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Found 12 papers, 11 papers with code
Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?
Theory of Mind (ToM), the ability to attribute mental states to others, is fundamental for human social intelligence and a critical capability for advanced Artificial Intelligence.
OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest recently.
Proposing and solving olympiad geometry with guided tree search
Mathematics olympiads are prestigious competitions, with problem proposing and solving highly honored.
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
While the progress in unified models offers new solutions, existing benchmarks are insufficient for evaluating these methods due to limitations in data size and diversity.
Out-of-distribution generalization via composition: a lens through induction heads in Transformers
We empirically examined the training dynamics of Transformers on a synthetic example and conducted extensive experiments on a variety of pretrained LLMs, focusing on a type of components known as induction heads.
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
Asynchronous Federated Learning (AFL) confronts inherent challenges arising from the heterogeneity of devices (e. g., their computation capacities) and low-bandwidth environments, both potentially causing stale model updates (e. g., local gradients) for global aggregation.
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering
The development of Urdu scene text detection, recognition, and Visual Question Answering (VQA) technologies is crucial for advancing accessibility, information retrieval, and linguistic diversity in digital content, facilitating better understanding and interaction with Urdu-language visual data.
Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories.
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD).
Ranked #1 on
Generalized Zero-Shot Object Detection
on MS-COCO
Uncovering hidden geometry in Transformers via disentangling position and context
Given embedding vector $\boldsymbol{h}_{c, t} \in \mathbb{R}^d$ at sequence position $t \le T$ in a sequence (or context) $c \le C$, extracting the mean effects yields the decomposition \[ \boldsymbol{h}_{c, t} = \boldsymbol{\mu} + \mathbf{pos}_t + \mathbf{ctx}_c + \mathbf{resid}_{c, t} \] where $\boldsymbol{\mu}$ is the global mean vector, $\mathbf{pos}_t$ and $\mathbf{ctx}_c$ are the mean vectors across contexts and across positions respectively, and $\mathbf{resid}_{c, t}$ is the residual vector.
Towards Food Image Retrieval via Generalization-oriented Sampling and Loss Function Design
Therefore, it is challenging to study the generalization of the model in food image retrieval.
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Large
