a>, <a href="https://arxiv.org/ps/1011.3482">ps</a>, <a href="https://arxiv.org/format/1011.3482">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Distributed Construction of the Critical Geometric Graph in Dense Wireless Sensor Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Acharya%2C+S">Srivathsa Acharya</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+A">Anurag Kumar</a>, <a href="/search/cs?searchtype=author&query=Dewangan%2C+V">Vijay Dewangan</a>, <a href="/search/cs?searchtype=author&query=Sankara%2C+N">Navneet Sankara</a>, <a href="/search/cs?searchtype=author&query=Hegde%2C+M">Malati Hegde</a>, <a href="/search/cs?searchtype=author&query=Anand%2C+S+V+R">S. V. R. Anand</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1011.3482v1-abstract-short" style="display: inline;"> Wireless sensor networks are often modeled in terms of a dense deployment of smart sensor nodes in a two-dimensional region. Give a node deployment, the \emph{critical geometric graph (CGG)} over these locations (i.e., the connected \emph{geometric graph (GG)} with the smallest radius) is a useful structure since it provides the most accurate proportionality between hop-count and Euclidean distanc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1011.3482v1-abstract-full').style.display = 'inline'; document.getElementById('1011.3482v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1011.3482v1-abstract-full" style="display: none;"> Wireless sensor networks are often modeled in terms of a dense deployment of smart sensor nodes in a two-dimensional region. Give a node deployment, the \emph{critical geometric graph (CGG)} over these locations (i.e., the connected \emph{geometric graph (GG)} with the smallest radius) is a useful structure since it provides the most accurate proportionality between hop-count and Euclidean distance. Hence, it can be used for GPS-free node localisation as well as minimum distance packet forwarding. It is also known to be asymptotically optimal for network transport capacity and power efficiency. In this context, we propose DISCRIT, a distributed and asynchronous algorithm for obtaining an approximation of the CGG on the node locations. The algorithm does not require the knowledge of node locations or internode distances, nor does it require pair-wise RSSI (Received Signal Strength Indication) measurements to be made. Instead, the algorithm makes use of successful Hello receipt counts (obtained during a Hello-protocol-based neighbour discovery process) as edge weights, along with a simple distributed min-max computation algorithm. In this paper, we first provide the theory for justifying the use of the above edge weights. Then we provide extensive simulation results to demonstrate the efficacy of DISCRIT in obtaining an approximation of the CGG. Finally, we show how the CGG obtained from DISCRIT performs when used in certain network self-organisation algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1011.3482v1-abstract-full').style.display = 'none'; document.getElementById('1011.3482v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 November, 2010; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2010. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 11 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div 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