Jike Zhong
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View article: TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning
TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning Open
The frontier of visual reasoning is shifting toward models like OpenAI o3, which can intelligently create and operate tools to transform images for problem-solving, also known as thinking-\textit{with}-images in chain-of-thought. Yet exist…
View article: Context Matters: Learning Global Semantics via Object-Centric Representation
Context Matters: Learning Global Semantics via Object-Centric Representation Open
Recent advances in language modeling have witnessed the rise of highly desirable emergent capabilities, such as reasoning and in-context learning. However, vision models have yet to exhibit comparable progress in these areas. In this paper…
View article: Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models Open
Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored. Medical vision-language tasks demand precise understanding and clinically coherent an…
View article: EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark
EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark Open
Recent studies on large language models (LLMs) and large multimodal models (LMMs) have demonstrated promising skills in various domains including science and mathematics. However, their capability in more challenging and real-world related…
View article: Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data Open
We propose a learning problem involving adapting a pre-trained source model to the target domain for classifying all classes that appeared in the source data, using target data that covers only a partial label space. This problem is practi…
View article: Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification Open
Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions. Existing works offer personalization primarily to clients w…
View article: Making Batch Normalization Great in Federated Deep Learning
Making Batch Normalization Great in Federated Deep Learning Open
Batch Normalization (BN) is widely used in {centralized} deep learning to improve convergence and generalization. However, in {federated} learning (FL) with decentralized data, prior work has observed that training with BN could hinder per…